Hexagon Shift

In [10]:

import geopandas as gpd
import pandas as pd
from shapely.affinity import translate
import matplotlib.pyplot as plt
import math
from matplotlib import pyplot as plt
from matplotlib.legend_handler import HandlerPatch
from mpl_toolkits.mplot3d import Axes3D

import geopandas as gpd import pandas as pd from shapely.affinity import translate import matplotlib.pyplot as plt import math from matplotlib import pyplot as plt from matplotlib.legend_handler import HandlerPatch from mpl_toolkits.mplot3d import Axes3D

Read data¶

In [3]:

origin_hexgaon = gpd.read_file("../../Data/Output/Study_Area.shp")
print(origin_hexgaon.shape)

origin_hexgaon = gpd.read_file("../../Data/Output/Study_Area.shp") print(origin_hexgaon.shape)

(22504, 2)

Shift hexagon to 6 corners¶

In [4]:

origin_hexgaon.geometry[0]

origin_hexgaon.geometry[0]

Out[4]:

In [5]:

# Get coordinates of original centroids
x = origin_hexgaon.geometry[0].centroid.x
y = origin_hexgaon.geometry[0].centroid.y
x,y

# Get coordinates of original centroids x = origin_hexgaon.geometry[0].centroid.x y = origin_hexgaon.geometry[0].centroid.y x,y

Out[5]:

(745603.4525000006, 2051234.2399555547)

In [6]:

xx_lst, yy_lst = origin_hexgaon.geometry[0].exterior.coords.xy
xx_lst.tolist()[:], yy_lst.tolist()[:]

xx_lst, yy_lst = origin_hexgaon.geometry[0].exterior.coords.xy xx_lst.tolist()[:], yy_lst.tolist()[:]

Out[6]:

([746003.4525000006,
  745803.4525000006,
  745403.4525000006,
  745203.4525000006,
  745403.4525000006,
  745803.4525000006,
  746003.4525000006],
 [2051234.2399999984,
  2050887.8297999986,
  2050887.8297999986,
  2051234.2399999984,
  2051580.6501000002,
  2051580.6501000002,
  2051234.2399999984])

In [36]:

shifted_1 = translate(origin_hexgaon.geometry[0],xoff=(x - xx_lst[0]),yoff=(y - yy_lst[0]))
fig, ax = plt.subplots(figsize = (12,6))

ax.plot(*origin_hexgaon.geometry[0].exterior.xy,color = "blue", linewidth=3)
ax.plot(*shifted_1.exterior.xy, color = "red", linewidth=3)

# Create polygons
poly1 = origin_hexgaon.geometry[0] 
poly2 = shifted_1
# Get intersection  
intersection = poly1.intersection(poly2)

ax.plot(*intersection.exterior.xy, color = "green", linewidth=3)
# Show plot
plt.show()

shifted_1 = translate(origin_hexgaon.geometry[0],xoff=(x - xx_lst[0]),yoff=(y - yy_lst[0])) fig, ax = plt.subplots(figsize = (12,6)) ax.plot(*origin_hexgaon.geometry[0].exterior.xy,color = "blue", linewidth=3) ax.plot(*shifted_1.exterior.xy, color = "red", linewidth=3) # Create polygons poly1 = origin_hexgaon.geometry[0] poly2 = shifted_1 # Get intersection intersection = poly1.intersection(poly2) ax.plot(*intersection.exterior.xy, color = "green", linewidth=3) # Show plot plt.show()

3D plot¶

In [30]:

