# Chicago04: Distance weighted environmental index

In “chicago02: network structure analysis_networkx”, pysal is used to calculate the spatial distance weight when establishing the spatial weight between network routers. Pysal gives a variety of different methods to calculate the spatial weight, and involves a variety of spatial statistical models to analyze the spatial relationship between regions. However, the current content of pysal library analysis is vector objects. If there are multiple vector objects, they should be divided into two parts When analyzing the spatial relationship between an object and a substitute value, you need to code it yourself. This experiment needs to analyze the influence of 614 Chicago city parks’ external environment on parks themselves, including SVF sky horizon factor and population distribution, Raser near the park polygon boundary Cell cells should have higher weight values, so it is necessary to calculate the spatial distance weight values of all grid cells of 614 polygon objects in a certain range, because its statistical data are 46337 polygon objects, and the old grid data of 1m-3m high-altitude resolution is smaller. Therefore, the distance between each park boundary and all population distribution polygons of 614 parks can be calculated directly.

**The result of grid value extraction under polygon value**

SVF weight environment

**Population weight and environmental pressure**

In CP255, I learn a few methods about data visualization. As for final project, I used this kind of model to analyze urban problems as well. However, I have not finished yet. Hence, I cannot share that project with you. But I really interested in this kind of topics. I think I will continue exploring. If you also have interest in this, please discuss with me. I am soooo glad to hearing from you.