Data ShowcaseBackground Introduction
Open Data Portal (formerly known as PSI Portal) makes open data available in machine-readable format for re-use for commercial or non-commercial purposes free-of-charge, with a view to facilitating the development of new solutions and innovations, as well as academic research and analysis.
The following datasets are available from the Open Data Platform and the Common Spatial Data Infrastructure (CSDI):
Background Introduction
Esri China (Hong Kong) Limited is a home-grown IT company specializing in Geographic Information System (GIS) and mapping solutions to serve Hong Kong and Macao customers since 1997. We aim to make our customers successful through the applications of innovative GIS and mapping technologies. We also support building Hong Kong and Macao as leading smart cities by integrating GIS seamlessly with other smart technologies as the backbone of smart city infrastructure. We deliver comprehensive GIS solutions, GIS & IT consulting and implementation lifecycle services as well as professional training to help organizations to enhance their business and competitiveness.
Datasets Description
The dataset can only be searched in English via the Open Geospatial Data HK（https://opendata.esrichina.hk/）. The below dataset is cited as an example for your reference only.
Transport
Road Network Data of Hong Kong
3D Pedestrian Network in Hong Kong
Building
LandsD 3D-BIT00 Models
Buildings in Hong Kong
Population
Hong Kong Population Density by 18 districts in 2021
Hong Kong Population Distribution by Age by Small Tertiary Planning Unit Group in 2016
Town Planning
Hong Kong Outline Zoning Plans Land Use Zonings
Natural Landscape
LiDAR Data in Hong Kong
Hong Kong Digital Terrain Model from 2020 LiDAR Survey (5m grid)
Slope of Hong Kong
Environmental
Old and Valuable Trees and Stonewall Trees in Hong Kong
Suggested Description
Utilizing GIS technology and 3D modeling, design the future urban landscapes and architectural plans for Hong Kong or Shanghai to achieve livable and sustainable Sponge City environments
Utilize geospatial data for the revitalization and development planning of the old districts in Hong Kong and Shanghai
Climate change in the dual cities of Hong Kong and Shanghai: Enhancing resilience and spatial governance through new strategies
Intelligent charging solutions: Innovative solutions to drive the development of electric vehicles
Based on GIS analysis of population, transportation, and infrastructure data in Hong Kong or Shanghai, propose urban expansion and land use planning strategies to address population growth and urban development needs
Analyze the cultural heritage and urban history of Hong Kong or Shanghai based on GIS technology and historical data, and propose plans for the protection and utilization of urban cultural assets
The Data Post platform provides free one-stop search access to public data catalogs openly available from local governments across China (excluding Hong Kong, Macau, and Taiwan). It does not offer direct access to raw data. Instead, it provides links to the official public data catalog platforms of each locality, helping users reach their target open data platform directly and obtain the data they need in accordance with each platform's specifications.
If you have keywords in mind, the platform supports both keyword search and advanced search with rich metadata filters — you can narrow results by region, government department, time period, and theme to quickly locate your target data. If you are unsure of the exact data name, you can browse all open data by industry or by province/city through the corresponding entry points.
What if you cannot find what you need? The team behind Data Post offers customised data services. Submit a request through the platform's "Data Request" portal — describe the name, content, coverage, and other details of the data you need along with your contact email, and the team will search, match, and locate the target data for you, providing the means to obtain it or even delivering it directly.
The Data Post platform currently provides one-stop search access to over 560,000 open data catalog entries from provinces and cities across China. Catalog information includes data name, providing department, description, format, and platform link. The platform supports filtering across 14 themes — including transportation, health, economy and commerce, education and technology, and resources and environment — as well as filtering by government departments within each province or city.
Shanghai Public Data Open Platform is an information management system for data open subjects and data utilization subjects, realizing the whole-process management of public data opening, guaranteeing the compliance, security and controllability of public data and the opening process, and promoting the development of the digital economy. Utilizing the Shanghai Big Data Resource Platform, the Data Open Portal provides data utilization subjects (natural persons and legal persons) with public data that are machine-readable, utilizable and original.
The platform has currently opened 53 data departments, 165 data-opening organizations, 6,402 datasets (including 2,504 data interfaces), 83 data applications, 54,148 data items, and a total of 2,050,444,231 records, covering the fields of economic construction, resources and environment, education and technology, road transportation, social development, and public security.
Shanghai Electric Vehicle Public Data Collecting, Monitoring and Research Center is the only third-party public data platform for electric vehicles in Shanghai, formally established in 2014. Based on national standards, the data center samples at a frequency of once every 10 seconds, covering 124 data fields in total — 44 static and 80 real-time dynamic. By the end of April 2026, the platform has cumulatively collected operating data from more than 2 million electric vehicles across Shanghai.