# Create 3D plot
fig = plt.figure(figsize = (15,9))
ax = fig.add_subplot(111, projection='3d')

# Set view angle
ax.view_init(elev=None, azim=45)

# Plot polygons on different z levels

for idx in [0,1]:
    poly1 = origin_hexgaon.geometry[idx]
    x = poly1.centroid.x
    y = poly1.centroid.y
    xx_lst, yy_lst = poly1.exterior.coords.xy
    poly2 = translate(poly1,xoff=(x - xx_lst[0]),yoff=(y - yy_lst[0]))
    poly3 = translate(poly1,xoff=(x - xx_lst[1]),yoff=(y - yy_lst[1]))
    poly4 = translate(poly1,xoff=(x - xx_lst[2]),yoff=(y - yy_lst[2]))
    poly5 = translate(poly1,xoff=(x - xx_lst[3]),yoff=(y - yy_lst[3]))
    poly6 = translate(poly1,xoff=(x - xx_lst[4]),yoff=(y - yy_lst[4]))
    poly7 = translate(poly1,xoff=(x - xx_lst[5]),yoff=(y - yy_lst[5]))
    ax.plot(*poly1.exterior.xy, zs=1, zdir='z', color='blue')
    ax.plot(*poly1.exterior.xy, zs=0.5, zdir='z', color='blue')
    ax.plot(*poly2.exterior.xy, zs=0.5, zdir='z', color='red')
    ax.plot(*poly3.exterior.xy, zs=0.5, zdir='z', color='red')
    ax.plot(*poly4.exterior.xy, zs=0.5, zdir='z', color='red')
    ax.plot(*poly5.exterior.xy, zs=0.5, zdir='z', color='red')
    ax.plot(*poly6.exterior.xy, zs=0.5, zdir='z', color='red')
    ax.plot(*poly7.exterior.xy, zs=0.5, zdir='z', color='red')
    
    # Get intersection  
    ax.plot(*poly1.intersection(poly2).intersection(poly3).exterior.xy, zs=0, zdir='z', color='green')
    ax.plot(*poly1.intersection(poly3).intersection(poly4).exterior.xy, zs=0, zdir='z', color='green')
    ax.plot(*poly1.intersection(poly4).intersection(poly5).exterior.xy, zs=0, zdir='z', color='green')
    ax.plot(*poly1.intersection(poly5).intersection(poly6).exterior.xy, zs=0, zdir='z', color='green')
    ax.plot(*poly1.intersection(poly6).intersection(poly7).exterior.xy, zs=0, zdir='z', color='green')
    ax.plot(*poly1.intersection(poly7).intersection(poly2).exterior.xy, zs=0, zdir='z', color='green')

# Set axis limits
ax.set_zlim(0,1)
# Remove x tick labels  
ax.set_xticks([])
# Remove y ticks and labels
ax.set_yticks([])
# Enable y grid lines
ax.yaxis.grid(True)

# Create proxy artists
blue_proxy = plt.Rectangle((0,0), 1, 1, fc="blue")
red_proxy = plt.Rectangle((0,0), 1, 1, fc="red")
green_proxy = plt.Rectangle((0,0), 1, 1, fc="green")

# Add legend
ax.legend([blue_proxy, red_proxy, green_proxy], 
          ["Original Hexagons", "Shifted Hexagons", "Ensemble Results"],
          handler_map={plt.Rectangle: HandlerPatch()})


plt.show()

# Create 3D plot fig = plt.figure(figsize = (15,9)) ax = fig.add_subplot(111, projection='3d') # Set view angle ax.view_init(elev=None, azim=45) # Plot polygons on different z levels for idx in [0,1]: poly1 = origin_hexgaon.geometry[idx] x = poly1.centroid.x y = poly1.centroid.y xx_lst, yy_lst = poly1.exterior.coords.xy poly2 = translate(poly1,xoff=(x - xx_lst[0]),yoff=(y - yy_lst[0])) poly3 = translate(poly1,xoff=(x - xx_lst[1]),yoff=(y - yy_lst[1])) poly4 = translate(poly1,xoff=(x - xx_lst[2]),yoff=(y - yy_lst[2])) poly5 = translate(poly1,xoff=(x - xx_lst[3]),yoff=(y - yy_lst[3])) poly6 = translate(poly1,xoff=(x - xx_lst[4]),yoff=(y - yy_lst[4])) poly7 = translate(poly1,xoff=(x - xx_lst[5]),yoff=(y - yy_lst[5])) ax.plot(*poly1.exterior.xy, zs=1, zdir='z', color='blue') ax.plot(*poly1.exterior.xy, zs=0.5, zdir='z', color='blue') ax.plot(*poly2.exterior.xy, zs=0.5, zdir='z', color='red') ax.plot(*poly3.exterior.xy, zs=0.5, zdir='z', color='red') ax.plot(*poly4.exterior.xy, zs=0.5, zdir='z', color='red') ax.plot(*poly5.exterior.xy, zs=0.5, zdir='z', color='red') ax.plot(*poly6.exterior.xy, zs=0.5, zdir='z', color='red') ax.plot(*poly7.exterior.xy, zs=0.5, zdir='z', color='red') # Get intersection ax.plot(*poly1.intersection(poly2).intersection(poly3).exterior.xy, zs=0, zdir='z', color='green') ax.plot(*poly1.intersection(poly3).intersection(poly4).exterior.xy, zs=0, zdir='z', color='green') ax.plot(*poly1.intersection(poly4).intersection(poly5).exterior.xy, zs=0, zdir='z', color='green') ax.plot(*poly1.intersection(poly5).intersection(poly6).exterior.xy, zs=0, zdir='z', color='green') ax.plot(*poly1.intersection(poly6).intersection(poly7).exterior.xy, zs=0, zdir='z', color='green') ax.plot(*poly1.intersection(poly7).intersection(poly2).exterior.xy, zs=0, zdir='z', color='green') # Set axis limits ax.set_zlim(0,1) # Remove x tick labels ax.set_xticks([]) # Remove y ticks and labels ax.set_yticks([]) # Enable y grid lines ax.yaxis.grid(True) # Create proxy artists blue_proxy = plt.Rectangle((0,0), 1, 1, fc="blue") red_proxy = plt.Rectangle((0,0), 1, 1, fc="red") green_proxy = plt.Rectangle((0,0), 1, 1, fc="green") # Add legend ax.legend([blue_proxy, red_proxy, green_proxy], ["Original Hexagons", "Shifted Hexagons", "Ensemble Results"], handler_map={plt.Rectangle: HandlerPatch()}) plt.show()

