Small leap forward

Project Type
Spatial Analysis, Data Visualization ​

Role
Spatial Analysis, Data Visualization, Storytelling

Year
2017

Collaborator
Kun Cheng, Qianhui Liang

This part of the study shows what is the urban regeneration in China. Usually a rental gap can indicate the existence of an urban village. Since many cases of urban regeneration are happening simultaneously in China, we are interested in how to use spatial analysis to identify these potential sites which embed future changes, in order to help both official agencies and local residents get well-prepared for these latent changes.


Basically we are using different informal data platform – they consist a crowdsourcing database for our project. We use the data provide by these various agencies and build a cartographic model, and adopt the matrix of demography, building information, informal consumption information, rental information and geolocation to predict the potential sites.


We set our study site within the middle ring road of Shanghai – it is due to our data availability and our familiarity with the site.


Another thing that we need to pay attention to is the neighborhoods without data – as long as the neighborhood lacks one of the indicators of our analytic matrix, we exclude them from our analysis scope. This helps us set the boundary of our study.


We create different maps at two scales to help us visualize the site situation regarding to historical community situation, building age, property value, rental value and low-income service quantity.


We then use a reclassification to identify the neighborhood with high reclassified standard deviation.


Finally, our analysis helps us select the neighborhood with these inconsistency regarding of rental gap and low socioeconomic consumption activities. We examine them on the google map and it turns out that they are all existing urban villages with high potential to be redeveloped. 


We also consider continue this study combining the public transportation data as well as educational agency data, in order to more precisely detect these sites.