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Urban greenery assessment

Urban greenery assessment - Python Tutorial

From the course: Geospatial Data Analytics Essential Training

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Urban greenery assessment

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- [Instructor] In this final section, we will combine the previously learned geospatial analytical steps and develop an algorithm that ranks New York City neighborhoods based on two factors, the total number of registered trees and the total registered population. In other words, by computing the tree per capita index, we estimate the level of green coverage and the lack of it across the city, addressing one of the most pressing topics of today's urban planning using quantitative data. To do this, we'll rely on the previously created GeoDataFrame containing the neighborhood level population data, as well as the neighborhood geometries, and on the previously created GeoDataFrame containing the location and other information of trees across the city. First, let's import GeoPandas and display these input data tables. Next, we merge these two data frames into one. While the neighborhood level data frame contains polygons, the tree level data frame contains points. Hence, doing a spatial…

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