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THE 10 BEST Shenzhen Tours & Excursions

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• The ranking of tours, activities, and experiences available on Tripadvisor is determined by several factors including the revenue generated by Tripadvisor from these bookings, the frequency of user clicks, and the volume and quality of customer reviews. Occasionally, newly listed offerings may be prioritized and appear higher in the list. The specific placement of these new listings may vary.

1. Private Shenzhen Day Tour from Hong Kong

2. The "Shenzhen"er" The Greatest, The Fastest and The Tallest Day Private Tour

3. Shenzhen Private Flexible Day Tour with Guide and Driver Service

4. Shenzhen tour guide with car service

5. Private Shenzhen Day Trip Tour from Guangzhou by Bullet Train

6. 2.5-Hour Shenzhen City-Village Walking Tour: Back In The 90's & TikTok Influence

7. Shenzhen Self-Guided Tour with Private Car and Driver Service

8. Private Shenzhen Evening Tour With Shenzhen Top Scenic Spots

9. Shenzhen Self-Guided Flexible Tour with Private Car and Driver

10. Shenzhen tour with Dim Sum lunch(Chinese Food )

11. Shenzhen Private Shopping Tour with Local Shopping Guru

12. 7 Days Hong Kong, Macau, Guangdong, and China Greater Bay Tour

13. Half Day Shenzhen Splendid China Folk Village Guided Tour

14. Full Day Private Shore Tour in Guangzhou from Shekou Cruise Port

15. Shenzhen Full-Day Guided Tour: Splendid China and Folk Culture Villages

16. Private Tour: Window of the World Theme Park in Shenzhen

17. Hong Kong including Shenzhen and Macau ( 5 Days - 4 Nights )

18. 2-Day Private Yangshuo Trip By Round-way Bullet Train From Shenzhen

19. Private Transfer from Shenzhen Baoan Airport to Shenzhen Hotel

20. Private Day Tour to Kaiping Diaolou World Heritage from Shenzhen

21. Private Shenzhen Tour: Splendid China Folk Village Cultural Center Tour from Guangzhou

22. Private Full-Day Shenzhen Highlights City Tour

23. Cruise Excursion Day Trip in Shenzhen: Dongmen Old Street, Dapeng Old City etc.

24. The Best of Shenzhen Walking Tour

25. Shenzhen Private Customized Sightseeing and Shopping Combo Tour

26. All Inclusive Tour: The Great Wall at Badaling with Hutong Rickshaw

27. Private Day Tour from Guangzhou to Shenzhen

28. Shenzhen Private Day Tour to Dafen Village, Shenzhen Museum and more

29. Romantic Tour in Shenzhen

30. Private Chengdu Panda Base Day Tour by Round-way Flight from Shenzhen

What travelers are saying.

• Shenzhen Private Flexible Day Tour with Guide and Driver Service
• Private Shenzhen Day Tour from Hong Kong
• 2.5-Hour Shenzhen City-Village Walking Tour: Back In The 90's & TikTok Influence
• Shenzhen tour guide with car
• Shenzhen Self-Guided Tour with Private Car and Driver Service
• Car Service & Tour Guide
• Shenzhen Overseas Chinese Town
• Mt. Yuanshan Scenic Resort
• Indonesian2china-Shenzhen One-day Tour
• Andrey Milov

Shenzhen Tours - 10 Best Shenzhen Tour Packages 2024

Experience Shenzhen Like Never Before with Our Top Shenzhen Tours 2024!

Nestled in the heart of the Greater Bay Area of China , Shenzhen is synonymous with innovative cityscapes and is renowned as China's Silicon Valley . Our Shenzhen Tours in 2024 invite you to experience the vibrant pulse of this dynamic city. We offer 10 specially-curated tour packages, unveiling Shenzhen's unique blend of modernity, culture, and natural beauty.  

1 or 2 Days in Shenzhen: Skyline & Development, Infusion & Openness

Venture into Shenzhen's core, a city that has transformed from a modest fishing village into a sprawling metropolis within a few decades. Marvel at the city's futuristic skyline, punctuated by architectural marvels like the Ping’an Financial Center and the Kingkey 100 skyscraper.

But Shenzhen isn't just about skyscrapers. Our tours guide you to the scenic Shenzhen Bay Park for a leisurely stroll along the coastline. You'll also explore the Splendid China Folk Village, offering an overview of China's attractions and a fun interaction with various ethnic groups. Our 2024 Shenzhen tour packages promise an unforgettable journey into the captivating world of Shenzhen, catering to both business travelers and city explorers .  

Extend Your China Tours from Shenzhen

Maximize your China trip by commencing your tour from Shenzhen, seamlessly connecting to other stunning destinations like Hong Kong , Macau , Guangzhou , Guilin /Yangshuo, Chengdu , Xian , Shanghai , Beijing , the Yangtze River, or even Tibet . Let us customize your trips and help you create unforgettable memories of this dynamic city and beyond. Explore with Asia Odyssey Travel now!

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3 Best Shenzhen Tours

Guangdong tours & greater bay area tours.

• Articles & Planning

Discover the vibrant city of Shenzhen with our curated selection of the 3 best Shenzhen private tours. Immerse yourself in its bustling streets, futuristic architecture, and theme parks. Whether it's a Shenzhen day trip from Hong Kong or Guangzhou , embark on an unforgettable journey and uncover the true essence of Shenzhen to witness how magical the city is. Book your trip now!

Enjoy a journey to unveil the marvels of Shenzhen, a captivating city nestled in Guangdong province , and an absolute gem within the Greater Bay Area . Expand your horizons by extending your adventure to embrace the myriad of experiences awaiting in Guangdong province and the Greater Bay Area, where you'll encounter the enchantment of Hong Kong, the allure of Macau , the vibrant spirit of Guangzhou, and the cultural tapestry of Foshan . Select your trips now!

China Tours from Shenzhen/Guangzhou

Guangzhou and Shenzhen serve as the southern gateways to China, offering ideal starting points for your China tours. Utilize convenient high speed trains or flights to seamlessly travel from Guangzhou or Shenzhen to captivating destinations like Guilin , Yangshuo, and Longji Rice Terraces . For those seeking even more exploration, consider extending your journey to enchanting places such as Zhangjiajie , Huangshan , and Guizhou . Unlock a world of discovery from these vibrant cities and embark on an unforgettable exploration of China's diverse landscapes .

Shenzhen Tours Planning & Useful Articles

When planning your China Shenzhen tour , it's essential to gather all the necessary information to make your trip a success. From navigating the city's transportation system to discovering the best attractions and hidden gems, proper planning is key. Our collection of Shenzhen useful articles provides valuable insights and tips to help you make the most of your Shenzhen trip. Whether you're a first-time visitor or bushiness travelers, our articles offer guidance and inspiration to ensure a memorable and fulfilling experience in Shenzhen.

Shenzhen Tours FAQs - Plan Your Shenzhen Tours Worry-Free

Shenzhen, located in southern China and the Pearl Delta River, experiences a subtropical monsoon climate that results in hot, rainy summers and mild, relatively dry winters. The ideal time to visit is during the fall and early winter months, from October to December , when temperatures range from 15°C to 28°C (59°F to 82°F). The weather during this period is pleasant, with less rainfall and clear skies, perfect for sightseeing.

There are several options to travel from Hong Kong to Shenzhen:

MTR: The most convenient and fastest way is to take the Mass Transit Railway (MTR). The East Rail Line directly connects Hong Kong to Shenzhen at the Lo Wu or Lok Ma Chau stations. The journey takes around 40-50 minutes.

Ferries: Ferries run from Hong Kong International Airport and Hong Kong Island to Shenzhen's Shekou Ferry Terminal. The trip takes around 30-60 minutes depending on the route.

Bus: Cross-border buses run from various points in Hong Kong to Shenzhen, including the airport. The journey can take around 1-2 hours depending on traffic.

Taxi: Cross-border taxis are available but are the most expensive option.

Learn more details about How to Travel from Hong Kong to Shenzhen >>

Futian CBD: The central business district is home to many of Shenzhen's iconic skyscrapers and shopping malls.

Window of the World: This theme park features miniaturized reproductions of famous landmarks from around the globe.

Splendid China Folk Village: A theme park showcasing miniatures of China's most famous landmarks and a variety of live performances reflecting China's diverse ethnic cultures.

Shenzhen Museum: Offers an insight into Shenzhen's history and development.

Ping An Finance Centre: One of the tallest buildings in the world, with an observation deck offering panoramic city views.

Dafen Oil Painting Village: A suburb renowned for producing high-quality oil painting reproductions.

Huaqiangbei Electronics Market: Known as the world's greatest electronics market, it's a paradise for tech enthusiasts.

Learn more about Top 15 Things to Do in Shenzhen >>

1-2 day tour usually suffices to cover the major attractions in Shenzhen. Day one can be spent exploring the city's modern architecture and shopping areas. On the second day, you can visit the theme parks and cultural attractions. If you have more time, consider day trips to nearby areas or explore the city's vibrant food scene and nightlife.

Learn more about Shenzhen Day Trip: Discover New Shenzhen through History, Skyscrapers & Folk Culture >>

Most foreign visitors require a visa to enter China . However, Shenzhen has a special 5-Day Visa on Arrival policy for tourists from certain countries who plan to stay only in the Shenzhen area. This visa is obtained at the port of entry. Always check the latest visa regulations before planning your trip.

Shenzhen's food scene is a blend of Cantonese cuisine and flavors from other parts of China. Must-try dishes include Dim Sum, a variety of bite-sized portions served in small steamer baskets; Shajing oysters, a local delicacy often grilled with garlic; and Hakka cuisine, characterized by its fragrant and savory flavors. Seafood is abundant in Shenzhen, and exotic fruits like durian, lychee, and mangosteen are also worth trying.

Shenzhen offers a wide range of accommodations to suit every budget. Luxury hotels such as the Shenzhen Marriott Hotel Nanshan and The Ritz-Carlton offer high-end rooms with excellent service. Mid-range options provide comfort at a more affordable price, and budget hostels like the Shenzhen Loft Youth Hostel are great for backpackers. Many international chains are present, and local options often provide an authentic experience. Always consider the location when booking, staying near a metro line will make your travel around the city easier.

Latest Shenzhen Tours Reviews from Our Customers

Glyfada, Greece

Destination(s): Beijing, Shanghai, Guangzhou/Shenzhen

Date of Experience: Jul 19, 2023

Tour Customized by: Nicole

You May be Interested in This Tour: Customized Tour

Shutterstock / HelloRF Zcool

The gleaming manifestation of China's economic miracle, Shēnzhèn (深圳) has risen from the marshy Pearl River Delta into one of the world's most mega megacities in less time than it took London's St Paul's Cathedral to be built. Millions of migrants have been drawn to its golden gates from the Chinese countryside since the 1980s; now, Shēnzhèn attracts high-flying tech graduates and global corporations.

Attractions

Must-see attractions.

By far the best place for strolling or simply hanging out in the city is the breezy OCT-LOFT complex, a warren of repurposed communist-era factories criss…

Dafen Oil Painting Village

This folksy urban village of narrow lanes and alleys is a pleasure to visit in itself, but what makes Dafen simply unmissable is the hundreds of art…

Lianhua Shan Park

It's an easy half-hour amble up to the top of this tropical hill in the heart of Futian District for Shenzhen's best skyline photographs. Appropriately,…

Sunrise Art Center

Built from the vestiges of a century-old Hakka home, this idyllic courtyard gallery was created by Chen Qiuzhi, a contemporary ink artist from Anhui…

Design Society

China's first dedicated museum of design, this architectural landmark opened with media fanfare in 2017 on the Shekou shore with its famous founding…

Window of the World

It's 'Around the World in 80 Minutes' (OK, more like half a day) at this endearingly kitsch theme park set in well-tended gardens. From the Houses of…

Dapeng Fortress

This Ming dynasty walled town was erected 600 years ago to shore up the coastline against marauding Japanese pirates, and later became embroiled in the…

Museum of Contemporary Art & Planning Exhibition

One of those thrillingly space-age, 'only in China' architectural projects, this gargantuan exhibition space designed by Coop Himmelb(l)au anchors…

Latest stories from Shenzhen

Feb 23, 2018 • 4 min read

Just three decades ago, Shenzhen was a drowsy fishing village on the fringe of Hong Kong, across the water in mainland China. Today, it’s one of the…

Jan 23, 2017 • 5 min read

Sep 25, 2015 • 5 min read

Apr 20, 2015 • 4 min read

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Sleepless Shenzhen pulses with theme parks and big business, boasts pretty beaches on Dapeng Bay and oodles of folk museums. Check it out with help from a local guide from tourHQ.

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Shenzhen Tour Guides

Experienced Tour Guide who helps make your trip more fun. Hello and welcome to Shenzhen! I'm an ...

Hope you are doing well! I  am a Guangzhou native, I am an enthusiastic, professional and ...

Jasmine Chan

Hello. I am Jasmine, living in Guangzhou for about 17 years. I travel a lot nationwide, and ...

My name is Emma. I am an friendly, active and easy-going woman from Guangzhou, China.Guangzhou ...

Hi! this is Jenny Lee, an official licensed English tour guide as well as ALL-IN-ONEbusiness tour ...

Shenzhen Driver & interpreter Rock

 My name is rock   I have many years of experience providing professional ...

Hi,I am Vivi , i am a travelling lover. I have tour guide licence. I believe traveling is the best ...

Hi, this is Mr. Kenny Qin in Guangzhou, China. I have been working as a private tour guide, ...

I have been a tour guide for more than 16 years, and good at Chinese history, culture, Silk Road, ...

As the founder of Shanghai Pathways, I have always been fascinated by my hometown of Shanghai. ...

