SHARING CITY

Smart, future, shared lifestyle in the modern city: Guomao

Updates (https://www.beijingcitylab.com/epma2020):

Mar 12, share companies in each office building

Mar 3, collect more related cases for each group and share at Tsinghua Cloud

Feb 28, analysis maps for urban structure using space syntax

Feb 24, upload the compressed street view images (now only around 800MB, rather over 3GB like before)

Feb 23, release the version 2 of the SU model, online visualization for main big/open data in GeoHey (see below), the SU model for the Guomao Metro Station, upload a future city space report by my lab and Tencent (but in Chinese only now)

Feb 17, launch of the page

For better understanding our site and its surrounding area, we are sharing the data with a spatial extent of around 3km by 3km. The shared data include three types, very high resolution satellite images in different years from Google Earth, street view images from Baidu Map and urban big/open spatial data from my lab. If you have any question related to the data / studio, please feel free to contact with me via WeChat 龙瀛a1_b2 / Ying Long or Email ylong@tsinghua.edu.cn

Note that for better access the above SKP file in China, it is also available at Tsinghua Cloud (version 2).

We have collected five very high resolution satellite images in five time points spanning from 2001 to 2019 (2001.01.28, 2005.01.19, 2010.09.27, 2015.08.04, and 2019.11.24) for the site and its surrounding area as shown below. I would recommend downloading the full satellite images via Tsinghua Cloud.

For better understanding the public space / streets of our site and its temporal evolution at the human scale, we have collected street view pictures captured in July 2013 and June 2017. We picked an observation point every a certain distance to guarantee the main streets of the whole site can be (mostly) fully covered. We selected four images for each observation point at various directions. The ID of each point labeled in the below figure is corresponding to the street view picture name directly.

We have put all street view images in Tsinghua Cloud.

We are providing big/open urban spatial data for our site and its surrounding area. These data range from buildings, roads, residential communities, points of interest, to areas of interest, and social media records like Dianping and Weibo. These data are all spatially explicit (spatial data).

We are providing three forms of presentations of the data for you. You can choose an appropriate one according to your capacity on data analysis and interest. First, we have mapped each data layer into images for your downloading and use in PS/PPT directly and the high resolution maps are available at Tsinghua Cloud.

Second, we put most of spatial layers to an online data visualization platform GeoHey. You will be able to explore each layer (like zoom in/out and pan) and overlay them by yourself.

Third, we have shared the raw data here in the format of GIS (geographical information system). If you are familiar with any GIS software like the open software QGIS, you can analyze the data quantitatively by yourself. You can also re-map the layers and visualize the analysis results in the GIS environment. Please click the below Zip for downloading the raw GIS data for our site and its surrounding area.

We have inferred companies in each main office building in our site using the provided points of interest, according to their location with office buildings and their detailed address in the layer. Hope this helps the sharing work group.

Integration

Integration measures how close each segment is to all others in terms of the sum of angular changes that are made on each route.

Sources

Hillier , B. & Iida, S. (2005), Network and psychological effects in urban movement, In: A.G. Cohn and D.M. Mark (Eds.): COSIT 2005, LNCS 3693, pp. 475-490

Choice

Choice is calculated by counting the number of times each street segment falls on the shortest path between all pairs of segments within a selected distance (termed ‘radius’). The ‘shortest path’ refers to the path of least angular deviation (namely, the straightest route) through the system.

Sources

Hillier , B. & Iida, S. (2005), Network and psychological effects in urban movement, In: A.G. Cohn and D.M. Mark (Eds.): COSIT 2005, LNCS 3693, pp. 475

We have tried to collected more related cases for each group and shared at Tsinghua Cloud.

The full report is available on request (ylong@tsinghua.edu.cn)

Some of them are at the Tsinghua Cloud of Prof HUANG He and DropBox I created as well.