# Introduction to Computational Analysis

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from IPython.core.display import Image
Image(filename='images/Xela-PazAmor.jpg')  # Xela
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Image(filename='images/NeckFace-CreepingSleeping.jpg')  # Los Angeles
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Image(filename='images/NewYork-OldTimers.jpg')  # New York

Rebecca is an anthropologist who wants to understand New York through its graffiti. Help her find the subway entrances with the most number of graffiti within a hundred foot radius.

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graffiti.ix[0]
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graffiti = graffiti[graffiti.Status == 'Open']
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graffitiXY = graffiti[['X Coordinate', 'Y Coordinate']]
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graffitiXY = graffitiXY.rename(columns={'X Coordinate': 'X', 'Y Coordinate': 'Y'})
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graffitiXY = graffitiXY.dropna()
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subway.ix[0]
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from pandas import Series
from geometryIO import get_transformPoint, proj4LL

proj4NY = '+proj=lcc +lat_1=41.03333333333333 +lat_2=40.66666666666666 +lat_0=40.16666666666666 +lon_0=-74 +x_0=300000.0000000001 +y_0=0 +ellps=GRS80 +datum=NAD83 +to_meter=0.3048006096012192 +no_defs'
transformPoint = get_transformPoint(proj4LL, proj4NY)

def parse_point(row):
string = row['Shape']
latitude, longitude = string.replace('(', '').replace(')', '').split(',')
x, y = transformPoint(float(longitude), float(latitude))
return Series(dict(ID=row['OBJECTID'], X=x, Y=y))
subwayIDXY = subway.apply(parse_point, axis=1)
subwayXY = subwayIDXY[['X', 'Y']]

# Count graffiti sightings within 100 feet of a subway entrance¶

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from scipy.spatial import KDTree
subwayXYValues = subwayXY.values
subwayTree = KDTree(subwayXYValues)
graffitiXYValues = graffitiXY.values
graffitiTree = KDTree(graffitiXYValues)
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from pandas import DataFrame

graffitiXYIndexPacks = subwayTree.query_ball_tree(graffitiTree, r=100)
results = []
for subwayID, graffitiXYIndices in zip(subwayIDXY['ID'], graffitiXYIndexPacks):
results.append([subwayID, len(graffitiXYIndices)])
subwayGraffiti = DataFrame(results, columns=['OBJECTID', 'COUNT'])
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subwayGraffiti = subwayGraffiti.merge(subway)