Hello Travellers.Welcome to China ！I'm Alice,a young, professional,energetic and easy-going, and ...

Michael Wang

Welcome to ChinaI am open to everything and can help you to have a happy holiday in Guangzhou, ...

my main service :*1) Airport pick-up & drop-off .*2)Car service with driver on actual charges ...

I take my work seriously, hope to exceed my customer's expectaions. I care about the details, ...

Alan Shadow

Hey! I am college teahcer here in Shenzhen, China. I teach English and Intercultureal ...

part of the job is itinerary planning for my US colleaguesduring business trip in China.I am very ...

This booming manufacturing powerhouse just to the north of Hong Kong was once just a sleepy village on the South China Sea. Today, it shimmers with the glitter of modernist high-rises, ticks over to the mechanised hum of metro trains and beats with all the endless energy you’d expect of one of the nation’s richest and fastest-growing megalopolises. For travellers, this bustling town offers up some of China’s most famous theme parks—ranging from the Vegas-esque Window of The World, complete with its Eiffel Tower and Giza Pyramid effigies, and its to the seemingly endless Happy Valley. These are matched by the city’s cultural offering, which throws up Splendid China and the Folk Culture village and the prestigious collections of the Shenzhen Museum. Then there are the parks, famed throughout China and sprawled out over Lianhua Mountain and the flower gardens of Futian District. Naturally there’s also a hugely eclectic range of activities to pursue elsewhere in the city, and visitors eager to make the most of Hong Kong’s sister city should ask their Shenzen tourist guide about breaking out to the beautiful beaches of the Dapeng Bay, the sprawling expat friendly shopping complexes of Luohu, or the unique settings at Dafen Oil Painting Village, where hundreds of artists are commissioned to churn out works ranging from originals to mostly replicas of the great masters.

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Shenzhen Private Tours & Local Tour Guides

Discover things to do and plan a trip to shenzhen, china with a local guide.

View all 12 reviews

Popular Shenzhen Tour Guides

Hi, everyone. I am Heidi, 2nd generation cantonese. My parents bring me to Canton. Then I live in Shenzhen for studying, working and creating my own family here.I love this magic seashore city ,which always bring people passions, hopes and endless opportunities. About 40 years ago Shenzhen is only a small fish village and after years developing it becomes a modern and international city where attracts people from different regions and countries to visit and develop. I have a background in English, had been worked in a Hongkong-invested Company and always travelled a lot like Australia, Tailand, Hongkong. Being fluent in English. I would like to share my knowledge and insights with you during our tour not only regarding the local history, Human culture,dining Culture, but also economics status, industrial zone, etc. I aim to make your experience not only informative but also engaging and enjoyable. Feel free to ask any questions along the way, and I'll do my best to provide answers and insights. Your curiosity is highly encouraged! Thank you for choosing me as your guide. I am looking forward to hearing from you.                                                                                                                                  

Hi,I am Vivi , i am a travelling lover. I believe traveling is the best way to explore and see the world.I have tour guide licence. travelling is my interest and passion. i hope i am not only a guide here , i also a reliable friend here for you, we can travel as friends,sharing different interests/ experience/ cultures and learn each other. I work as a Business assistant/sourcing assistant/ Tour guide based in Shenzhen. Let me briefly introduce myself.I majored in International economics and trade. After graduation, I had been working for several years as an international sales manager in the electronic manufacturer. After that, i have been working as a Business Assistant/Sourcing Assistant/Tour Guide now. I participated in Canton Fair& Hongkong Fairs & other fairs for many times. I also accompany customer to visit factories for the business meeting and factory audit. I have the full knowledge and practice for the international trading and production manufacturing process. I can help to find good suppliers and make your business trip successful here. Furthermore, i am a travelling lover. I have been to USA, Australia,Italy, Germany, Iran, Dubai, Singapore, Turkey, Thailand, Malaysia , Korea and Sri Lanka,Vietnam and so on. I have the tour guide licence. .China is a country with 5000 years of history, a vast territory and multitude of nationalities. China don't only have the fast developing modern cities,but also have a resourceful natural landscape, diverse and delicious food, i will not only take you to the must-visit tourist attractions, but also to the only-local-knows places. The services i am offering are as below: -Sourcing Assistant/Agent - Business interpreter - Supplier and factory visit/Audit - Order follow-up - Shipment arrangement - Tour guide service - Car /Hotel booking Looking forwards to meeting you here!                                                                                                                                  

Popular shenzhen private tours.

Shenzhen One Day Tour with locals' favorite spots

Shenzhen is a beautiful seashore city with flashy modernity and best urban greening in China. No matter you are here for leisure visits or for business opportunities. It is worth for you to enjoy a fully customized itinerary with local expert guide that meet your interests. This tour includes the must see sightseeing spots, like Lianhuashan park, KK mall and Shenzhen bay park. You will of course to taste the local Canton cuisine. Let's go to explore together!

Shenzhen 1 Day Private City Tour-Market, Views and Cruise

On this tour, you are going to visit three sightseeing spots: huaqiang north electronics market, ping an finance center tower (free sky) and sea world. you will have a tour at the large shopping destination for wholesale electronics and components. then go to the new landmark of shenzhen, pingan finance center tower. after that, you will go to the western-style entertainment center-sea world..

Shenzhen 1 day trip customize to your request and interest

I can customize the trip according to your request and interest. it can be a business trip to visit your suppliers/factories here in Shenzhen or the cities nearby. or a sightseeing trip here to visit the tourist attractions, or a trip to mix of business and sightseeing. Just Let me know your request and purpose of the trip, i will help you plan the itinerary.

Shenzhen 1 day trip

On this tour, we will go to gankeng kejia town see the old/traditional side of china, then go to the new landmark of shenzhen :pingan finance center tower, to have the best bird's eye view of the city. at last , we will visit the large shopping destination for wholesale electronics and components., new shenzhen private tours.

Shenzhen tour guide with car services

Hello, i'm heidi and i bring extensive experiences as a skilled driver and knowledgeable tour guide. i provide not just transportation but a complete travel experience, ensuring my passengers enjoy every moment of their journey. quite familiar with shenzhen, foshan and guangzhou areas. with years of driving experiences i have served many customers from other regions & countries of the world.i look forward to the opportunity to bring my skills and passion for travel to enhance your journey, day trips from shenzhen.

Shenzhen Tour Reviews

“ Can't recommend Vivi enough! ”

We requested Vivi to take us on a shopping trip to the wholesale fabric markets in Dongmen, China. She had never done a tour there but she researched where to go and which were the best malls for the fabrics we needed. She was indispensable for dealing with the shop holders and instrumental in getting them to take us seriously. We had a full on day and managed to get all the fabrics we needed. Vivi was patient and cheerful and so nice all day. she stayed with us the whole time until we were back at the entrance to the train station at Luohu. I highly recommend Vivi if you need a guide for Shenzen or that area. * * * * * Judy & Halle from Australia

Shenzhen day trip

“ Helpful Professional Friendly ”

It was nice to have a local that could speak to what life there has been like in Shenzhen.

“ The Best Guide Ever Heidi L ”

I’ll start with the good - punctual, courteous, professional, customer focused, extremely knowledgeable, multilingual. Now the bad - absolutely nothing. My time with Heidi L was absolutely fabulous. She is the consummate professional and as mentioned extremely knowledgeable. I was shown a wonderful day seeing Shenzhen and can highly recommend Heidi L. She is focused on ensuring that each of her guests are treated to a Shenzhen experience that will be memorable. I was shown the real China experience that I could not have envisaged or have been able to do without the BEST GUIDE EVER - HEIDI L. Thank you so very very much. Ross M

Dear Mr.Ross, Thank you very much for the comments. You are absolutely a gentle and kind person, being very nice and polite. I hope you will enjoy the rest holidays in Shenzhen. Cheers. B.rgds/Heidi

Shenzhen Private Tour Guide

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The Top 20 Things to Do in Shenzhen

Shenzhen is an active and innovative city. When talking about Shenzhen, people always mention its fast-growing and advanced economy but many may not know that Shenzhen is also a treasure box for travelers.

From skyscrapers to ancient relics, from luxury shopping malls to artistic urban villages, and from lively theme parks to solemn churches, you will never get bored in Shenzhen.

Here, we have listed 20 places you should visit in Shenzhen, including tall buildings, places to go shopping and enjoy the local cuisine, characteristic urban villages, and more.

See 3 Amazing Skyscrapers!

Shenzhen grew from a fishing village to an international metropolis in 40 years. Today, the city has about 30% of the skyscrapers in China. Ascend one and enjoy the great view!

1. Scale Shenzhen's Tallest Building: Ping An International Finance Centre (平安金融中心)

• Address: junction of Yitian Road and Fuhua Road, Futian District
• Metro: Take Line 1 or 3 to Shopping Park Station and leave via Exit A.

With a height of 592.5 meters (1,944 feet), the Ping An IFC is the tallest tower in Shenzhen and the fourth tallest building in the world. The building features offices, a hotel, and a luxury shopping mall.

The observatory deck named Free Sky is located on the 116th floor, and you can admire a bird's-eye view of Shenzhen and Hong Kong's skyline from it.

2. Go Sightseeing and Shopping at The KK100

• Address: 5016 Shennan East Road, Luohu District
• Metro: Take Line 1 or 2 to Grand Theater Station.

The KK100 is the second tallest skyscraper in Shenzhen with a height of 441.8 meters (1,449 feet). The whole building is designed in a beautiful streamlined shape, which symbolizes a fountain, representing the development and prosperity of Shenzhen.

The KK100 might be the most luxurious skyscraper in Shenzhen as it accommodates top-class offices, an international level shopping center called the KK Mall, and one of the world's highest 5-star hotels, The St. Regis Shenzhen.

3. Admire the Thinnest Building: Shun Hing Square (信兴广场)

• Address: 5002 Shennan East Road, Luohu District

When Shun Hing Square was built in 1996, it was the tallest building in Shenzhen, China, and even the whole of Asia. It also holds another record of being the thinnest skyscraper because the ratio of its width to its height is 1:9.

The top floor of Shun Hing Square is an observation deck and there are exhibits on Shenzhen's development history.

Seek Out 4 Spots for Shopping / Gourmet Food

Shenzhen offers great shopping opportunities. The city has excellent shopping choices, such as electrical appliances, digital products, fashionable clothing, jewelry, and seafood. You can also try the local cuisine!

4. Find Electronic Products in Huaqiangbei (华强北)

• Location: Futian District
• Metro: Take Line 2 or 7 to Huaqiangbei (华强北) Station.

Huaqiangbei (also called Huaqiang North) is the largest electronics market in China. It is a good place to buy electronic appliances, telecommunication products, and computer accessories.

The market also sells electronic parts. You can find everything you need to create a smartphone. If you are interested in electronics, don't miss the chance to walk around Huaqiangbei.

5. Observe Antiques in Shenzhen Antique City (深圳古玩城)

• Address: 168 Xinxiu Road, Luohu District
• Metro: Take Line 2 to Xinxiu (新秀) Station.

If you like old artifacts, Shenzhen Antique City is a good place to stroll around. On the open-air stalls, there are piles of teapots, tableware, beads, and objects that look as though they were made hundreds of years ago.

Although you are not advised to buy any expensive items in the market because there are a lot of fakes, it is still interesting to just walk around and see the buyers using their lenses to check the goods.

6. Look for Affordable Goods in East Gate Old Street (东门老街)

• Location: Luohu District
• Metro: Take Line 1 or 3 to Laojie (老街) Station.

East Gate (or Dongmen) Old Street is a popular pedestrian street for local people and tourists. There are over 20 shopping malls as well as countless stores and restaurants scattered in the surrounding streets and alleys.

It is also a good place for some sightseeing as there are ancient-style pavilions, houses, and antique objects, such as a large ancient bell and a bronze relief.

Check out more shopping places in Shenzhen .

7. Enjoy Luxury Shopping and Eating in Sea World (海上世界)

• Address: 1128 Wanghai Road, Nanshan District
• Metro: Take Line 2 to Sea World Station.

Sea World is a high-end recreation center, which includes shopping malls, restaurants, hotels, and parks. There is also a luxury ferry containing top-class stores and restaurants. Having a meal in one of the restaurants on the ferry can be a romantic experience.

See more characteristic restaurants in Shenzhen .

Check Out 2 Urban Villages

Shenzhen is not just a place filled with skyscrapers - over 1,000 urban villages are situated around the high buildings. Today, 60% of Shenzhen's population lives in these urban villages and they are the starting point for many youngsters.

8. Visit Shenzhen's Most Artistic Village: Dafen Oil Painting Village (大芬油画村)

• Location: Dafen Community, Buji Street, Longgang District
• Metro: Take Line 3 to Dafen (大芬) Station.

Over 1,200 workshops and over 8,000 painters are situated in Dafen Oil Painting Village, which has an area of just 0.4 square kilometer. At its peak, over 60% of oil paintings sold on the market originated from Dafen.

Although most of the paintings there are copies, it is a great place to get closer to the local painters and hear their stories. You can walk around, watch the painters working in their workshops, or try your hand at oil painting there.

9. Wander through the Most Historical Village: Huanggang Village (皇岗村)

• Location: junction of Jintian Road and Fumin Road, Futian District
• Metro: Take Line 7 to Huanggang (皇岗) Checkpoint Station.

Huanggang is a historical village and a great place to see how the residents have retained their traditional living environment. Walking around, you will find clean paths, a playground, a fountain, a stage, and more interesting places.

Huanggang also has the first village museum in China. The museum houses objects and photos that record the history of the village.

Enjoy 3 Theme Parks

Although the theme parks in Shenzhen are not world class like the ones in Hong Kong, they are fun options to spend a day or half a day in, or when you are traveling with your kids.

