I'm using 7 functions to create dot density maps in Python, Pandas, and GeoPandas. The functions read the US Census blockgroups and create random points that fall within the blockgroup polygon. The end result is a nice dot density map where each point represents 300 people of a certain ethnicity.
For some of the counties, this works totally fine. Here's Denver County
But for others I'm hitting list index out of range and I can't figure out why. I'm using the FIPS blockgroup as the index this is what the dataframe ends up looking like.
gen_count_dot_density_map('Yakima County, WA')
Error message:
IndexError Traceback (most recent call last)
<ipython-input-25-3174d4f9a7b7> in <module>()
----> 1 gen_count_dot_density_map('Yakima County, WA')
3 frames
/usr/local/lib/python3.7/dist-packages/pandas/core/reshape/reshape.py in stack(frame, level, dropna)
520 # we concatenate instead.
521 dtypes = list(frame.dtypes._values)
--> 522 dtype = dtypes[0]
523
524 if is_extension_array_dtype(dtype):
IndexError: list index out of range
Here are the functions I'm using.
from urllib.request import urlopen
from zipfile import ZipFile
from io import BytesIO, StringIO
import shapefile
import geopandas as gpd
from shapely.geometry import shape
import osr
import pandas as pd
import requests
from shapely.geometry import Point
from numpy.random import RandomState, uniform
import numpy as np
def gen_random_points_poly(poly, num_points, seed = None):
"""
Returns a list of N randomly generated points within a polygon.
"""
min_x, min_y, max_x, max_y = poly.bounds
points = []
i=0
while len(points) < num_points:
s=RandomState(seed+i) if seed else RandomState(seed)
random_point = Point([s.uniform(min_x, max_x), s.uniform(min_y, max_y)])
if random_point.within(poly):
points.append(random_point)
i+=1
return points
def gen_points_in_gdf_polys(geometry, values, points_per_value = None, seed = None):
"""
Take a GeoSeries of Polygons along with a Series of values and returns randomly generated points within
these polygons. Optionally takes a "points_per_value" integer which indicates the number of points that
should be generated for each 1 value.
"""
if points_per_value:
new_values = (values/points_per_value).astype(int)
else:
new_values = values
new_values = new_values[new_values>0]
g = gpd.GeoDataFrame(data = {'vals':new_values}, geometry = geometry)
a = g.apply(lambda row: tuple(gen_random_points_poly(row['geometry'], row['vals'], seed)),1)
b = gpd.GeoSeries(a.apply(pd.Series).stack(), crs = geometry.crs)
b.name='geometry'
return b
def zip_shp_to_gdf(zip_file_name):
"""
Returns a GeoDataFrame from a URL for a zipped Shapefile
"""
zipfile = ZipFile(BytesIO(urlopen(zip_file_name).read()))
filenames = [y for y in sorted(zipfile.namelist()) for ending in ['dbf', 'prj', 'shp', 'shx']\
if y.endswith(ending)]
dbf, prj, shp, shx = [BytesIO(zipfile.read(filename)) for filename in filenames]
r = shapefile.Reader(shp=shp, shx=shx, dbf=dbf)
attributes, geometry = [], []
field_names = [field[0] for field in r.fields[1:]]
for row in r.shapeRecords():
geometry.append(shape(row.shape.__geo_interface__))
attributes.append(dict(zip(field_names, row.record)))
proj4_string = osr.SpatialReference(prj.read().decode('UTF-8')).ExportToProj4()
gdf = gpd.GeoDataFrame(data = attributes, geometry = geometry, crs = proj4_string)
return gdf
def get_census_variables(year, dataset, geography, area, variables, variable_labels = None):
"""Wraps the Census API and returns a DataFrame of Census Data
Parameters
----------
year : integer
Year representing the dataset vintage
dataset : string
the name of the dataset (https://api.census.gov/data.html)
geography : string
the census geography
area : dictionary
dictionary contains the FIPS codes at each nested geographic level. For example "{'county':'001', 'state':'06'}"
variables : list
list of the variables to be extracted
variable_labels : list
optional to relabel the variable names. Must be same length as "variables"
"""
base_url = 'https://api.census.gov/data/{}/acs/{}'.format(year, dataset)
#define parameters
get_parameter = ','.join(['NAME'] + variables)
for_parameter = '{}:*'.format(geography)
in_paramater = '+'.join([k+':'+v for (k,v) in area.items()])