The dataset aggregates electric-vehicle traffic data for a 7 km road segment, fitted from real EV operating data. It captures volume and speed characteristics during morning and evening peaks as well as off-peak hours, and includes vehicle diversion behaviour at an intersection. In a single day, roughly 7,000 EVs (not the full motor-vehicle volume) pass through. The set covers two weekdays — Monday and Wednesday — and includes fields such as vehicle ID, timestamp, coordinates and speed at each ping.
The dataset randomly selects 15 battery-electric vehicles operating in the public sector across 52 consecutive weeks (1 day per week). Fields include run mode, speed, state of charge (SOC), total current, total voltage, battery temperature extremes and more. The data can be used to explore EV charging performance — charging power, speed, remaining capacity — as well as discharge performance, such as acceleration and remaining range.
The Shanghai Hongqiao transportation hub area is selected as the research object. Inbound and outbound traffic-flow information for new-energy vehicles in this area is collected across different time periods. The dataset covers vehicle flow data segmented into 48 hours over two days — one weekday and one weekend day — in 2024, and involves 100,000 new-energy passenger vehicles and 3,000 new-energy logistics vehicles. It contains a grid information table and a vehicle origin–destination (OD) spatial flow table.
Grid Information Table
| Field | Description |
|---|---|
| grid_id | Grid ID; each grid is 1 km × 1 km |
| geometry | Grid geometry vector object (Polygon), for visualization |
| centroid_x | Longitude of grid centroid, 6 decimal digits, WGS84 |
| centroid_y | Latitude of grid centroid, 6 decimal digits, WGS84 |
Vehicle Origin–Destination Spatial Flow Table
| Field | Description |
|---|---|
| date | Date: Friday or Saturday |
| hour | Hour: 0–23 |
| vehicle_type | Vehicle type: passenger vehicle or logistics vehicle |
| depart_grid_id | Departure grid ID |
| arrive_grid_id | Arrival grid ID (a vehicle is counted as arrived if it stays in the grid for 15 minutes or longer) |
| vehicle_cnt | Vehicle count |
The dataset reflects the transportation connections between different regions of Shanghai and the Hongqiao area, supporting research into the potential layout and operation model of the low-altitude economy in Hongqiao. Contestants can combine this dataset with Shanghai's spatial development characteristics to explore a potential space–time operation plan for the city's low-altitude economy.
The study selects a typical Saturday in March of a given year as the time window and the core area of Anting Auto City as the spatial area. Within this spatio-temporal environment, a large-scale exhibition event is held, leading to road congestion. The dataset covers operating information for more than 9,000 new-energy vehicles, including driving, charging and parking. It also provides the most recent week's traffic flow, average speed and congestion coefficient for the area's main roads to describe overall traffic conditions.
Within the same space and a similar time range, traffic behaviour and abnormal traffic conditions caused by large exhibitions and similar events can be studied from a micro perspective using EV operating data — offering travel suggestions to vehicle owners, traffic-diversion plans to authorities, and parking guides to event organisers.
Traffic Data
Operational Data
Low-Altitude Economy Data
Spatiotemporal Data
Available to finalist teams only
Shanghai Topease Information Technology Co., Ltd. (referred to as "Topease"), a wholly-owned subsidiary of Donghao Lansheng (Group) Co., Ltd., was established in 2004 as a comprehensive solution provider for enterprise digital marketing. Adhering to clients' real needs and solving practical problems, Topease combines AI and cloud computing technology to reach the full potential of big data, helping to empower customers with global insights and improve their digital marketing ability.
Topease's global import-export trade database contains over 52 data sources, covering goods trade data from nearly 228 countries. For the competition, anonymised import-export statistical data from 50 countries and regions for the year 2025 has been extracted as the dataset.
Countries and regions covered:
Fields include: Import/Export Month, HS Code, HS Code Description, Trading Partner, Import/Export Amount, Import/Export Quantity
The core value of global import-export statistical data lies in deciphering the global economic pulse and industrial shifts through macro-level trade flow data. It effectively assists decision-makers in analysing supply-demand trends of specific industries in the global market, identifying opportunities for trade structural transformation, and evaluating the profound impacts of trade barriers and policy changes from a regional economic perspective.
Participating teams are encouraged to base their research on the statistical dimensions of various countries and regions, conducting in-depth exploration of core indicators such as trade flows, value fluctuations, and industrial distribution. We advocate for the comprehensive use of AI and big data modelling technologies to construct industry-specific "market prediction models" or "macro-economic analysis frameworks", with forward-looking intelligent explorations in fields such as trade trend forecasting and global supply chain resilience assessment.
Will be sent to all participating teams by the curator upon successful registration