10. See the World in One Day: Window of the World (世界之窗)

• Address: 9037 Shennan Road, Nanshan District
• Metro: Take Line 1 or 2 to Window of the World Station.

It is now possible to see the sights around the world in one day. Shenzhen's theme park called Window of the World allows you to see more than 130 reproductions of some of the famous sights in the world.

Along with these reproductions and models, there are rides and special nighttime entertainment options. There are also special events during festivals.

11. See China in One Day: Splendid China Folk Village (锦绣中华)

• Location: Shennan Avenue, Nanshan District
• Metro: Take Line 1 to OCT Station.

Different from Window of the World mentioned above, Splendid China showcases miniatures of famous Chinese attractions. Visitors can see the sights around China in one day.

Another part of the theme park is the China Folk Culture Village, which provides experiences and custom performances relating to China's ethnic groups. There are also large stage performances.

12. See Uncaged Animals in Shenzhen Safari Park (深圳野生动物园)

• Location: east side of Xili Lake, Nanshan District
• Metro: Take Line 7 to Xili Lake (西丽湖) Station.

Shenzhen Safari Park breeds over 300 animal species and has over 10,000 animals, which are uncaged. There are also circus performances and science events. If you are traveling with your kids, it is a good place to spend one day.

See More Wildlife in 3 Nature Parks

Shenzhen has placed an emphasis on environment protection since the city was first built and its green rating is currently at 50%. There are also ecological parks and wetland parks in the city.

13. Discover a Mangrove Forest in the City: Shenzhen Mangrove Nature Reserve (福田红树林自然保护区)

• Location: estuary of the Shenzhen River, Shatou Xiasha Village, Futian District
• Metro: Take Line 9 to Xiasha (下沙) Station.

This is one of the most important mangrove nature reserves in China, preserving over 170 acres of mangrove forest. The marina promenade provides wonderful natural environments for outings and sightseeing experiences.

14. Watch Birds in OCT Wetland Park (华侨城国家湿地公园)

• Address: 8 East Baishi Road, Nanshan District
• Metro: Take Line 1 to Qiaocheng East (侨城东) Station or Line 9 to Shenzhen Bay Park (深圳湾公园) Station.

OCT Wetland Park is an oasis for plants and birds in the metropolis. The wetland features a saltwater lake and mangrove forest, providing a habitat for 70% of the bird species living in Shenzhen.

15. Visit Rare Plants in Fairy Lake Botanical Garden (仙湖植物园)

• Address: 160 Xianhu Road, Liantang, Luohu District
• Metro: Take Line 2 or 5 to Huangbeiling (黄贝岭) Station.

Fairy Lake Botanical Garden features a research center and a place for sightseeing. It has over 8,000 plant species with a lot of rare or endangered species among them. Visitors can hike around the 21 gardens to admire the different types of plants in them.

See Shenzhen's Top 2 Churches/Cathedrals

There are not many churches in Shenzhen but they are different in style. Some of them are worth a visit even if you are not religious.

16. See a Quaintly Designed Church: St. Anthony's Catholic Church

• Address: 65 Nonglin Road, Futian District
• Metro: Take Line 2, 9, or 11 to Chegongmiao (车公庙) Station, then take bus 326 to the Eye Hospital stop or take a taxi there.

The design of St. Anthony's Catholic Church is different from most Gothic churches. There are no ornate spires and walls or rose windows, just plain walls with a cubic appearance, which resembles a thatched cottage.

Every Sunday at 11am, the Holy Mass is held in English in the church.

17. Admire a Noah's-Ark-Shaped Church: Shenzhen Christian Church

• Address: 126 Meilin Road, Futian District
• Metro: Take Line 9 to Xiameilin (下梅林) Station, then walk east for about 600 meters or simply take a taxi there.

This is the largest gathering place for Christians in Shenzhen. The church is designed in a boat shape, symbolizing Noah's Ark in the Bible, God's redemption of all things in the world, and the idea of equality of all beings.

Uncover Shenzhen's 3 Ancient Sites

Shenzhen's area has been extended hundreds of times over the decades but ancient sites remain that have recorded its history.

18. Admire Ancient Buildings in Dapeng Fortress / Dapeng Ancient City (大鹏所城)

• Location: Pengcheng Village, Dapeng Town, Longgang District

Dapeng Fortress (also known as Dapeng Ancient City) is located in a peninsula in the east of Shenzhen. It was a military fortress 600 years ago.

General Lai Ying Yang's Mansion and the Canoe Museum are worth a visit.

19. Experience Cultural Fusion in Chung Ying Street (Chinese/British Street, 中英街)

• Location: Sha Tau Kok Town, Yantian District

Chung Ying Street is an alley that is 250 meters long and 4 meters wide. Before 1997, when Hong Kong was a British colony, the east side of the street belonged to China while the west side belonged to the UK.

Today, the boundary tablet is still there and a museum holds records of that era's history. You can do some duty-free shopping there.

As there isn't a metro line connected, you can take a taxi there or take a bus to Sha Tau Kok Free Trade Zone. Before entering the site, you will need to apply for a special document with your passport near the entrance.

20. Understand Local Beliefs in Pak Tai Temple (北帝古庙)

• Location: Dongfang Road, Bao'an District
• Transport: From Shenzhen Airport, take bus M527/M528 to Xixiang Pedestrian Street or take a taxi.

Pak Tai Temple is an ancient Taoist temple in Shenzhen. In legends, Pak Tai is the god of water and rainfall, so local people worship him to pray for good weather for the crops.

Every year on the third day of the third lunar month (March 26, 2020 or April 14, 2021), large ceremonies are held to celebrate Pak Tai's birthday.

Visit Shenzhen's Highlights with Us

Using our services, you can enjoy a bespoke tour in Shenzhen. An English-speaking guide and an experienced driver will take you around so you won't need to worry about which metro line to take and how to get to the attractions.

Check out our popular Shenzhen tour for reference:

• 4-Day Guangzhou and Shenzhen Essence Tour

Or contact us to let your travel consultant customize your tour plan.

• 2-Week Private China Tour: Beijing–Xi'an–Lhasa-Shanghai
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• 8 Days in China: Top 15 Tours and Itineraries (2024/2025)
• China Weather in January 2024: Enjoy Less-Crowded Traveling
• China Weather in February 2024: Places to Go, Costs, and Crowds
• China Weather in March 2024: Destinations, Crowds, and Costs
• China Weather in April 2024: Where to Go (Smart Pre-Season Pick)
• China Weather in May 2024: Where to Go, Crowds, and Costs
• China Weather in June 2024: How to Benefit from the Rainy Season
• China Weather in July 2024: How to Avoid Heat and Crowds
• China Weather in August 2024: Weather Tips & Where to Go
• China Weather in September 2024: Weather Tips & Where to Go
• China Weather in October 2024: Where to Go, Crowds, and Costs
• China Weather in November 2024: Places to Go & Crowds
• China Weather in December 2024: Places to Go and Crowds

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Shenzhen Attractions - Things to Do

Shenzhen is a young and modern metropolis in southern China adjacent to Hong Kong. Best known for its fast development over the last decades, it attracts flowing businessmen to seek for opportunities, especially cheap but good-qualified electronic products. It is also a great holiday destination with fabulous tourist attractions. Many first-time visitors won’t miss the two highlights in Shenzhen, namely the Window of the World and Splendid China - Folk Culture Villages. In the Window of the World, you are able to visit many of the world's great landmarks in just one day. In Splendid China – Folk Culture Villages, you will see miniature replicas of many famous Chinese historical sites and life-sized examples of distinctive buildings of ethnic groups. In addition, there are other theme parks for you to explore, such as the Shenzhen Happy Valley and Overseas Chinese Town East. As a coastal city, Shenzhen also offers great spots for you to feel the charm of sea. The best Shenzhen tourist attractions at the seaside include the Dameisha Beach, Xiaomeisha Beach, Shenzhen Bay Park and Xiaomensha Sea World. Time permits, you can also spend a leisure day at Jiaochangwei, a small fishing village in the east of Shenzhen where there are characteristic gourmet restaurants and homestays.

Top 5 Shenzhen Attractions

• It showcases the miniatures of famous landmarks from all over the world, such as the Taj Mahal in India, the Pyramids of Egypt, and the Sydney Opera House in Australia.
• The park is divided into eight main areas, including the World Square, Asia, Oceania, Europe, Africa, America, World Sculpture Garden and International Street.
• Visitors have a chance to enjoy the folk performances of different countries and nationalities, like the Halloween Night Party and aboriginal African dance.
• There are some activities for adventurous travelers experiencing high-tech, including jungle trekking, journey to the center of the Earth, flying over America, etc.

• It is especially suitable for those having a tight schedule but wishing to explore China as much as they can.
• Visitors can admire the mini-sized Great Wall, Forbidden City, Terracotta Army, Mogao Caves, and Potala Palace in only one day.
• The park also demonstrates the costumes, folk villages, and unique customs and habits of various ethnic groups in China through grand performances.
• Visitors can have a deeper understanding of Chinese culture, art, history and ancient architecture.

• It is a comprehensive large eco tourist attraction in Shenzhen which contains the Knight Valley, Tea Stream Valley, Wind Valley, and Huaxing Temple.
• The three fantastic towns inside, including the Seafield Village, Interlaken Town, and Ancient Tea Town, feature romantic tea and wine culture.
• As the core area, the Knight Valley offers visitors a thrilling experience in the Rapids Forest, waterfall, and glass skywalk, while the Tea Stream Valley is more idyllic with a large area of flowers and tea trees.
• The show themed on Zen Buddhism and Tea culture should not be missed.

• It’s always on the list of the must-visit Shenzhen attractions for families and thrill seekers.
• There are altogether 9 theme areas, providing over 100 kinds of rides and games, so that visitors of all ages can enjoy a good time.
• The most popular attractions include Shooting the Chute, the Suspended Looping Coaster, and the Space Shot.
• The Playa Maya Water Park inside is open from May to early October. You will have an unforgettable experience on water if visiting during this period.

• A free bathing beach stretching as long as 1.8 km (1.1 mi) and a great site for lovers to date.
• The water here is very clear and the beach is soft and clean. On sunny days, you can enjoy a sun bath or a picnic on the beach.
• A walk on the seaside plank will make you feel very tranquil and relaxed.
• You can take part in lots of entertainment activities on the beach, like riding a motorboat, bungee jumping, and volleyball. It’s also allowed to swim in the sea.

More Attractions in Shenzhen

• historical sites
• modern sites
• natural scenery
• religious sites
• Chung Ying Street
• Fairy Lake Botanical Garden
• Meridian View Center
• Mission Hills
• Safari Park
• Shenzhen Bay Sports Center
• Universidad Sports Centre
• Xiaomeisha Beach
• Xiaomeisha Sea World
• Xili Lake Holiday Resort

Shenzhen Attractions Visiting Plan for Reference

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2024 Volvo China Open final results: Prize money payout, DP World Tour leaderboard, how much each golfer won

The 2024 Volvo China Open final leaderboard is headed by winner Adrian Otaegui, who topped the DP World Tour leaderboard this week with a win at Hidden Grace Golf Club in Shenzhen, China.

Otaegui won on the DP World Tour for the fifth time after winning the 54-hole event by three shots on 18-under 198. The event was shortened to 54 holes with interrupting weather.

Guido Migliozzi finished in solo second on the week, after Otaegui overcame a five-shot final-round deficit to win.

Sebastian Söderberg, Paul Waring and Joel Girrbach finished in a tie for third place, a shot behind Migliozzi.

Otaegui won the €356,293.78 winner's share of the $2,250,000 purse.

Volvo China Open recap notes

Otaegui earned 14.5 Official World Golf Ranking points with the win, with the minimum point schedule removed from the Official World Golf Ranking.

There was a 36-hole cut, with 68 of 156 starting players finishing the event in the 19th completed event of the season.

Otaegui earned 500 Race to Dubai points as a DP World Tour member, with its season-long Race to Dubai points race well underway for 2023-2024. He also earned a spot in the PGA Championship through the DP World Tour points standings.

The 2024 European Tour schedule continues in two weeks with the PGA Championship.

2024 Volvo China Open final leaderboard, results and prize money payouts

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China to Mexico: Launching the world's eighth-longest flight

O n Saturday, May 11, the inaugural flight of one of the world's longest routes will depart from Shenzhen, China, and arrive in Mexico City. The journey will take passengers 16 hours in the air and, due to a stopover on the return leg, over 21 hours.

China Southern Airlines will introduce its first direct flight from Shenzhen to Mexico's capital, setting a record as the longest scheduled flight departing from China and ranking as the eighth longest worldwide.

A record-setting route from China

The service will utilize an Airbus A350, covering more than 8,700 miles in one direction.

According to the airline, the first flight is scheduled for May 11. The nonstop trip from Shenzhen, located in southern China, to Mexico, is expected to last 16 hours, and flights will operate on Tuesdays and Saturdays.

The trip back will be considerably longer—specifically, 5 hours more, totaling 21 hours and 20 minutes. This increase is attributed to a necessary refueling stop in Tijuana, during which passengers must stay on board. The return flights to Shenzhen are planned for Wednesdays and Sundays, beginning May 12.

Previously, the longest flight from China was operated by China Southern Airlines from Guangzhou to New York, spanning nearly 8,078 miles.

The current global record holder is the flight from Singapore to New York. Operated by Singapore Airlines, this route stretches 9,500 miles and takes 19 hours aboard an Airbus A350.

Singapore Airlines highlights its aircraft's design features for passenger comfort, including high ceilings, LED lighting, increased window access, and enhanced cabin ventilation.

