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.gitignore
vendored
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.gitignore
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poetry.lock
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.ipynb_checkpoints/
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README.md
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README.md
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**Chicago Crime Analysis**
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Basic analysis of chicago crime in python. Using Chicago Police Department Data.
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The goal is to find which areas of the city are the safest, and which are the
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most dangerous.
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The code in the jupyter notebook produces the below graph:
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![Image of the plot](map.png)
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Areas in red have higher relative crime than areas in blue.
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crimes.ipynb
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crimes.ipynb
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data/crimes.csv
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data/crimes.csv
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map.png
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map.png
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maps/Boundaries - Police Beats (current).zip
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maps/Boundaries - Police Beats (current).zip
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maps/geo_export_cf56fcdf-4e4b-485b-83b8-f76287821f6a.dbf
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maps/geo_export_cf56fcdf-4e4b-485b-83b8-f76287821f6a.prj
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maps/geo_export_cf56fcdf-4e4b-485b-83b8-f76287821f6a.prj
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GEOGCS["WGS84(DD)", DATUM["WGS84", SPHEROID["WGS84", 6378137.0, 298.257223563]], PRIMEM["Greenwich", 0.0], UNIT["degree", 0.017453292519943295], AXIS["Geodetic longitude", EAST], AXIS["Geodetic latitude", NORTH]]
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maps/geo_export_cf56fcdf-4e4b-485b-83b8-f76287821f6a.shp
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maps/geo_export_cf56fcdf-4e4b-485b-83b8-f76287821f6a.shx
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maps/geo_export_cf56fcdf-4e4b-485b-83b8-f76287821f6a.shx
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pyproject.toml
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pyproject.toml
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[tool.poetry]
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name = "chicago-crime"
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version = "0.1.0"
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description = ""
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authors = ["Noah Swerhun <noah@noahsw.xyz>"]
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readme = "README.md"
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[tool.poetry.dependencies]
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python = "^3.11"
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pandas = "^2.1.0"
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jupyter = "^1.0.0"
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ipykernel = "^6.25.2"
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geopandas = "^0.13.2"
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geoplot = "^0.5.1"
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[build-system]
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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