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  "documentTitle": "Homeowner availability study",
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  "authorName": "Oliver Wyman",
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  "notes": "The slide outlines three pillars: Data Manipulation, Visual Representation, and the conversion process from ZIP to County data.",
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      "text": "This representation is imperfect, as in practice any given ZIP code can spread across more than one county.",
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      "text": "Data Manipulation: We used the 'zipcodes' open-source Python package to perform our manipulations of the ZIP code-level data collected from carriers. Some of the useful information we relied on includes the type of code ('Standard' or 'PO Box'), their central location (lat./long.), as well as associated their cities and counties. Link: https://pypi.org/project/zipcodes/",
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      "text": "From ZIP code data to County data: Using the 'zipcodes' open-source package, we retrieved the most county associated to each unique zip code. We then combined this to the ArcGIS dataset to compute the geographical surface of each county by combining the surface of each underlying ZIP code. This representation is imperfect, as in practice any given ZIP code can spread across more than one county. Nonetheless, we believe this simplified representation may prove useful to the reader.",
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      "text": "Visual Representation: We used ArcGIS' dataset of US ZIP codes areas (as-of Dec 2021) – owned by ESRI – to present our figures onto maps (using the geographical coordinates relevant to each ZIP code). The map we relied on is 'Open Street' map, which is free and publicly available. This dataset also contained some demographic information such as the population density by zip code (as-of June 2021), which we used as well. Link: https://www.arcgis.com/home/item.html?id=8d2012a2016e484dafaac0451f9aea24",
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