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Computing local statistics

Computing local statistics - Python Tutorial

From the course: Geospatial Data Analytics Essential Training

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Computing local statistics

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- [Instructor] In this video, we will learn about computing population level statistics within the reach of a selected area, which in this example is Manhattan. In particular, we will compute the number of people living in a given distance range from Manhattan using the polygon buffering technique we learned earlier. For this, we first import GeoPandas the usual way. Additionally, we will use the GeoDataFrame called gdf_merged, which we defined in the previous video titled, Enrich Administrative Boundaries by Population Information. Then we use the borough name column of this GeoDataFrame to filter it down to Manhattan. Finally, we will store this filter to GeoDataFrame in a new GeoDataFrame, only containing Manhattan. Then to ensure that we conduct local statistics using the proper CRS, we transform both the original neighborhood level data frame and the one filter down to Manhattan using the local CRS of EPSG 2263. Additionally, we quickly visualize the Manhattan data using the…

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