Trapped in an elevator: Qatar Airways crew causes chaotic flight delay from Birmingham to Doha

The world's "skinniest" hotel. Amazing pictures

Atlantic's strong winds propel planes to near-sonic speeds, ensuring early arrivals

Low-budget Chinese travellers highlight shift in Hong Kong tourism

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Jessie Pang joined Reuters in 2019 after an internship. She covers Hong Kong with a focus on politics and general news.

Joyce is a talented visual journalist based in Hong Kong and Greater China, driven by a profound passion for capturing historical moments and sharing stories through the medium of video. Her exceptional skills and dedication to her craft earned her the prestigious title of Reuters Video Journalist of the Year in 2019. Joyce's work revolves around documenting significant events and exploring the rich cultural tapestry of the region. Outside of her professional pursuits, she finds solace and inspiration in hiking, immersing herself in the beauty of nature. With a deep appreciation for tea, Joyce enjoys discovering its diverse flavours and delving into its rich history.

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• Published: 08 May 2024

Estimating and modeling spontaneous mobility changes during the COVID-19 pandemic without stay-at-home orders

• Baining Zhao   ORCID: orcid.org/0000-0002-9653-3316 1 , 2   na1 ,
• Xuzhe Wang 1   na1 ,
• Tianyu Zhang 1 ,
• Rongye Shi   ORCID: orcid.org/0000-0003-4298-9358 3 ,
• Fengli Xu 4 ,
• Fanhang Man 1 ,
• Erbing Chen 5 ,
• Yang Li   ORCID: orcid.org/0000-0002-2053-6393 1 ,
• Yong Li   ORCID: orcid.org/0000-0001-5617-1659 4 ,
• Tao Sun   ORCID: orcid.org/0000-0003-2570-543X 2 &
• Xinlei Chen 1 , 2 , 6  

Humanities and Social Sciences Communications volume  11 , Article number:  591 ( 2024 ) Cite this article

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Comprehending the complex interplay among urban mobility, human behavior, and the COVID-19 pandemic could deliver vital perspectives to steer forthcoming public health endeavors. In late 2022, China lifted its "Zero-COVID" policy and rapidly abandoned nearly all interventions. It provides a unique opportunity to observe spontaneous mobility changes without government restriction throughout such a pandemic with high infection. Based on 148 million travel data from the public bus, subway, and taxi systems in Shenzhen, China, our analysis reveals discernible spatial discrepancies within mobility patterns. This phenomenon can be ascribed to the heterogeneous responses of mobility behavior tailored to specific purposes and travel modes in reaction to the pandemic. Considering both the physiological effects of virus infection and subjective willingness to travel, a dynamic model is proposed and capable of fitting fine-grained urban mobility. The analysis and model can interpret mobility data and underlying population behavior to inform policymakers when evaluating public health strategies against future large-scale infectious diseases.

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COVID-19 is linked to changes in the time–space dimension of human mobility

Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States

Practical geospatial and sociodemographic predictors of human mobility

Introduction.

As one of the most infectious pandemics, COVID-19 has resulted in a devastating toll of over 7 million lives lost and an estimated $8 trillion in economic damage (Clark et al., 2020 ; Taskinsoy, 2020 ; Weiss et al., 2020 ; Zhang et al., 2022 ). Research on the COVID-19 pandemic and its impact holds the potential to offer valuable insights for addressing unforeseen large-scale, highly infectious diseases in the future (Chen et al., 2022 ; Menkir et al., 2021 ; Sibley et al., 2020 ).

Due to rapid urbanization, more than 55% of the global population resides in urban areas which serve as a hotbed for infectious diseases due to dense population (United Nations, 2018 ). It is imminent to study the response and recuperation of urban mobility during the influence of COVID-19 (Arellana et al., 2020 ; Atkinson-Clement and Pigalle, 2021 ; Gkiotsalitis and Cats, 2021 ; Levin et al., 2021 ) to facilitate understanding of the spread of the pandemics (Chang et al., 2021 ; Wei et al., 2021 ). Simultaneously, the change in urban mobility profoundly influences economic dynamics and mental health outcomes, among other societal aspects (Wang et al., 2021 , 2022 ). Existing research has primarily investigated how governments proactively implement mandatory or advisory stay-at-home orders to change people’s mobility behavior and contain virus transmission (Martínez and Short, 2021 ; Shen et al., 2020 ; Tirachini and Cats, 2020 ; Wang et al., 2022 ; Zhang, 2021 ). However, there is limited knowledge regarding the spontaneous change in citizens’ mobility behavior during the COVID-19 pandemic. The spontaneous change consists of individuals’ voluntary adaption of their travel behavior concerning the infection rate of disease and perceived threats, without the restriction of government policies (Han et al., 2021 ). In the case of COVID-19, the self-driven behavior involves engaging in home-based care while infected, limiting travel to essential trips only, avoiding crowded places, shifting on-site work to remote work to reduce potential exposure to the virus, etc. (Balmford et al., 2020 ; Tisdell, 2020 ). A better understanding of these spontaneous mobility changes can provide valuable insights into the necessary mobility behavior of the citizens and in turn facilitate the design of urban mobility-related policies during pandemics.

In this paper, we investigate spontaneous mobility changes without stay-at-home orders throughout a highly infectious pandemic, from its emergence through large-scale proliferation to eventual stabilization. The mobility changes are manifested as fluctuations in the number of individuals traveling, which are microscopically composed of varying purpose-specific mobility behavior. We aim to answer the following research questions:

Q1: At the temporal and spatial scale, how does urban mobility evolve in response to a pandemic’s lifecycle?

Q2: Given distinct regions corresponding to varied functional zones and data pertaining to various modes of transportation within a city, what is the underlying mobility behavior, and how do they react to the pandemic?

Q3: How to establish a dynamic model to deduce the spatio-temporal mobility changes of the entire city based on the behavior of different travel purposes and modes?

To answer these questions, we utilize origin-destination (OD) mobility data involving 148 million occurrences before and after the cancellation of the "Zero-COVID" policy in Shenzhen subway, bus, and taxi systems, China, as shown in Fig. 1 a, b. The OD data logs the time and quantity of individuals traveling from areas proximal to the origin to regions near the destination in the urban public transport system. Such data encapsulates the spatio-temporal mobility of urban populations, focusing solely on the volume of individuals moving between two regions, while avoiding the disclosure of individual-specific information. This ensures a high degree of privacy preservation.

a Trips in the Shenzhen bus, subway, and taxi systems from December 8, 2022, to January 6, 2023. China adhered to a rigorous "Zero-COVID" policy and implemented strong non-pharmaceutical measures from the beginning of the COVID-19 outbreak until early Dec. 2022. As a result, over 99% of the population in China had never been infected by any variant of SARS-CoV-2. Even though China heavily distributed the COVID-19 vaccine, the vaccine merely prevents people from serious symptoms without comprehensive immunity due to the fast variability of COVID-19. On December 7, 2022, the "Zero-COVID" policy, along with the travel restrictions, was rescinded. The travel volume immediately returned to a level approximating normalcy. Thereafter, the spread of SARS-CoV-2 precipitated a substantial decrease in urban mobility. A progressive recovery would then succeed. To highlight the impact of the pandemic more effectively, we conducted normalization separately for weekdays, Saturdays, and Sundays, ultimately showcasing the trip rate. b The lines bridging the origin and destination maps symbolize the human movement between two corresponding regions. Due to the dense nature of OD data, we depict flows exceeding a daily average of 200 for clarity. Three time periods are highlighted: before the COVID-19 pandemic outbreak, during the peak impact, and during the gradual stabilization period. These periods correspond to three different levels of mobility.

The period before and after the cancellation of the "Zero-COVID" policy provides a unique opportunity to observe the spontaneous evolution of urban mobility without mobility-restricting measures, facing the pandemic spreading on a large scale in a short period. The "Zero-COVID" policy focuses on promptly identifying and containing localized outbreaks using advanced technologies such as big data analysis and nucleic acid screening (China CDC, 2022 ). Through strict quarantine measures, identification of infection sources, and high-risk group identification, China aims to swiftly end outbreaks with minimal societal and economic impacts. China’s implementation of the "Zero-COVID" policy has been highly successful. Despite its massive population of 1.4 billion people, the country has reported relatively low numbers of COVID-19 cases and deaths before the cancellation of the "Zero-COVID" policy. Impressively, China has recorded 1,655,477 cases (less than 0.2% of the population) and 13,524 deaths (less than 0.1%% of the population) (Burki, 2022 ). In late 2022, the Omicron variant of SARS-CoV-2 became predominant worldwide and it was known for its high transmissibility (basic reproduction number ≈ 9.5) and penetration rate. Yet, compared to previous variants, it also demonstrated a relatively lower infection fatality rate (<1%) (Liu et al., 2022 ). Under such a circumstance, on Dec. 7, 2022, China lifted the "Zero-COVID" policy along with the travel restrictions. However, the Omicron variant showed a high rate of immune escape from vaccines which merely prevented people from serious symptoms without delivering comprehensive immunity. Therefore, the lift of the policy led to a rapid and massive spread of the Omicron variant of SARS-CoV-2. It is estimated that within a month, the proportion of infected individuals in cities sharply escalated from nearly zero to surpassing 70% (Leung et al., 2023 ). Besides, Shenzhen is a megacity with a permanent population exceeding 17 million individuals and a population density reaching 8,800 individuals per square kilometer (Shenzhen Government, 2023 ). The research on mobility behavior during the pandemic in Shenzhen is representative and informative.

An analytic framework and a dynamic model are proposed with the following key designs. In terms of spatial distribution, mobility in central business districts (CBDs) and their adjacent areas is significantly impacted. The mobility changes can be quantitatively represented by a time series and evaluated by features about the decline and recovery of trips. To explore the spatial disparities, four mobility patterns are found among the mobility changes corresponding to various OD pairs through the K -means++ clustering algorithm (answering Q1 ). We overlay an urban land use (ULU) map on origins and destinations, thus elucidating the intent behind mobility behavior, such as commuting, recreation, schooling, and more. The differences in mobility changes among subways, buses, and taxis are also discussed (answering Q2 ). Inspired by this, we try to devise a dynamic model of changes in passenger flow, predicated on the impact of COVID-19 on different mobility behavior. We model distinct mobility behavior by combining physical infection and the willingness influence, which subsequently deduces the effects of ULU embodying travel intentions. As a result, we can infer the mobility within the geographic regions housing these lands with different uses (answering Q3 ). Our model allows us to not only fit observed changes in trips but also to conduct detailed analysis at a granularity of less than 500m. We believe the proposed insight and model could be leveraged to provide public health officials with a holistic recommendation as they decide on mobility-related policies under similar pandemic conditions.

Spatial distributions of urban mobility level

The spatial distribution of the decline and recovery in mobility levels can be approximately observed based on Fig. 2 . This observation is made at the granularity of a 1km × 1km grid level. For subways, buses, and taxis in a grid, the initial travel volumes for departures and arrivals are aggregated and compared with the passenger flow from the pre-pandemic period to assess the level of mobility. It is observed that the mobility in CBDs was more significantly impacted by COVID-19 compared to areas farther from the CBDs.

a The departure and arrival passenger flows for each region were aggregated and then divided by their pre-pandemic values to ascertain the mobility levels in each area. Notably, Day 15 was observed to have one of the most significant reductions in passenger flow. Day 7 marked a period of decline, whereas Days 21 and 28 were identified as recovery phases. Furthermore, a disparity in the impact on mobility levels was observed between the CBD and its adjacent areas compared to regions located further from the CBD. b The spontaneous mobility changes consist of the reduction, trough, and recovery stages. These three stages are quantitatively measured and a set of corresponding features are formed, as listed in Table 1 .

CBDs are typically characterized by high density, both in terms of population and infrastructure. This density leads to more congested public spaces and transportation systems. During the pandemic, such conditions increased the risk of virus transmission, leading to more stringent movement restrictions and a greater reluctance among the public to travel in these areas. Furthermore, this may be related to the attributes of land use, which will be discussed in the section “Population behavior behind mobility patterns”.

Identifying mobility patterns

To investigate mobility changes at a finer granularity (street level), bus and subway data were processed based on station information and passenger flow, while taxi data were analyzed by urban areas (see Methods for a detailed description of the data). The OD data derived from the public bus, subway, and taxi systems in Shenzhen include both spatial and temporal information on urban mobility. In the bus system, bus routes establish connectivity between two distinct urban regions corresponding to the origin station and destination station, and passenger flow is influenced by the gradual spread of the epidemic, resulting in temporal variations. This principle is similarly applicable to subway and taxi systems.

Following the large-scale outbreak of the COVID-19 pandemic (post-December 7, 2022), the mobility for almost all OD pairs swiftly plummeted from pre-pandemic levels to a markedly low volume, eventually showing signs of gradual recovery, as shown in Fig. 2 . This change in trips can be delineated via a temporal variation curve for each OD pair, as shown in Fig. 3 a. We normalize each time series of OD passenger flow using historical data (i.e., typical passenger flow levels before November 25, 2022) and subject the data to preprocessing.

a This time series chart shows the changes in OD trip rate during the period from December 8, 2022, to January 6, 2023. The clustering algorithm identifies four distinct groups with different mobility patterns of decline and recovery. The middle line in each plot represents the average trip rate for each cluster. b Through the spatial attractiveness of ULU, we can infer prominent mobility behavior under each cluster. The changes in mobility are related to mobility behavior between different origins and destinations. c indicates the total impact on the overall passenger volumes of subway, bus, and taxi services.

