Taxi Big Data, The Inspiration For Urban Planning 19 January, 2018 SuperMap Software Press Releases

SuperMap has launched the GIS platform software - SuperMap GIS 9D at GIS Software Technology Conference 2017 to meet the demand for big data era.

Our team will post a series of articles about SuperMap GIS 9D to help you get a whole picture about its capabilities and achievements in aspects of technol-ogy, products, data and application. So, let’s get started!

You wake up in the morning, turn the smartphone on, switch on the wifi, and start your day. You might go to work by ordering a taxi with your phone, or taking public transportation, or even by a shared bike. With no doubt, most of these historical records are stored and taken as a resource for the urban big data.

In this post, we are going to talk about the taxi big data, and how it con-tributes to the urban planning.

We have built a population mobility - related analytical platform, based on SuperMap 9D GIS series.

The platform is basically composed of three main modules: the data storage - Elasticsearch, the data analysis and publication server - SuperMap iServer, and the client (frontend) development platform - SuperMap iClient.

The planner can work out the regional transfer status and major city & area population mobility by analyzing the taxi data of population and occupation distribution on varied space and time scale.

 1.Work & Live

The work & live list is initially formed with cellular signaling data, and gradually completed and modified as the change of bus card swiping data, taxi data and bandwidth data afterwards.

You can have a knowledge of the population and occupation distribution of this area, based on which you can evaluate the work & live sta-tus quo by referring to the relevant index.

We can easily find that the crowds are mainly gathering around the commercial area in the morning and evening rush hour, as the picture showing below.



2. Commuting Status

We commute almost everyday, no matter you work or you’re a student, and not many people like to travel a long way to get to the destination.

The picture below shows the commuting time in major cities of China.


You can do analysis in terms of OD, commuting time and commuting distance in the morning & evening rush hour on the platform.


OD analysis: O is for ‘origin’, and D is for ‘destination’. Taking the case below as an example, you can infer the work location of the people from the same community (origin) by analyzing the fading spots (destination) in the picture.

Commuting time: using the work & live list as databases to work out every objects’ commuting time according to the card swiping records, and get the mean time of commute.

Commuting time: working similarly to commuting time, while the mean distance of commute are calculated with the GPS data.

The theme map that generated from regional commuting time and distance can be a convincible reference for the urban planning and optimi-zation.

3. Population Mobility analysis

The population mobility status varies with particular period of time.

For example, people tend to gather around business area in morning rush hour, going to restaurants at nighttime and entertainment spots e.g. shopping mall and cinema at weekend.
And all the data comes from the taxi drop-off spots analysis.

4. Traffic Congestion Analysis

The traffic congestion status can be tracked by analysing the taxi speed that calculated with the GPS positioning information per sec.

The transportation department can do relevant analysis on the real-time visualized big data and come up with solutions to ease the traffic pressure.


The industrial structure has been changing over the years, increasing major cities keep focusing on the modern services industry develop-ment. On this case, taxi data is certainly a significant source of urban planning.

Basing on the taxi big data analysis , the planners can rationalize the functional area arrangement, optimize traffic field and even ease the tidal effects on urban development.

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