Using Dynamic Mapping

import transportation_tutorials as tt
import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt
import folium
from shapely.geometry import Polygon

Questions

Using folium, create a dynamic map and place markers on the centroids of the five MAZ’s with the highest population density MAZs. Use popup text on markers to show population density and total employment information for each of those MAZs. By clicking the markers, you should be able to answer the following questions:

1. Where is the highest population density?
2. What is the total employment in the MAZ that has the highest population density?

Data

To answer the questions, use the following files:

maz = gpd.read_file(tt.data('SERPM8-MAZSHAPE'))
maz_data = pd.read_csv(tt.data('SERPM8-MAZDATA', '*.csv'))

maz.head()

	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,...

#maz = maz.to_crs(epsg = 4326)

maz.crs

{'init': 'epsg:2236'}

maz.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1a28780a20>

maz_data.head()

	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

Solution

First, we will merge maz_data dataframe with maz geo-dataframe to get a single dataframe with required geographic and demographic information together. After merging, we select top 5 highly populated MAZs.

maz_data = maz_data[['mgra', 'POP', 'PopDen', 'emp_total']]

# maz_data = maz_data.astype(float)

maz = maz.merge(maz_data, how = 'left', left_on = 'MAZ', right_on = 'mgra')

maz['ExactPopDen'] = maz['POP'] / maz.area * 5280**2

maz = maz.sort_values(by = 'ExactPopDen', ascending = False).head(5)

Then, we prepare the geographic information for dynamic mapping. We convert the CRS format of coordinates of MAZ centroids to input them in folium, the mapping package that we are going to use. We will use pyproj for this purpose.

from pyproj import Proj, transform
input_Proj  = Proj("+init=EPSG:2236",preserve_units=True)
output_Proj = Proj("+init=EPSG:4326")

point_x = []
point_y = []

for index, row in maz.iterrows():

    coor = transform(input_Proj,output_Proj,row.POINT_X, row.POINT_Y)
    point_x.append(coor[0])
    point_y.append(coor[1])

maz['POINT_X_'] = point_x
maz['POINT_Y_'] = point_y

maz

	OBJECTID	MAZ	SHAPE_LENG	SHAPE_AREA	ACRES	POINT_X	POINT_Y	geometry	mgra	POP	PopDen	emp_total	ExactPopDen	POINT_X_	POINT_Y_
8719	8720	2043	1118.734044	73775.530919	2	923754	523748	POLYGON ((923616.3597905785 523581.4791255742,...	2043	1271	33.70	2127	480287.243731	-80.186882	25.772271
7439	7440	763	4598.270892	96146.266956	2	932171	571467	POLYGON ((932668.741948247 572438.5365949869, ...	763	869	16.03	0	251973.689328	-80.160381	25.903407
8753	8754	2077	2213.840968	284133.182991	7	922641	521139	POLYGON ((922916.4012167454 521429.7248989046,...	2077	1508	52.82	1040	147960.990538	-80.190312	25.765112
8640	8641	1964	2578.656554	344555.061769	8	923398	531247	POLYGON ((923850.8084686622 531288.7324118242,...	1964	1755	25.74	62	141999.341843	-80.187823	25.792908
8642	8643	1966	2030.071693	247320.734972	6	923906	530650	POLYGON ((924173.7717334963 530376.9533954933,...	1966	1118	22.44	978	126022.798709	-80.186290	25.791257

Now, we create a map with a suitable starting location and zoom level. Then, we plot the markers with necessary popup text.

maz_map = folium.Map(location = [maz.POINT_Y_.mean(), maz.POINT_X_.mean()], tiles = 'Stamen Toner', zoom_start = 9)

for index, row in maz.iterrows():
    popup = 'PopDen:{}, EMP:{}, ExactPopDen:{:,.0f}'.format(row.PopDen, row.emp_total, row.ExactPopDen)
    folium.Marker([row.POINT_Y_, row.POINT_X_], popup = popup, tooltip = 'MAZ ID:{}'.format(row.MAZ)).add_to(maz_map)

maz_map