To quantify the initial decline and subsequent recovery in mobility during the COVID-19 pandemic (Fig. 2 ), we establish a set of features to assess how trips evolve, as listed in Table 1 . The concept of resilience is about how the system responds to disturbances (Qian et al., 2022 ; Schwarz, 2018 ; Standish et al., 2014 ; Tabatabaei et al., 2018 ): does it withstand the shock and remain unchanged, does it adapt and transform into a new state, or does it collapse (Forzieri et al., 2022 ; Kumpfer, 2002 ; Zhao et al., 2021 ). Inspired by this, we concentrate on the resilience of mobility to the pandemic—a characteristic of mobility decreasing and rapidly recovering from large-scale infectious diseases. Features are devised to quantitatively measure the three stages—reduction, trough, and recovery— capturing the mobility changes throughout the COVID-19 pandemic.

Once the features of changes in mobility for OD pairs are extracted, the mobility trends for different modes of transportation can be uniformly analyzed. Based on the aforementioned properties, we employ unsupervised machine learning technology, i.e. K-means++ clustering algorithm (Arthur and Vassilvitskii, 2006 ), to discern the similarities and disparities in passenger flow among various OD pairs (see Methods for detail). As depicted in Fig. 3 a, the K -means++ algorithm categorizes the OD trip trends into four distinct patterns.

The OD mobility in Cluster 1 exhibits the most notable decline, with a total magnitude of impact of -13.47(%  ⋅  days), as presented in Table 1 . In the initial phase of the COVID-19 pandemic, a precipitous decrease in passenger flow was observed, with the average value plummeting to less than 50% by the 12th day. While the subsequent descent in passenger flow exhibited a slower pace, a persistently low level or downward trend was maintained. Certain OD passenger flows reached a standstill till the conclusion of the statistical period. This suggests a high degree of travel flexibility within this cluster, or significant aversion to infection, implying that this demographic endeavors to avoid travel throughout the pandemic. Furthermore, the resurgence of travel intent tends to be protracted, with a near-zero recovery rate in the short term.

Cluster 2 exhibits a pronounced U-shape curve with a comparable decline rate, as shown in Fig. 3 a. The nadir of OD passenger flow appeared around the 15th day, hovering at approximately 50%. Thereafter, the travel volume exhibited a gradual resurgence, culminating in an average travel volume recovery of 82.2%. The graphical representation illustrated a significantly slower pace of travel recovery in the pandemic’s later stages, in contrast to the rapid decline observed during the initial phases. The mobility behavior characterizing Cluster 2 aligns with the epidemiological patterns of infection and recovery. During the initial phase of the COVID-19 pandemic, citizens refrained from traveling, either due to active infection or as a preventive measure against contagion. However, as the infection peaked and those infected began to recover, travel volume followed a trajectory of gradual recovery. This pattern is representative of the majority of mobility behavior.

The mobility change curve for Cluster 3 also exhibits a U-shaped pattern; however, its recovery speed significantly outpaces that of Cluster 2. Following the onset of the pandemic, there was a rapid decrease in passenger flow, averaging approximately a 48.8% reduction. Contrasting with the sluggish recovery observed in Cluster 2, the resurgence pace in Cluster 3 mirrors the speed of passenger flow decline experienced during the reduction stage. Consequently, by the conclusion of the observation period, the OD mobility level essentially reverted to its pre-epidemic benchmark. In the end, the mobility behavior corresponding to this cluster rapidly surmounted the impacts of the COVID-19 pandemic, facilitating, and even accelerating, the return to pre-epidemic levels.

The mobility patterns of OD bus stations in Cluster 4 are minimally impacted by the COVID-19 pandemic, with the total impact merely a third of that experienced by Cluster 1. The corresponding curve demonstrated minor fluctuations, with a peak declining amplitude of 38.2%. Across the tripartite stages of mobility alteration (reduction, trough, recovery), the pandemic’s impact on passenger volume was relatively insubstantial. This suggests that within this cluster, the corresponding origins and destinations exhibit robust travel demand, complemented by relatively inflexible mobility behavior.

Population behavior behind mobility patterns

The two crucial facets of population behavior are where to go and how to get there. Consequently, we delve into discussions on insights regarding travel purposes and modes during the processes of mobility decline and recovery.

Travel purpose

Mobility between two areas is aggregated from population behavior with various travel purposes. Lands with specific uses around each origin and destination have the potential to reveal the land-use characteristics of the places where passengers most visit (Chang et al., 2021 ; Sun et al., 2007 ). Therefore, the integration of OD data and the ULU map has the potential to reveal the travel purposes behind OD trips. For example, if a passenger departs from a station (or pick-up point of a taxi trip) surrounded by lands with the category of residential and alights at lands with the category of the company at night o’clock, it can be inferred that the passenger is most likely commuting to work. Therefore, the ULU categories clearly indicate people’s travel purposes.

More generally, we establish a ULU feature vector to represent the probability of departure or arrival at each nearby urban land category (Xing et al., 2020 ). We have collected nine common types of ULU information from Gaode Maps and Baidu Maps, including the residential, company, commercial service, transport hub, college, school, hospital, cultural/sport, and park/scenery. The OD pairs are mapped to OD land pairs according to the corresponding ULU feature vectors (see Methods for details). We further delineate the primary ULU visited and the key travel objectives within each cluster, as presented in Fig. 3 b.

Travel behavior to schools is mainly classified within Cluster 1 and Cluster 2. Following COVID-19’s onset, passenger flow experienced a precipitous decline, with no short-term recovery trend in sight. Firstly, the health of minors is often perceived as more vulnerable, prompting parents to exercise added caution in safeguarding their children from the COVID-19 virus (She et al., 2020 ). Parents likely prefer to refrain from sending their children to school, owing to concerns regarding viral transmission. Subsequently, as infection rates escalated, schools transitioned to online or remote learning modalities (Betthäuser et al., 2023 ). This shift obviated the need for students, teachers, and staff to commute daily, thereby perpetuating the decrease in passenger flow. The passenger volume at school-associated stations was anticipated to remain low until the pandemic stabilizes. As for colleges, numerous students were sent home ahead of the lifting of the "Zero-COVID" policy, resulting in a significant decline in passenger flow.

The majority of trips from residential, company lands to park/scenery, cultural/sports lands are concentrated within clusters with a more significant impact on mobility. Analyzing travel sentiments reveals that these journeys are deemed non-essential, and the inclination to embark on them significantly diminishes following the COVID-19 pandemic outbreak (Han et al., 2021 ). Given the increased risk of COVID-19 infection, older populations exhibit heightened caution when using buses and visiting crowded locations, resulting in highly affected resilience. After contracting COVID-19 (Wang et al., 2022 ), individuals experience compromised systemic and pulmonary functions (Mulcahey et al., 2021 ), leading to a reluctance to engage in strenuous physical activity at sports halls for a short duration. Consequently, there was a diminished recovery in passenger flow to sports halls. Although there is no lockdown ban, citizens avoid visiting by bus and maintain social distance.

Intriguingly, despite the overarching epidemic conditions, passenger counts for the transport serving major transport hubs such as airports, train stations, and ferry terminals exhibit comparatively minor reductions and rapid recovery, mainly aligning with the trends of Cluster 3 and Cluster 4. The relative stability of these numbers can largely be attributed to the inherent nature of long-distance travel across cities. Unlike short-range transit, which may be supplanted by walking, cycling, or personal vehicles, alternatives for long-haul journeys are notably limited, thereby maintaining a baseline demand for buses servicing these transport hubs even during the height of the COVID-19 pandemic.

The alterations in mobility patterns concerning residential—hospital are primarily observed within clusters characterized by a comparatively modest reduction in mobility. The OD trips exhibited fluctuation, characterized by a decline and a gradual recovery within 30 days. In the initial stages, individuals with chronic conditions are advised to minimize hospital visits to lower their exposure risk to COVID-19. However, the pandemic triggered an escalation in healthcare demand, as numerous individuals sought medical care, testing, and treatment (Birkmeyer et al., 2020 ; Peiffer-Smadja et al., 2020 ). The heightened need for infectious disease services counterbalanced the reduction in visits to other outpatient clinics in hospitals, thereby contributing to the recovery of passenger flow towards hospitals. Moreover, the surge in hospital visits necessitated that healthcare workers and other essential personnel continue to commute to and from hospitals, irrespective of the pandemic situation. Their unwavering travel patterns help maintain a basic level of passenger flow of over 40% trip rate.

The behavior disparity between residential—company exhibits various patterns, with a significant proportion found in various clusters. Overall, they are relatively less affected, serving as primary driving factors for urban mobility recovery. It is possibly related to the various industries of companies. On one hand, the characteristics of various industries significantly influence the patterns of public transport use. Employees in industries of internet technology and electric communication demonstrate greater adaptability to remote work during the COVID-19 pandemic. Due to the pressing demand for healthcare products, drugs, and research, stations located near pharmaceutical companies are expected to experience a faster recovery in passenger flow. The nature of manufacturing and factory work typically requires on-site participation, rendering remote work impractical. On the other hand, income levels within industries also influence travel patterns. Employees in the internet and telecommunications industries tend to have higher incomes and have more flexibility in choosing their transport modes, potentially opting for private vehicles over public transport during the COVID-19 pandemic to reduce exposure risks. In contrast, lower-income employees might be more reliant on public transportation to commute to their workplaces, maintaining the demand for buses. The income disparity between industries further contributes to the observed differences in passenger flow trends during the pandemic.

Travel behavior involving commercial service falls within Cluster 3 and Cluster 4. The effect on the service industry essentially aligns with the proportion of the population infected with the virus. Following about three years of "Zero-COVID" policy, the associated panic has largely dissipated. As people yearn to return to normalcy and recreational activities, customer flow at shopping malls stages a swift recovery in later phases. This observation appears comprehensible, given the fundamental nature of work activities as a pivotal component of economic endeavors. With the resumption of work activities, a consequential surge in demands for commercial activities is anticipated (Ma et al., 2023 ).

Overall, during the COVID-19 pandemic, commuting, commercial, and healthcare demand constituted the largest components of urban mobility. It is crucial to maintain and promptly restore the supply of buses among communities, transport hubs, hospitals, and companies. Integrating ULU data with OD data enables governments and transport operators to thoroughly analyze and elucidate shifts in mobility behavior during the pandemic. This comprehensive approach offers a solid foundation for well-informed policy development and implementation against unforeseen pandemics with high infection and low case fatality rates.

Travel mode

The total impact for the overall passenger volumes of subway, bus, and taxi services are computed, as presented in Fig. 3 c. The data indicates varying degrees of reduction in passenger flow, with subways (−9.42%  ⋅  days) experiencing the most significant decline, followed by buses (−8.63%  ⋅  days), and taxis (−7.90%  ⋅  days). This trend can be primarily attributed to the perceived risk of COVID-19 transmission in different transportation environments and the adaptive responses of urban populations to the pandemic.

Subways, typically characterized by high passenger densities and closed environments, represent the apex of perceived transmission risk. The significant drop in subway usage can be attributed to people’s avoidance of crowded spaces and potential virus hotspots. Furthermore, the role of subways as connectors of various urban hubs rendered them particularly vulnerable to reduced usage as individuals sought to minimize travel and potential exposure to the virus. In contrast, buses, while also experiencing a notable decline in passenger flow, were marginally less impacted than subways. This difference might be due to the varied nature of bus routes, some of which cater to essential travel less feasible via other means, and the slightly lower passenger densities compared to subways.

Taxis, offering more individualized and controlled travel environments, demonstrated the least reduction in passenger flow. This trend suggests a public preference for modes of transportation perceived as safer and less conducive to virus spread. Nonetheless, the overall decline in taxi usage reflects broader patterns of reduced mobility, driven by lockdown measures, the shift to remote work, and heightened public health awareness. Economic factors also played a role, as the financial impacts of the pandemic might have influenced individuals’ transportation choices, with taxis being a costlier option compared to public transit. Collectively, these observations underscore the multifaceted impact of the COVID-19 pandemic on urban transportation, shaped by an interplay of health, lifestyle, and economic considerations.

Dynamic model of urban mobility

We develop a dynamic OD mobility model to quantitatively simulate the fine-grained impact of the COVID-19 pandemic on OD passenger flow. The mobility changes can be ascribed to the rate of COVID-19 infection and the willingness to travel. We employ an epidemic transmission model, the susceptible-infectious-removed (SIR) model (Cooper et al., 2020 ; Keeling and Eames, 2005 ), to simulate the rise in infection cases in the city. In general, people who got afflicted with COVID-19 would recuperate at home for days, leading to a plunge in overall city-wide human mobility flow. However, some patients might choose to crowd into the hospitals which leads to an unusual increase in travel needs between urban lands. Therefore, we establish the willingness factors to represent the emotional effects of the COVID-19 pandemic on various types of mobility behavior. The disparity in OD passenger flow under high spatial granularity can be elucidated by the travel purposes and travel modes (refer to Methods for details).

Specifically, people in the dynamic OD model have three distinct states: usual (U), infectious (I), and recovered (R). (Fig. 4 ). In contrast to the classic SIR model, the usual state in the proposed model refers to passengers who remained uninfected by the COVID-19 pandemic, with unaffected travel willingness. Infectious passengers encompass those who have contracted the COVID-19 virus or have been driven by panic to abstain from bus travel. Over time, if affected passengers decide to resume travel, they transition out of the recovered state. The model has merely four free parameters that scale: (1) transmission rate of the COVID-19 pandemic, (2) recovery rate of the COVID-19 pandemic, (3) willingness factors for transmission, and (4) willingness factors for recovery; all four parameters persist as constants over time. The first two parameters, which are determined by the infectivity and virulence of the epidemic itself, remain consistent for all OD pairs. The latter two categories of parameters are determined by travel purposes, which can be inferred by the ULU surrounding OD bus stations.

