Table Of Contents

  1. Docs
  2. Exercises
  3. Using Static Mapping

Using Static Mapping

[1]:

import transportation_tutorials as tt
import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from shapely.geometry import Polygon, Point

Questions

  1. Generate a population density map of Miami-Dade County, at the MAZ resolution level for SERPM 8.

Data

To answer the question, use the following files:

[2]:

maz = gpd.read_file(tt.data('SERPM8-MAZSHAPE'))
maz.head()

[2]:
OBJECTID MAZ SHAPE_LENG SHAPE_AREA ACRES POINT_X POINT_Y geometry
0 1 5347 8589.393674 3.111034e+06 71 953130 724165 POLYGON ((953970.4660769962 723936.0810402408,...
1 2 5348 11974.067469 7.628753e+06 175 907018 634551 POLYGON ((908505.2801046632 635081.7738410756,...
2 3 5349 9446.131753 4.007041e+06 92 923725 707062 POLYGON ((922736.6374686621 708387.6918614879,...
3 4 5350 21773.153739 2.487397e+07 571 908988 713484 POLYGON ((908334.2374677472 715692.2628822401,...
4 5 5351 17882.701416 1.963139e+07 451 909221 717493 POLYGON ((911883.0187559947 719309.3261861578,... 
[3]:

maz_data = pd.read_csv(tt.data('SERPM8-MAZDATA', '*.csv'))
maz_data.head()

[3]:
mgra TAZ HH POP emp_self emp_ag emp_const_non_bldg_prod emp_const_non_bldg_office emp_utilities_prod emp_utilities_office ... EmpDenBin DuDenBin POINT_X POINT_Y ACRES HotelRoomTotal mall_flag beachAcres geoSRate geoSRateNm
0 1 2901 43 169 0 0 0 0 0 0 ... 1 1 841743 586817 510 0 0 0 1 1
1 2 2902 9 21 0 1 1006 0 8 0 ... 1 1 855391 585688 5678 0 0 0 1 1
2 3 2903 403 1389 0 0 6 0 0 0 ... 1 1 858417 549492 85 0 0 0 1 1
3 4 2903 477 1659 0 0 3 0 0 0 ... 1 1 858468 552269 103 0 0 0 1 1
4 5 2903 374 1389 0 0 11 0 0 0 ... 1 1 859899 552161 72 0 0 0 1 1

5 rows × 76 columns

[4]:

fl_county = gpd.read_file(tt.data('FL-COUNTY-SHAPE'))
fl_county.head()

[4]:
OBJECTID DEPCODE COUNTY COUNTYNAME DATESTAMP ShapeSTAre ShapeSTLen geometry
0 1 21 041 GILCHRIST 2000-05-16T00:00:00.000Z 9.908353e+09 4.873000e+05 POLYGON ((-82.65813600527831 29.83028106592836...
1 2 54 107 PUTNAM 2000-05-16T00:00:00.000Z 2.305869e+10 7.629677e+05 POLYGON ((-81.58084263191225 29.83955869988373...
2 3 62 123 TAYLOR 2000-05-16T00:00:00.000Z 2.891747e+10 8.772527e+05 (POLYGON ((-83.73036687278116 30.3035764144186...
3 4 46 091 OKALOOSA 2000-05-16T00:00:00.000Z 2.562159e+10 1.087058e+06 (POLYGON ((-86.39159408384468 30.6497039172779...
4 5 7 013 CALHOUN 2000-05-16T00:00:00.000Z 1.604809e+10 6.313440e+05 POLYGON ((-84.93265667941331 30.60636534524649...

Solution

We can begin by extracting just Miami-Dade county from the counties shapefile. As noted, the name is recorded in this file as just “DADE”, so we can use that to get the correct county.

[5]:

md_county = fl_county.query("COUNTYNAME == 'DADE'")

The county shapefile uses a different crs, so we’ll need to make them aligned before doing a join.

[7]:

md_county = md_county.to_crs({'init': 'epsg:2236', 'no_defs': True})

[8]:

md_polygon = md_county.iloc[0].geometry

Next, we can select the MAZ centroids that are within the Miami-Dade polygon.

[9]:

md_maz = maz[maz.centroid.within(md_polygon)]

Then we merge maz_data dataframe with spatially joined md_maz to pull in the required population information.

[10]:

md_maz_info = md_maz.merge(maz_data.astype(float)[['mgra', 'POP']], how = 'left', left_on = 'MAZ', right_on = 'mgra')

[11]:

md_maz_info.head()

[11]:
OBJECTID MAZ SHAPE_LENG SHAPE_AREA ACRES POINT_X POINT_Y geometry mgra POP
0 6678 1 22430.293060 2.220769e+07 510 841744 586819 POLYGON ((843052.3509714119 590418.30837816, 8... 1.0 169.0
1 6679 2 77075.035787 2.480557e+08 5695 855391 585705 POLYGON ((872650.4321950786 590666.4279606566,... 2.0 21.0
2 6680 3 8681.166565 3.704251e+06 85 858417 549492 POLYGON ((859334.5900604129 549311.0687424093,... 3.0 1389.0
3 6681 4 8629.955860 4.492026e+06 103 858468 552269 POLYGON ((859424.1906029955 553546.9969503209,... 4.0 1659.0
4 6682 5 8022.697944 3.140387e+06 72 859899 552161 POLYGON ((860482.4981976636 553546.9001657367,... 5.0 1389.0

We can review a map of the selected MAZ’s and the Miami-Dade County borders. Note that the MAZ’s don’t actually cover the whole county, as the south and west areas of the county are undeveloped swampland.

[12]:

ax = md_maz_info.plot()
md_county.plot(ax=ax, color='none', edgecolor='red');
../_images/exercises_solution-geo-static-map_21_0.png

Because the unit of measure in EPSG:2236 is approximately a foot, the area property of the md_maz_info GeoDataFrame gives the area in square feet. To express population density in persons per square mile, we need to multiply by 5280 (feet per mile) squared.

[13]:

md_maz_info["Population Density"] = md_maz_info.POP / md_maz_info.area * 5280**2

A first attempt at drawing a population density choropleth shows something is wrong; the entire county is displayed as nearly zero.

[14]:

fig, ax = plt.subplots(figsize=(12,9))
ax.axis('off') # don't show axis
ax.set_title("Population Density", fontweight='bold', fontsize=16)
ax = md_maz_info.plot(ax=ax, column="Population Density", legend=True)
../_images/exercises_solution-geo-static-map_25_0.png

The problem is identifiable in the legend: the scale goes up to nearly half a million people per square mile, which is an enormous value, and generally not achievable unless a zone is basically just skyscrapers. This does apply to a handful of MAZ’s in downtown Miami, but the density everywhere else is so much lower that this map is meaningless.

We can create a more meaningful map by clipping the top of the range to a more reasonable value, say only 40,000 people per square mile.

[15]:

fig, ax = plt.subplots(figsize=(12,9))
ax.axis('off') # don't show axis
ax.set_title("Population Density", fontweight='bold', fontsize=16)
ax = md_maz_info.plot(ax=ax, column=np.clip(md_maz_info["Population Density"], 0, 40_000), legend=True)
../_images/exercises_solution-geo-static-map_27_0.png
Using Spatial Joins
Using Dynamic Mapping
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