Using Dynamic Mapping¶

[1]:

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:

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

Data¶

To answer the questions, use the following files:

[2]:

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

[3]:

maz.head()

[3]:

	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,...

[4]:

#maz = maz.to_crs(epsg = 4326)

[5]:

maz.crs

[5]:

{'init': 'epsg:2236'}

[6]:

maz.plot()

[6]:

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

../_images/exercises_exercise-geo-dynamic-map_10_1.png

[7]:

maz_data.head()

[7]:

	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