The model inputs encompass the pandemic’s transmission and recovery rates, as well as people’s willingness factors. Furthermore, the model requires the ULU information near bus stations and the pre-outbreak historical passenger flow levels. In the model, the passengers have usual (U), infectious (I), and recovered (R) states. We fit models to all OD pairs in bus, subway, and taxi systems, which shows full model fits of four clusters corresponding to Fig. 3 . The blue line represents the model predictions. As the trips of the OD passenger flow tend to have great variability, we also show the smoothed average (line of a different color from blue). Shaded regions denote the 2.5th and 97.5th percentiles across stochastic realizations. The green dashed line represents the predictions without considering willingness factors (In this case, the predictions for all clusters are consistent). We sample 100 parameter sets of willingness factors and perform stochastic realizations for each set.

The willingness factors serve as a measure of how much the panic mentality contributes to the decline and recovery in mobility. They reflect the degree of anxiety and perceived risk associated with traveling during the COVID-19 pandemic. For example, as the number of infectious passengers increases, other passengers perceive a higher risk of infection when taking the subway, which leads them to seek alternative means of transport or avoid traveling altogether. This corresponds to a negative willingness factor. Conversely, the surge in public health demand during the COVID-19 pandemic leads to positive willingness factors associated with medically related travel. The willingness factors collectively illustrate the various responses of different travel purposes to the COVID-19 pandemic.

Our model precisely matches the observed trips of OD pairs in Shenzhen from December 8, 2022, to January 6, 2023, as shown in Fig. 4 . As shown in Supplementary Fig. 1 , the average values of willingness factors correspond to the analysis of mobility patterns in section “Population behavior behind mobility patterns”. For example, the value of the willingness factor for recovery associated with the travel behavior of school-age children exhibits a significantly negative trend, indicating a slow recovery in their travel activities. This observation aligns well with the predominant mobility pattern observed within Cluster 1. The willingness factors for the recovery of “residential-transport hub” and “company-transport hub” are the two highest positive values, indicating that there are fewer viable alternatives for long-distance travel, and as a result, passenger flow is less affected. The distinctions between subway, bus, and taxi services can also be discerned from the willingness factors.

At a micro level, we can forecast the decline and recovery of mobility between any two stations or taxi-operating regions in Shenzhen. At a macro level, our model precisely captures the average mobility changes for the four clusters (Fig. 4 ). The dynamic OD model enables even a relatively straightforward UIR model to accurately fit observed passenger flow, despite mobility behavior during that period. This model offers valuable insights into urban mobility during potential future outbreaks of infectious diseases with high infection, empowering policymakers to deduce alterations in urban mobility and population behavior during the initial stages of an epidemic outbreak.

Although COVID-19 has become a familiar presence in our lives, it continues to pose a significant global threat. As we write this paper, the virus still claims a life every three minutes (WHO, 2023 ). The suffering and the painful lessons learned from the COVID-19 pandemic must not be in vain. The analytical framework and model proposed in this research contribute to the long-term management of the COVID-19 pandemic and offer tools to confront potential future viral epidemics.

Based on 148 million travel records, this paper examines the fine-grained spatio-temporal characteristics of urban mobility during the COVID-19 pandemic. It integrates these characteristics with an urban land use map to elucidate the heterogeneity in the decline and recovery of mobility. In terms of travel purposes, trips originating from schools and colleges experienced a sharp decline attributable to class suspensions and risk aversion to infections. Non-essential travel, such as visits to museums, sports halls, and parks, saw a substantial decrease and a gradual recovery. In contrast, travel associated with commuting, commercial services, and healthcare exhibited relatively modest declines and a quicker rebound. Regarding transportation modes, the most affected to least affected were subway, bus, and taxi. Subsequently, the UIR model with willingness factors is built, which comprehensively captures the influence of COVID-19 on travel willingness. In contrast to previous research that primarily focused on macro-level mobility analysis, we propose a set of indicators to measure the temporal variations in urban mobility and uncover the heterogeneity of mobility changes through clustering. There were rare urban mobility models at the block level encompassing various modes of transportation.

Our dataset has limitations as it does not encompass all modes of urban mobility. The model is also minimalist, neglecting regional differences in epidemic spread. Nevertheless, our findings are valuable for revealing spatial differences in the decline and recovery of travel. The proposed analytical framework and dynamic model are adaptable and extensible, which can be applied to different pandemics of other cities or other travel modes, capturing detailed aspects of real-world urban mobility.

This paper offers potential implications for future application and research aimed at formulating more targeted and effective public health policies and strategies. In the context of evaluating public health policies, our results can guide policymakers in assessing the opportunity cost of urban mobility limitations during large-scale pandemic outbreaks. The dynamic OD urban mobility model provides a benchmark for mobility changes without mobility-restricting intervention, enabling the precise evaluation of the impacts on the transmission rate and economic productivity. Additionally, in the realm of policy formulation, policy decisions can influence mobility behavior by altering travel willingness and emotions associated with different POI types, significantly affecting the outcome of the epidemic infection. Improved paid leave policy or income support can reduce mobility for essential staff during illness. Strengthening hygiene and disinfection, maintaining proper ventilation, and increasing routes between origins and destinations of high travel demand contribute to enhanced passenger safety. Lastly, in terms of future academic research, fusing large language models with computational, interactive agents seems to be a path to realistic simulations of human behavior (Xu et al., 2023 ). Based on emerging urban sensing technologies (e.g., drones (Wang et al., 2022 ) and crowdsensing (Chen et al., 2019 ; Guo et al., 2021 ; Xia et al., 2023 ), diverse and heterogeneous human behavioral data are continuously mined. Integrating travel behavior and large models to construct urban individual profiles for simulating travel behaviors appears to be a promising direction. Additionally, we will continue to investigate the changes and resilience of urban elements when faced with restricted urban mobility, including variations in air pollution (Chen et al., 2020 ), unmanned delivery (Chen et al., 2022 ), and emergency communication (Ren et al., 2023 ).

Origin-destination data of urban mobility

Thanks to the extensive deployment of mobile devices (Chen et al., 2020 ), the travel data are collected by a public transportation service company in Shenzhen. The original data, gathered by 663 bus routes, 16 subway routes, and 17,826 taxis during operation, total comprises approximately 148 million trips. Through advanced data collection (Li et al., 2022 ) and processing techniques (Chen et al., 2018 ), aggregated OD data can be obtained. The data collection period for bus-related information spans from October 15, 2022, to January 6, 2023, while that for subway and taxi data extends from November 1, 2022, to January 6, 2023. The OD data for buses has a shape of (3010, 3010, 98), where the first and second dimensions correspond to 3010 bus stations representing origins and destinations, respectively, and the third dimension represents dates. Similarly, the OD data for subways has a shape of (240, 240, 67) with a similar interpretation. The original data for taxis consists of individual trip records, including vehicle ID, pick-up time, drop-off time, pick–up latitude and longitude, and drop-off latitude and longitude, among other details. Based on the urban land use (ULU) map in Shenzhen (Fig. 5 a), which can be divided into 11362 regions (Gong et al., 2020 ), we perform individual matching of each order’s latitude and longitude to the corresponding urban region or the nearest urban region based on proximity to the region’s boundaries. The taxi OD data has been organized into a matrix of shapes (11362, 11362, 67). Among them, we specifically focused on the data of 56 million urban mobility records spanning from December 8, 2022, to January 6, 2023. The data preceding December 7, 2022, are utilized to calculate the baseline level of mobility and assist in obtaining the spatial attractiveness of ULU.

a This map shows urban land use categories in Shenzhen. b This figure illustrates the process of obtaining the urban land use feature vector.

This dataset, capturing changes in passenger flow before and after China’s cancellation of the "Zero-COVID" policy, is distinct from other datasets. China adhered to the "Zero-COVID" policy from 2020 to 2022, with over 99% of Chinese residents never having been infected with COVID-19. After the policy’s cancellation on December 7, 2022, Shenzhen experienced the entire process of large-scale COVID-19 spread without mobility-restricting intervention. As of July 23rd, 2023, China has implemented universal and free COVID-19 vaccination for its population. The reported vaccination rates are as follows (Xinhua, 2022 ): a cumulative coverage rate of 92.1% for the first dose, a completion rate of 89.7%, and an enhanced immunization rate of 71.7%. Among individuals aged 60 and above, the rates are 89.6% for at least one dose, 84.7% for completion, and 67.3% for enhanced immunization. The widespread administration of vaccines has significantly reduced the mortality rate, facilitating recovery from COVID-19. However, the transmissibility of SARS-CoV-2 remains high. This data offers insights into the effects of highly infectious and low-toxicity viruses on social mobility and public transport use under natural transmission conditions in the city. This information provides a baseline for assessing the potential impact of future infectious diseases on urban mobility in the absence of government intervention.

This dataset encompasses the entire process of trip changes, from the initial stage to a sharp decline, and ultimately to a gradual recovery during large-scale infectious disease outbreaks in cities. From November 1, 2022, to November 25, 2022, Shenzhen experienced fewer than 50 confirmed cases per day, and the daily lives of citizens remained largely unaffected (Shenzhen Government, 2022 ). The data from this period can be considered the baseline passenger volume for Shenzhen’s bus system. As the number of confirmed cases progressively increased, the government implemented stringent lockdown measures from November 25, 2022, to December 7, 2022, resulting in a substantial decrease in passenger flow. As of December 7, 2022, the cumulative number of confirmed cases constituted less than 0.1% of the total population. The official outbreak of the COVID-19 pandemic transpired after the cancellation of the "Zero-COVID" policy. The Omicron variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly (Leung et al., 2023 ), which is evident in the drastic reduction of passenger flow within the public transport system. Subsequently, as a majority of urban residents became infected and recovered, the public transport passenger flow gradually rebounded, as depicted in Fig. 1 .

Assuming the passenger flow from origin i to destination j on day t is denoted as f i , j , t , the entire dataset can be denoted as

where M represents the sum of the number of bus stations, subway stations, and the total number of taxi-operating urban regions. T is the number of days.

Point of interest data and urban land use data

As online social media and mobile communication continue to flourish, location-based service (LBS) systems such as Google Places, Gaode Maps (China Satellite Maps), and Facebook are gaining popularity across various sectors (Han et al., 2021 ; Xing et al., 2020 ). These LBS systems enable users to search for points of interest (POIs) to access better services and share experiences from places they have visited (Chang et al., 2021 ). Generally, a POI is a specific location that individuals may find useful or intriguing. The term is commonly used to refer to commercial services, schools, subway stations, or other categories found in digital maps (Wang et al., 2019 ; Zhu et al., 2020 ).

A detailed land use map of Chinese cities can be derived by combining POI data with 10-meter satellite imagery, OpenStreetMap, nighttime lights, and Tencent social big data (Gong et al., 2020 ). This map serves as a valuable resource for inferring travel intentions. As the data is from 2018, we complement the newly developed land use attributes based on the latest available POI data. The data sources include Gaode Maps and Baidu Maps, which are leading providers of digital map content, navigation, and location service solutions in China. Important urban land use categories for travel are selected, including residential, company, commercial service, transport hub, college, school, hospital, cultural/sport, and park/scenery.

OD data preprocessing

For each OD pair ( i ,  j ) in bus, subway, and taxi systems, the time series of mobility can be represented as:

Since the passenger flow at a single OD pair exhibits randomness, f i , j experiences significant fluctuations over time, as shown in Supplementary Fig. 2 . To better illustrate the impact of COVID-19 on public transport, we apply Kalman filtering to smooth the data.

Kalman filtering is a recursive algorithm utilized for estimating the state of a dynamic system by combining noisy measurements with a mathematical model of the system (Chui et al., 2017 ). When applied to noisy time series data, the Kalman filter can provide a smoothed version of the data by recursively estimating the underlying state of the system that generated the data. The filter accounts for uncertainties in both the measurements and the system’s model, rendering it particularly effective at reducing noise while preserving the true signal. Upon applying Kalman filtering, the resulting sequence for the OD station pair ( i ,  j ) is denoted as:

The mobility between different OD pairs exhibits significant variation, with a maximum difference spanning several orders of magnitude. To uncover the general patterns impact of large-scale infectious diseases on passenger flow, it is essential to perform normalization on the time series \({{{{{\bf{f}}}}}^{{\prime} }}_{i,j}\) . Specifically, for any OD pair ( i ,  j ), the passenger flow on December 8, 2022 is considered a normal level unaffected by the epidemic. The normalized time series is calculated by:

Identifying mobility patterns via clustering algorithm

Measuring of mobility changes.

The features of changes in mobility are developed to facilitate description and subsequent clustering analysis. Drawing inspiration from the resilience in the fields of biology and engineering (Schwarz, 2018 ; Standish et al., 2014 ; Tabatabaei et al., 2018 ), we develop a set of features to comprehensively quantify the decline and recovery patterns of travel before, during, and after a large-scale infectious disease outbreak. These features can characterize the temporal changes in the response of mobility behavior to the pandemic, which reflects the interplay between human behavior, public policy, and the built environment, and provides insights into the dynamics of urban mobility under extraordinary circumstances.

Firstly, for any OD time series \({{{{{\bf{f}}}}}^{{\prime}{\prime}}}_{i,j}\) , we can identify several basic features, including the maximum value R top before the passenger flow decreases, the minimum value R bottom of passenger flow, the final passenger flow level R end , and their corresponding time moments t 0 , t 1 , t 2 , t 3 . The definition and significance of the features are as follows.

Declining speed Γ: The speed at which trips decrease serves as a measure of how quickly urban populations adapt their mobility behavior in response to public health crises. It is calculated by:

Declining amplitude Λ: The maximum amplitude of the decrease in trips directly mirrors the influence of the epidemic on mobility behavior.