Execute shift to all hexagons¶

In [7]:

def shift_hexagon(row, index = 0):
    x,y = row.geometry.centroid.x, row.geometry.centroid.y
    xx_lst, yy_lst = row.geometry.exterior.coords.xy
    return translate(row.geometry,xoff=(x - xx_lst[index]),yoff=(y - yy_lst[index]))

def shift_hexagon(row, index = 0): x,y = row.geometry.centroid.x, row.geometry.centroid.y xx_lst, yy_lst = row.geometry.exterior.coords.xy return translate(row.geometry,xoff=(x - xx_lst[index]),yoff=(y - yy_lst[index]))

In [8]:

temp = origin_hexgaon.loc[:2,:]
fig, ax = plt.subplots()
ax.plot(*temp['geometry'][0].exterior.xy, color="blue")

for i in range(6):
    temp_1 = temp.copy()
    temp_1['geometry'] = temp.apply(lambda x: shift_hexagon(x,i),axis=1) 
    ax.plot(*temp_1['geometry'][0].exterior.xy, color="red")

plt.show()

temp = origin_hexgaon.loc[:2,:] fig, ax = plt.subplots() ax.plot(*temp['geometry'][0].exterior.xy, color="blue") for i in range(6): temp_1 = temp.copy() temp_1['geometry'] = temp.apply(lambda x: shift_hexagon(x,i),axis=1) ax.plot(*temp_1['geometry'][0].exterior.xy, color="red") plt.show()

In [9]:

# Setup plot 
fig, ax = plt.subplots()
ax.plot(*origin_hexgaon.geometry[0].exterior.xy,color = "blue")

for idx in list(range(0,6)):
    shift_hexagon_gdf = origin_hexgaon.copy()
    shift_hexagon_gdf['geometry'] = origin_hexgaon.apply(lambda x: shift_hexagon(x,idx),axis=1)
    ax.plot(*shift_hexagon_gdf['geometry'][0].exterior.xy, color = "red")
    # intersections = intersect_with_poi(shift_hexagon_gdf)
    # print(f'{idx} : {intersections.shape[0]}')
    out_name = f"../../Data/Output/Study_Area_{idx}.shp"
    print(out_name)
    shift_hexagon_gdf.to_file(out_name)
    
# Show plot
plt.show()

# Setup plot fig, ax = plt.subplots() ax.plot(*origin_hexgaon.geometry[0].exterior.xy,color = "blue") for idx in list(range(0,6)): shift_hexagon_gdf = origin_hexgaon.copy() shift_hexagon_gdf['geometry'] = origin_hexgaon.apply(lambda x: shift_hexagon(x,idx),axis=1) ax.plot(*shift_hexagon_gdf['geometry'][0].exterior.xy, color = "red") # intersections = intersect_with_poi(shift_hexagon_gdf) # print(f'{idx} : {intersections.shape[0]}') out_name = f"../../Data/Output/Study_Area_{idx}.shp" print(out_name) shift_hexagon_gdf.to_file(out_name) # Show plot plt.show()

../../Data/Output/Study_Area_0.shp
../../Data/Output/Study_Area_1.shp
../../Data/Output/Study_Area_2.shp
../../Data/Output/Study_Area_3.shp
../../Data/Output/Study_Area_4.shp
../../Data/Output/Study_Area_5.shp

