Some Useful Data Science Techniques in Python to Analyze and Improve City Resilience Indexes: Case Study Mexico

In this work we show how city indexes can be analyzed by using some basic data analysis techniques in Python. The correlation of variables allows us to identify which are the variables/features that have the greatest impact on the general index. In this way, city managers can make data driven decisions to improve the general index and with this the ranking of their cities. Multiple linear regression models can be useful to obtain general indexes when the methodology to assign indexes is unknown while simple linear regression models can be used to make future projections that allow us to take measures to improve the performance of cities.