Trough duration Π: The third indicator of mobility changes we define pertains to the trough duration of mobility. This indicator captures the persistence of the reduced trips during the COVID-19 pandemic, reflecting the extent to which the usual travels are disrupted.

Recovery speed Θ: When infected individuals gradually recover and the overall panic levels subside, this metric captures the speed at which urban mobility bounces back to normalcy. The determination of the indicator is achieved through:

Recovery amplitude ϒ: The amplitude of mobility recovery highlights the degree to which trips rebound after the initial impact of the pandemic. As infected citizens gradually recover and no longer reject traveling, this indicator provides insights into the interplay between public health measures, human behavior, and the ability of societies to regain their normal functioning after experiencing a large-scale infectious disease outbreak.

Total impact Δ: We finally introduce a comprehensive metric that captures the decline and recovery process of the pandemic’s impact on mobility. This total impact indicator effectively represents the degree to which the pandemic affects mobility, calculated as the area between the curve and the usual level of trips using the formula:

A lower value indicates a more substantial influence of the epidemic on mobility.

Therefore, we extract features of mobility change from each OD array ( i ,  j ):

K-means++ clustering

In order to identify change patterns of OD mobility during the pandemic, we perform the K-means++ clustering algorithm on the OD dataset. K -means++ clustering is an unsupervised machine learning algorithm used for partitioning a given dataset into a specified number ( K ) of clusters.

K-means++ clustering aims to minimize the within-cluster sum of squared (WCSS) distances from the data points to their respective cluster centroids by iteratively updating the centroids and assigning the data points. Specifically, the input of the K-means++ clustering algorithm is

where each OD time series is represented by the features.

The algorithm first needs to initialize the cluster center. A centroid c 1 is randomly chosen from X . For each x i , j   ∈   X , compute the squared distance to its nearest centroid:

where \({{{\mathcal{C}}}}={\left\{{c}_{p}\right\}}_{p = 1,...,k-1}\) represents the set of centroids chosen so far. Then, the next centroid c k is determined from the data points with probability proportional to \(D{({x}_{i})}^{2}\) :

Repeat the two steps until we have taken K centers altogether.

After selecting the K initial centroids, the algorithm proceeds with the standard iterative updates of centroids. Step 1 is assigning x i , j to the closest centroid using ( 13 ). Suppose that at iteration n , the dataset is divided into K clusters:

In step 2, calculate new centroid positions for each cluster by minimizing the WCSS distance:

The two steps are repeated until convergence is reached, i.e., when the assignment of samples to clusters no longer changes.

The elbow method is utilized to determine the optimal number of clusters, denoted as K , in K-means clustering (Bholowalia and Kumar, 2014 ). The elbow method involves plotting the variance explained or the sum of squared distances (SSE) of the data points to their cluster centroids against different values of K . The elbow method suggests selecting the value of K at the “elbow" or bend in the SSE plot (Supplementary Fig. 3a) . This point represents the optimal trade-off between minimizing SSE and avoiding excessive complexity in the clustering model.

After establishing a preliminary range for K using the elbow method, Silhouette Analysis is employed to finalize the value of K (Lleti et al., 2004 ). For each K -value, silhouette coefficient is computed for each data point, which measures the similarity between each data point and the cluster to which it is assigned, with values ranging from –1 to 1. Subsequently, the average silhouette coefficient is calculated for all data points at each K -value. This average value serves as an indicator of cluster cohesion and separation, with higher values indicating better clustering results. As depicted in Supplementary Fig. 3b , the analysis determined that the optimal value for K  = 4.

Finally, we derive K clusters of OD mobility, as shown in Fig. 3 a. The labels for each OD pair have been obtained. Furthermore, the OD pairs can be associated with spatial information, which leads to subsequent analysis.

Spatial attractiveness of urban land use

Our goal is to illuminate the variation in mobility for OD pairs during a widespread infectious disease outbreak. Solely analyzing the geographical locations of the starting and ending stations of travel, however, is inadequate. As a result, we endeavor to extract travel purposes by integrating the urban land use map.

Lands in close proximity to bus stations, subway stations, and taxi pick-up and drop-off points provide insight into passengers’ potential travel motivation. For example, when a bus station is located in a residual land, there is a high probability that passengers disembarking at this station intend to commute to their residences. Accordingly, a ULU feature vector is established for each origin and destination, which is represented as:

where r  = 1, 2, . . . ,  R represents urban land use categories. s i , r is defined as the spatial attractiveness of land use category r to the citizens boarding or disembarking from the station or urban regions i ( i  = 1, 2, . . . ,  M ).

For the bus stations and subway stations, a common method (Xing et al., 2020 ) is to use the number of lands to determine s i , r :

where z i , r denotes the number of lands belonging to category r within a certain distance range of the stations. Taking into account the varying importance of stations, the distance range for bus stations is set at 200 meters, while the distance range for subway stations is set at 500 meters, as presented in Fig. 5 b.

As mentioned in “Origin-destination data of urban mobility”, the taxi OD data represents the mobility between 11,362 distinct regions within the city. These urban regions have been delineated with nearly homogeneous land use attributes. Therefore, s i , r can be derived directly:

For an urban region with the land use attribute r , the s i , r value corresponds to 1, while the remaining values of the land feature vector are set to 0.

As each urban land use category can be mapped to an activity, identifying the most probable land use category corresponds to determining the most likely activity to be undertaken. To further analyze the mobility behavior from the origin i to the destination j , we compute the travel transition matrix:

The elements in the matrix S i , j represent the likelihood of passengers transitioning from a land use category at the origin station (region) to a ULU category at the destination station (region). For instance, s i ,1 s j ,2 in the bus system corresponds to passengers departing from land use category 1 near bus station i and arriving at land use category 2 near bus station j . From this, we can infer the passengers’ mobility behavior and purposes. The travel transition matrix for the subway and taxi follows the same principle.

We combine the K clusters of change patterns with the spatial attractiveness of urban land use. For each cluster, we sum the corresponding S i , j values to obtain a total transition matrix. Considering daily frequency changes in passenger flow, the passenger flow from station i to station j is approximately equal to the passenger flow from station j to station i on a daily timescale. Based on the symmetry of OD passenger flow, we add the lower and upper triangular parts of the total transition matrix. Finally, we identify the top values in the matrix that correspond to the primary OD land pairs and their corresponding proportions of mobility behavior within each cluster.

Dynamic OD mobility model

We develop a dynamic model to capture the fine-grained urban mobility changes. The decline in trips can be attributed to two factors: physiological infection with viruses and emotional influence. Inspired by the classic epidemic transmission model SIR (Cooper et al., 2020 ; Keeling and Eames, 2005 ), an OD passenger flow model is proposed, comprehensively considering the spread of the COVID-19 pandemic and the willingness to travel via public transportation, as shown in Fig. 4 .

The model incorporates pandemic transmission and recovery rates, willingness factors of mobility behavior, urban land use map, and pre-pandemic OD passenger flow as input. It outputs the changes in trips for every OD pair during the COVID-19 pandemic with high spatial resolution.

Physiological infection module

Prior to the outbreak of large-scale infectious diseases, we assume that the daily number of population from station i to station j is W i , j . Following the epidemic outbreak, all passengers can be categorized into three states: usual (U), infected (I) by the virus or panic, and recovered (R). The relationship among passengers associated with the three states is shown in the subsequent ( 22 ).

Specifically, initially uninfected people may become infected with the virus through contact with infected individuals. Eventually, the infected people transition to a recovery state. For each citizen, the probability state transition process of physiological infection is as follows:

where U i , j , t ,  I i , j , t ,  R i , j , t denote passenger numbers of usual state, infected state, and recovery state, respectively on the t day. β and γ represent the transmission and recovery rates, respectively, which are inherent attributes of the pandemic spread.

Willingness influence module

During the pandemic, urban residents tend to reduce their travel activities to mitigate the risk of infection. This tendency is particularly pronounced when there is a higher reported or perceived number of infections and potential cases through news outlets, social media, or personal observations, leading to increased levels of anxiety. During the recovery phase, mobility gradually rebounds over time, and confidence in travel is steadily restored. Furthermore, the decline and recovery of different travel behaviors exhibit heterogeneity. From the clustering results in the section “Population behavior behind mobility patterns”, it is evident that the decrease and recovery of trips are associated with travel purposes.

Willingness factors are proposed to model the willingness influence. It is assumed that the willingness factors of travel from ULU type r to ULU type v follow normal distributions:

where r ,  v  = 1, 2, , . . . ,  R . \({\mu }_{r,v},{\sigma }_{r,v}^{2}\) and \({{\mu }^{{\prime} }}_{r,v},{\sigma }_{r,v}^{{\prime} 2}\) are distribution parameters (mean and variance) of willingness factors for transmission and recovery, respectively.

In terms of travel mode choices, individuals tend to opt for modes associated with lower exposure to viral risks. Consequently, the willingness factor for transmission and recovery in the context of subway, bus, and taxi are denoted as \({\mu }_{q},{{\mu }^{{\prime} }}_{q}(q=1,2,3)\) .

The probability state transition process of emotional influence on travel is as follows:

Put it together

For an OD pair ( i ,  j ) of transport mode q , the travel transition matrix S i , j decomposes travel for a given OD pair into combinations of various trip purposes. Consequently, it enables the synthesis of willingness factors, thereby generating the willingness factor for that specific OD pair. The willingness factors can be estimated by linearly stacking the sampled willingness factors for travel purposes:

The mobility change for OD pair ( i ,  j ) can be determined through the following equation.

The time series of the passenger flow from origin i to destination j is:

W i , j is determined by the trips of OD ( i ,  j ) prior to the pandemic outbreak. α i , j and \({\alpha }_{i,j}^{{\prime} }\) denote willingness factors for transmission and recovery, respectively. These willingness factors depend on the ULU feature arrays of the origin i and the destination j , accounting for the diversity of OD passenger flow. The initial values of U i , j , t ,  I i , j , t ,  R i , j , t are determined by the initial infection rate in the city.

By obtaining the changes in passenger flow at all OD pairs, we can also derive the overall change in trips under the impact of the pandemic. This model characterizes the response of an entire city’s public transport system to the pandemic and the shifts in mobility behavior.

Model validation

The alterations in actual OD passenger flow within the Shenzhen bus, subway, and taxi systems during the COVID-19 pandemic serve to verify the effectiveness of the dynamic model. Research (Cai et al., 2022 ; Leung et al., 2023 ; Ren et al., 2022 ; Ribeiro Xavier et al., 2022 ) indicates that the transmission rate of SARS-CoV-2 ranges from approximately 0.2 to 0.4, while the recovery rate spans from approximately 0.05 to 0.15. These values are determined by the inherent transmission characteristics of COVID-19. This paper primarily concentrates on modeling the variations in mobility for different OD stations. Without loss of generality, we employ β  = 0.3,  γ  = 0.1.

When calculating the willingness factor for recovery, we consider only the main ULU pairs depicted in Fig 3 b. Consequently, the parameter estimation target constitutes the mean values and standard deviations of willingness factors for OD ULU pairs. Based on the maximum likelihood estimation (MLE) method, we find the optimal parameter values of the willingness factor distributions, enabling the predictive changes in the mobility model to approximate the actual trend. Specifically, we employed the grid search method to identify the values of \({\alpha }_{r,v},{{\alpha }^{{\prime} }}_{r,v}\) that correspond to the minimum root mean squared error (RMSE) between the true trips and predicted trips. These values of \({\alpha }_{r,v},{{\alpha }^{{\prime} }}_{r,v}\) are treated as sampling data for the distribution of willingness factors to be estimated. Subsequently, we determined the parameter values that maximize the probability of sampling data occurrence, resulting in the optimal mean values and standard deviations of the empirical factors.

The optimization results show that the values of willingness factors for transmission are similar for all OD station pairs, whereas the willingness factors for recovery primarily contribute to the diversity observed in mobility behavior, as displayed in Supplementary Fig. 1 . Under the optimal parameters, we sample and derive 100 sets of dynamic model parameters. The predictive trips in Shenzhen during the COVID-19 pandemic are shown in Fig. 4 .

Besides, an analysis is conducted to evaluate the robustness of the UIR model. The values of β and γ vary by approximately ± 30% and observe the model performance while keeping other parameters constant. This analysis focused on calculating the average root mean square error (RMSE) between the actual and predicted values of passenger flow across various OD pairs in the city. The RMSE for each trip series provided a daily prediction error. As shown in Supplementary Fig. 4 , our findings indicate that for every 1% deviation in β , the prediction error increased by approximately 0.61%, and for every 1% deviation in γ , the prediction error increased by around 0.77%. These results suggest a certain level of sensitivity of the model to these parameters.

Data availability

Urban land use categories are available at https://data-starcloud.pcl.ac.cn/zh . Points of interest (POIs) data can be openly accessed via the APIs of Gaode Maps ( https://lbs.amap.com/ ) and Baidu Maps ( https://lbsyun.baidu.com/ ). Raw origin-destination (OD) data of buses and taxis are available with the permission of Shenzhen Bus Group Co., Ltd. ( https://en.szbus.com.cn/ ) upon request for academic cooperation. The de-identified data are available at https://github.com/trainandtest666/OD-Data-in-Public-Transport-System.git .

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Acknowledgements

This paper was supported by the National Key R&D Program of China No. 2022YFC3300703, the Natural Science Foundation of China under Grant No. 62371269. Guangdong Innovative and Entrepreneurial Research Team Program No. 2021ZT09L197, Shenzhen 2022 Stabilization Support Program No. WDZC20220811103500001, Tsinghua Shenzhen International Graduate School Cross-disciplinary Research and Innovation Fund Research Plan No. JC20220011, the Project from Science and Technology Innovation Committee of Shenzhen (Grant No. KCXST20221021111201002), the Major Key Project of PCL (Peng Cheng Laboratory) under Grants PCL2023A09, and Meituan.