parameters = {'get' : get_parameter,
'for' : for_parameter,
'in' : in_paramater}
#make request specifiying url and parameters
r = requests.get(base_url, params=parameters)
#read json into pandas dataframe, specifying first row as column names
data = r.json()
df=pd.DataFrame(columns = data[0], data = data[1:])
#identify geography fields - concatenate them into a fips code to be set as index and then delete them
geo_fields = [x for x in df.columns if x not in ['NAME'] + variables]
df.index = df[geo_fields].apply(lambda row: ''.join(map(str, row)), 1)
df.index.name = 'FIPS'
df = df.drop(geo_fields, 1)
if variable_labels:
df = df.rename(columns = dict(zip(variables, variable_labels)))
#convert data numeric
df = df.applymap(lambda x:pd.to_numeric(x, errors='ignore'))
return df
def gen_count_dot_density_map(county, pts_per_person = 300,
epsg = 2163, seed=10,
dot_transparency=0.4, figsize=(12,12),
ax=None, legend=False):
"""
Wraps previous functions and generates population dot density maps for a specified county by race
"""
#read in fips to county name relationship file
fips = pd.read_csv('https://www2.census.gov/geo/docs/reference/codes/files/national_county.txt',
header=None, dtype={1:np.object, 2:np.object})
fips['name']=fips[3]+', '+fips[0]
fips['fips']=fips[1]+fips[2]
#get name from fips if fips specified
if county.isdigit():
lookup = fips.set_index('fips')['name']
county_fips = county
name = lookup[county_fips]
#get fips from name if name specified
else:
lookup = fips.set_index('name')['fips']
name = county
county_fips = lookup[name]
#get geodataframe of block group shapefile
bgfile_name = 'http://www2.census.gov/geo/tiger/GENZ2015/shp/cb_2015_{}_bg_500k.zip'.format(county_fips[:2])
bg_geo = zip_shp_to_gdf(bgfile_name)
#subset to those that are in the county and project it to the CRS
bg_geo=bg_geo[bg_geo['GEOID'].str[:5]==county_fips].to_crs(epsg=epsg).set_index("GEOID")['geometry']
#specify variable list and variable names for the census api function
varlist = ['B03002_003E',
'B03002_012E',
'B03002_004E',
'B03002_006E',
'B03002_005E',
'B03002_007E',
'B03002_008E',
'B03002_009E']
names = ['White',
'Hispanic',
'Black',
'Asian',
'AI/AN',
'NH/PI',
'Other_',
'Two Plus']
#read in block group level census variables
dems = get_census_variables(2015, 'acs5', 'block group',
{'county':county_fips[2:],
'state':county_fips[:2]}, varlist, names)
#Calculate other as sum of those not in the 4 most populated race categories
dems['Other']=dems[['AI/AN', 'NH/PI','Other_', 'Two Plus']].sum(1)
#Calculate county boundaries as the union of block groups
union = gpd.GeoSeries(bg_geo.unary_union)
#if axes object is specified, plot to this axis, otherwise create a new one
if ax:
union.plot(color='white', figsize=figsize, ax=ax)
else:
ax = union.plot(color='white', figsize=figsize)
#set aspect equal and add title if specified
ax.set(aspect='equal', xticks=[], yticks=[])
#set title as county name
ax.set_title(name, size=15)
#annotate the dot per person ratio
# ax.annotate("1 dot = {} people".format(pts_per_person),
# xy=(.5, .97), xycoords='axes fraction', horizontalalignment='center',
# fontsize = 12)
#loop each race category and generate points for each within each block group
list_of_point_categories=[]
for field in ['White','Hispanic','Black','Asian','Other']:
ps=gpd.GeoDataFrame(gen_points_in_gdf_polys(geometry = bg_geo, values=dems[field],
points_per_value = pts_per_person, seed=seed))
ps['field']=field
list_of_point_categories.append(ps)
all_points=gpd.GeoDataFrame(pd.concat(list_of_point_categories))
print(all_points.head())
all_points.plot(ax=ax, markersize=2, alpha=dot_transparency, column='field', categorical=True, legend=legend)
return ax
Best Answer
This is a pure Python problem (not a geospatial problem) and is due to the value of
pts_per_personin the functiongen_points_in_gdf_polys(geometry, values, points_per_value = None, seed = None)(300 in your example)In your example, most of the values of
dems['White'](for example) are > 300, so:But this is not the case with
dems['Asian']Here all the values are null, and the result is the error
indexError: list index out of rangeChoosing a smaller value resolves the problem. With
pts_per_person = 180and your script, for example