Calculate POI and Social events presence¶

In [12]:

poi = gpd.read_file("../../Data/POI/SafeGraph_POI/SafeGraph_Category_DFW.shp")
events = gpd.read_file("../../Data/Social-Events/events_in_dfw_reclass.shp")
print(poi.shape)
print(events.shape)

poi = gpd.read_file("../../Data/POI/SafeGraph_POI/SafeGraph_Category_DFW.shp") events = gpd.read_file("../../Data/Social-Events/events_in_dfw_reclass.shp") print(poi.shape) print(events.shape)

(102920, 14)
(9445, 22)

In [58]:

# Get unique POI and event types
poi_types = poi['Category'].unique()
event_types = events['new_cate'].unique()
poi_types,event_types

# Get unique POI and event types poi_types = poi['Category'].unique() event_types = events['new_cate'].unique() poi_types,event_types

Out[58]:

(array(['Retail Trade', 'Professional, Scientific, and Technical Services',
        'Health Care and Social Assistance',
        'Other Services (except Public Administration)',
        'Finance and Insurance', 'Accommodation and Food Services',
        'Educational Services', 'Real Estate and Rental and Leasing',
        'Arts, Entertainment, and Recreation',
        'Transportation and Warehousing', 'Manufacturing',
        'Administrative and Support and Waste Management and Remediation Services',
        'Information', 'Public Administration', 'Wholesale Trade',
        'Construction', 'Utilities',
        'Management of Companies and Enterprises',
        'Agriculture, Forestry, Fishing and Hunting'], dtype=object),
 array(['Hobbies & Passions', 'Religon & Identity', 'Science & Education',
        'Career & Business', 'Social Activities', 'Spots & Health',
        'Movements'], dtype=object))

In [59]:

# Spatial joins 
origin_hexgaon = origin_hexgaon.to_crs(poi.crs)
poi_joined = gpd.sjoin(poi,origin_hexgaon, how='left', predicate='intersects')
events_joined = gpd.sjoin(events,origin_hexgaon, how='left', predicate='intersects')
poi_joined.shape

# Spatial joins origin_hexgaon = origin_hexgaon.to_crs(poi.crs) poi_joined = gpd.sjoin(poi,origin_hexgaon, how='left', predicate='intersects') events_joined = gpd.sjoin(events,origin_hexgaon, how='left', predicate='intersects') poi_joined.shape

Out[59]:

(102920, 16)

In [83]:

result  = origin_hexgaon.to_crs(poi.crs).copy()
for event_type in event_types:
    result[event_type] = 0

for poi_type in poi_types:
    result[poi_type] = 0
result.shape

for row in poi_joined.iloc:
    if(math.isnan(row['index_right'])):
        pass
    else:
        result.loc[int(row['index_right']),(row['Category'])] += 1
        
for row in events_joined.iloc:
    if(math.isnan(row['index_right'])):
        pass
    else:
        result.loc[int(row['index_right']),(row['new_cate'])] += 1

result.head()

result = origin_hexgaon.to_crs(poi.crs).copy() for event_type in event_types: result[event_type] = 0 for poi_type in poi_types: result[poi_type] = 0 result.shape for row in poi_joined.iloc: if(math.isnan(row['index_right'])): pass else: result.loc[int(row['index_right']),(row['Category'])] += 1 for row in events_joined.iloc: if(math.isnan(row['index_right'])): pass else: result.loc[int(row['index_right']),(row['new_cate'])] += 1 result.head()

Out[83]:

	label_num	geometry	Hobbies & Passions	Religon & Identity	Science & Education	Career & Business	Social Activities	Spots & Health	Movements	Retail Trade	...	Transportation and Warehousing	Manufacturing	Administrative and Support and Waste Management and Remediation Services	Information	Public Administration	Wholesale Trade	Construction	Utilities	Management of Companies and Enterprises	Agriculture, Forestry, Fishing and Hunting
0	0	POLYGON ((746003.453 2051234.240, 745803.453 2...	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	0	POLYGON ((746603.453 2050887.830, 746403.453 2...	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	POLYGON ((747203.453 2051234.240, 747003.453 2...	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	POLYGON ((746603.453 2051580.650, 746403.453 2...	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	0	POLYGON ((747203.453 2051927.060, 747003.453 2...	0	0	0	0	0	0	0	1	...	0	0	1	0	1	0	0	0	0	0