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Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

Baining Zhao, Xuzhe Wang, Tianyu Zhang, Fanhang Man, Yang Li & Xinlei Chen

Peng Cheng Laboratory, Shenzhen, China

Baining Zhao, Tao Sun & Xinlei Chen

Institute of artificial intelligence, Beihang University, Beijing, China

Department of Electronic Engineering, Tsinghua University, Beijing, China

Fengli Xu & Yong Li

Department of Mathematics, Shenzhen University, Shenzhen, China

Erbing Chen

RISC-V International Open Source Laboratory, Shenzhen, China

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Fellow researchers and authors contributed as follows: Conceptualization: BZ, RS, YL, & XC; methodology: XW, BZ, & TS; formal analysis: RS, FX, & XC; writing/original draft preparation: BZ, XW, FM, EC, & TZ; writing/review and editing: TS, XW, XC, BZ, & YL; BZ, XW, & TS contributed equally to this work and share first authorship. Correspondence to XC.

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Zhao, B., Wang, X., Zhang, T. et al. Estimating and modeling spontaneous mobility changes during the COVID-19 pandemic without stay-at-home orders. Humanit Soc Sci Commun 11 , 591 (2024). https://doi.org/10.1057/s41599-024-03068-4

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Xi’s European tour: where is Chinese leader going and what are visit’s aims?

Emmanuel Macron and Viktor Orbán among leaders Xi is meeting, with several key issues on the table

China’s president, Xi Jinping , has begun a three-country tour of Europe – his first state visit to the continent in five years – at a time when China-EU ties are under strain from trade disputes and Russia’s war on Ukraine.

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On Wednesday, Xi will travel to Belgrade for talks with Serbia’s president, Aleksandar Vučić, and on Thursday he will go to Budapest where he will meet Hungary’s prime minister, Viktor Orbán . Both countries are pro-Russia and big recipients of Chinese investment.

What is the purpose of his visit?

Officially, Xi’s visit to Paris is to mark 60 years since diplomatic relations were established between France and China: France was the first western country to formally recognise the People’s Republic of China, on 27 January 1964.

His visit to Belgrade coincides with the 25th anniversary of the bombing of the Chinese embassy in Serbia , when three people died after a US strike accidentally hit the compound during Nato’s air campaign against Serb forces occupying Kosovo.

Analysts have said that in his meetings with Macron and von der Leyen, away from the ceremonies, Xi will mainly be aiming to lobby against the EU’s anti-subsidy investigations, particularly on electric vehicles, and on stabilising diplomatic relationships.

In Serbia , where China is the biggest single source of inward investment, he will hope to play up Beijing’s anti-US, anti-Nato agenda – one reason why China has maintained its support for Russia since its invasion of Ukraine.

In Hungary, Xi will underline the close economic and diplomatic ties between the two countries, including in security cooperation, and discuss progress on China’s belt and road initiative , which includes a high-speed Budapest-Belgrade rail link.

Hungary, a vocal supporter of China that has blocked some EU motions criticising Beijing on human rights, has Huawei’s largest base outside China and will soon host the carmaker BYD’s first European factory.

What big issues will be on the table?

Overshadowing the visit are EU concerns over China’s support for Russia two years into war against Ukraine, and Beijing’s concerns over the bloc’s economic security agenda, including the threat of heavy tariffs on Chinese imports.

Macron and von der Leyen will call on China to stop exports to Russia of “dual-use” and other technologies aiding Moscow war effort. Beijing claims to be neutral in the conflict but China-Russia trade has helped offset western sanctions on Moscow.

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Beijing, for its part, aims to head off the threat of European tariffs on Chinese EVs after an EU investigation into state support for the industry in China. Studies put China’s subsidies at between three and nine times those of other major economies.

Human rights groups have said the issues of Tibet and Xinjiang , where the UN has said China may have committed crimes against humanity by placing up to a million ethnic Uyghur Muslims in re-education camps, must be raised.

What is the outcome likely to be?

Most analysts doubt that the EU and its messages on the looming trade dispute and Chinese support for Russia over Ukraine will make much headway with Xi, whose visit seems designed to exploit the bloc’s internal differences.

China’s economy is facing some difficulties and the US is increasingly reticent about opening up to Chinese companies, meaning the EU could have some leverage, but its 27 members are not fully aligned on China policy, undermining their influence.

Macron wants a more aggressive EU stance on subsidies and has warned that the bloc risks falling behind without one, but other leaders, such as Germany’s chancellor, Olaf Scholz, stress the importance of the Chinese market for their own exporters.

Overall, said Janka Oertel, of the European Council on Foreign Relations, Xi’s Paris visit is “unlikely to have a significant impact on Chinese behaviour”. And Shen Dingli, a Shanghai-based analyst, said the Belgrade and Budapest stops were part of China’s efforts to deepen divisions within the west.

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• Top 10 Shenzhen Attractions

Although Shenzhen is a young metroplis, it will give you a big suprise when you put your first step on this energetic city.

As a coastal city, Shenzhen is a great place for resort and vacation. It has long, soft and beautiful sand beach, such as the Dameishan and Xiaomeishan . There are not many ancient relics here, but the wise Shenzhen people have built up many modern attractions, such as the famous Window of the World , Splendid China , Happy Valley , etc. These places are suitable for all ranges of people to visit.

The locals also created their own spectacular culture, such as Dragon Dancing Show , Ethnic Arts Show, Lychee Festival, etc.

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No.1: Splendid China Folk Village

Type: Theme Parks, Water & Amusement Parks, Performances

Recommended Lenth of visiting: About 3 Hours

Splendid China Folk Village, a theme park located in Shenzhen, consists of two areas including Splendid China Miniature Park and China Folk Culture Village.

“One step to learn China history, one day to visit all in China”. This is the slogan of the Splendid China Miniature Park, which is the largest miniature park in the world. It houses 82 reproductions of China’s iconic places. These miniatures are divided into three kinds: beautiful scenery, grand architecture, historical sites and traditional dwellings. As there are more than 50,000 small vivid ceramic human or animal figurines, it is also renowned as “Lilliput of Shenzhen”.

In China Folk Culture Village, there are 27 ethnic villages built with the proportion of 1:1. It is the first large cultural tourism zone which combines art, folk culture and architecture of ethnic groups in China. There are daily performances of ethnic folk culture, handcrafts arts. During festivals, China Folk Culture Village becomes the “party world”. Here, travelers can experience the authentic ethnic culture of China comprehensively. Know more about Splendid China Folk Village

>> 3 Days Classic Shenzhen Tour

No.2: Dafen Oil Painting Village

Type: Art Gallery, Painting Stores, Painting Show

Recommended Lenth of visiting: About 1~2 Hours

Located in the Longgang District of Shenzhen Province, Dafen Oil Painting Village is a Chinese “urban village” working on the replicas of world-famous paintings, from Van Gogh's sunflowers to to enormous, gilt-framed scenes of mounted cavalry or galleons at sea rendered in lavish detail. It is reputed as “The World’s Art Factory”. In its heyday, Dafen produced 60 percent of all new oil paintings available worldwide. This is a folksy urban village of narrow lanes and alleys, which gives you pleasure by just visiting itself. The hundreds of art studios and stores producing reproductions of oil paintings by well-known artists are worth visit where you will know how art is produced in an industrial age. There are also more conventional galleries, generally showcasing the work of—and often owned by—a single artist. Surrounding these studios and galleries in Dafen are art supply stores and a handful of whimsical cafes, some of which offer painting classes to visitors. At the village’s eastern edge is the Dafen Museum where you will find more modern and contemporary art from China.

>> 2 Days Shenzhen Short Stay Tour with All Essences

No.3: Shenzhen Museum

Type: Museum, Comprehensive Museum

Recommended Lenth of visiting: About 2 hours

Shenzhen Museum is a comprehensive museum located in the south of Lianhuashan Park, and is in the downtown area of Shenzhen City. This museum has four parts which separately record the ancient times and modern times of Shenzhen, guerrilla warfare in Shenzhen and China’s reform and opening up in Shenzhen. In this museum, you will have a general view about the history of Shenzhen. As Shenzhen is one of the first cities that implement the reform and opening up policy, you will have a vivid understanding of how does a fishing village become a first-tier city within 40 years and it is advised to learn about a modern China from this museum. In addition, there is an exhibition hall of wildlife specimen which was donated by a famous American - Kenneth·E·Behring, in which you could explore a world of wildlife. The museum also host some other exhibitions from time to time.

>> 2 Days Shenzhen Short Stay Tour with All Essences >> 4 Days Guangzhou Shenzhen Explorer Tour by Bullet Train

No.4: Window of the World Shenzhen

Type: Theme Parks, Amusement Parks, Performances

Recommended length of visiting: About 3 hours

Covering an area of 480,000 square meters, Window of the World in Shenzhen is a large-scale theme park designed to show the essence of world's culture. In the park, you will see many replicas of landmarks in the world. It is a Miniature park which combines history relics, nature wonders and culture of the world together. In this park, you have chance to many great world famous attractions, such as the Mahamuni Pagoda of Mandalay, Angkor Wat of Cambodia, Eiffel Tower, London Tower, Roman Colosseum, Sydney Opera House, etc. Meanwhile, there are excited amusement projects and splendid performances, which attract many tourists. You can’t miss this sea of joy in Shenzhen! Know more about Window of the World>>

No.5: Ping An Finance Centre

Type: Observation Decks & Towers, Modern Buildings & Landmarks

Ping An International Finance Centre is a 115-storey skyscraper in the center of Futian CBD of Shenzhen, rising like a glass pencil. It was completed in 2017, becoming the tallest building in Shenzhen, the 2nd tallest building in China and the 4th tallest building in the world. It also shares the record (with the Shanghai Tower) of having the highest observation deck in a building at 562 m. The building contains office, hotel and retail spaces, a conference centre and a high-end shopping mall. In a clear day, you will have a suitably jaw-dropping views from the Free Sky Observation Deck. You could also explore a bird’s eye view of the whole city on Diwang Mansion with the height of 384 meters which is cheaper than Ping An Finance Center. The latter one also equipped with telescope to overlook Hong Kong.

>> 3 Days Shenzhen Business Tour (Customizable)

No.6: Lychee Park

Type: Park and Nature

Lychee Park is is nestled in the middle of Shenzhen. It has the name of “Lychee Park” because of over 500 lychee trees in it. In the park, you could not only see natural beauty, but also some beautiful architectures. When big-city fatigue sets in, you can seek refuge in this spacious inner-city park. It may still get busy but here the mood is created by musicians, elderly taichi practitioners, couples crossing faux-ancient bridges and a small lake. It is also very nice to jog, run or walk around.

No.7: OCT Loft Creative Culture Park

Type: Culture Park

OCT Loft Creative Culture Park covers an area of about 150000 square meters, with a construction area of about 200000 square meters, is divided into north and south sides. Filled with exhibition spaces, outdoor installations, design shops, and open-air cafes, this abandoned-factory-turned-cultural space is a great way to get a feel for the city’s emerging artsy tenor. You can see exhibitions of all kinds of art including design work, photography, paintings, and so on. Some famous artists also have their studios in this park. In addition, major festivals take place here throughout the year, including Design Week in April; the Bi-City Biennale of Urbanism/Architecture in December; and the Oct-Loft Jazz Festival in October.

No.8: OCT Harbour

Type: Resort, Water & Amusement Parks, Theme Parks

Recommended Lenth of visiting: About 3 hours

OCT Harbour, originally called OCT Bay, is a large retail and entertainment complex in Nanshan District of Shenzhen close to the well-known Window of the World theme park and Splendid China Folk Village. It covers an area of roughly 1.25 square kilometers and features a man-made lake and canals, hotels, restaurants and a shopping mall. This place combines both modern architectures and traditional culture, which gives a new definition to fashion. Here you could watch splendid show of water and light every night. If you go there with your kids, then your kids could experience various careers in the Small Mailu City, or you both could watch those special sea life in the aquarium. It’s very suitable for a family tour.

No.9: Coco Park

Type: Mall, Shopping Center, Dinning, Amusement

Located in the centre of the city, Futian COCO Park is not only a popular shopping mall but also a landmark of the area. The mall has 5 levels, covering 85,000 square meters, with a big outdoor square in the centre. There are five major shopping sections in COCO Park: jeans, sports & digital, undergarment, household, and international fashion brands, and you can buy almost you want here. It is much more than about shopping because there are also a lot of restaurants, bars and entertainment in it. Near the Pingan Finance International Center, Coco Park can be easily found by you.

No.10: Lianhuashan Park

Recommended Lenth of visiting: About 1~3 hours

Lianhuashan Park is located in the downtown area of Shenzhen city, so it’s convenient to arrive at. The Lianhua Mountain is only 100 meters, which can be climbed up easily. After climbing up to the top of the mountain, there is a large square where you could overlook Shenzhen’s central business district. In the square, you could see a big statue of Deng Xiaoping, a Chinese President who started the Reform and Opening Up of China. In the north side of the square, you could learn about the development history of Shenzhen city in the Exhibition Hall of City Planning. If you go there during festivals and holidays, you could see locals flying kites in the park’s grasslands square. The park is a relaxing place to visit for Shenzhen people after a week’s busy works.

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China Southern launches long flights from Shenzhen to Mexico City

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Starting May 11th, China Southern Airlines will offer round-trip flights between the industrial hub of Shenzhen and Mexico City. Aviation experts note that this route will be among the longest flights from China to any global destination. CGTN’s Franc Contreras provides more details from Mexico City.

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