5 rows × 28 columns

In [84]:

result[list(poi_types) + list(event_types)].describe()

result[list(poi_types) + list(event_types)].describe()

Out[84]:

	Retail Trade	Professional, Scientific, and Technical Services	Health Care and Social Assistance	Other Services (except Public Administration)	Finance and Insurance	Accommodation and Food Services	Educational Services	Real Estate and Rental and Leasing	Arts, Entertainment, and Recreation	Transportation and Warehousing	...	Utilities	Management of Companies and Enterprises	Agriculture, Forestry, Fishing and Hunting	Hobbies & Passions	Religon & Identity	Science & Education	Career & Business	Social Activities	Spots & Health	Movements
count	22504.000000	22504.000000	22504.000000	22504.000000	22504.000000	22504.000000	22504.000000	22504.00000	22504.00000	22504.000000	...	22504.000000	22504.000000	22504.000000	22504.000000	22504.000000	22504.000000	22504.000000	22504.000000	22504.000000	22504.000000
mean	1.110158	0.082341	0.791326	0.751155	0.327186	0.717250	0.157261	0.12069	0.22947	0.055012	...	0.004621	0.000355	0.000311	0.113846	0.037371	0.058478	0.038215	0.046170	0.100249	0.021241
std	3.235405	0.363075	2.925432	2.085968	1.133805	2.310554	0.512322	0.49439	0.69607	0.266807	...	0.067825	0.018852	0.017634	1.810420	0.838132	1.120660	0.768530	0.864222	1.479839	0.528604
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	1.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.00000	0.00000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	123.000000	9.000000	167.000000	42.000000	25.000000	65.000000	9.000000	10.00000	13.00000	6.000000	...	1.000000	1.000000	1.000000	92.000000	67.000000	65.000000	45.000000	58.000000	63.000000	28.000000

8 rows × 26 columns

In [91]:

column_mapping = {
    'Career & Business': 'CareerBiz',
    'Hobbies & Passions': 'HobbiesPas', 
    'Movements': 'Movements',
    'Religon & Identity': 'ReligionId',
    'Science & Education': 'SciEdu',
    'Social Activities': 'SocialAct',
    'Spots & Health': 'SportHeal', 
    'Accommodation and Food Services': 'AccomFood',
    'Administrative and Support and Waste Management and Remediation Services': 'AdminSup',
    'Agriculture, Forestry, Fishing and Hunting': 'AgriFish', 
    'Arts, Entertainment, and Recreation': 'ArtsRec',
    'Construction': 'Construct',
    'Educational Services': 'EduSvcs', 
    'Finance and Insurance': 'FinanceIns',
    'Health Care and Social Assistance': 'HealthCare',
    'Information': 'Info',
    'Management of Companies and Enterprises': 'MgmtEnterp',
    'Manufacturing': 'Manufac',
    'Other Services (except Public Administration)': 'OtherSvcs',
    'Professional, Scientific, and Technical Services': 'ProfSvcs',
    'Public Administration': 'PubAdmin',
    'Real Estate and Rental and Leasing': 'RealEstate',
    'Retail Trade': 'Retail',
    'Transportation and Warehousing': 'TransWare',
    'Utilities': 'Utilities',
    'Wholesale Trade': 'Wholesale'
}
# Rename the columns using the column_mapping dictionary
result.rename(columns=column_mapping, inplace=True)

result.head()

column_mapping = { 'Career & Business': 'CareerBiz', 'Hobbies & Passions': 'HobbiesPas', 'Movements': 'Movements', 'Religon & Identity': 'ReligionId', 'Science & Education': 'SciEdu', 'Social Activities': 'SocialAct', 'Spots & Health': 'SportHeal', 'Accommodation and Food Services': 'AccomFood', 'Administrative and Support and Waste Management and Remediation Services': 'AdminSup', 'Agriculture, Forestry, Fishing and Hunting': 'AgriFish', 'Arts, Entertainment, and Recreation': 'ArtsRec', 'Construction': 'Construct', 'Educational Services': 'EduSvcs', 'Finance and Insurance': 'FinanceIns', 'Health Care and Social Assistance': 'HealthCare', 'Information': 'Info', 'Management of Companies and Enterprises': 'MgmtEnterp', 'Manufacturing': 'Manufac', 'Other Services (except Public Administration)': 'OtherSvcs', 'Professional, Scientific, and Technical Services': 'ProfSvcs', 'Public Administration': 'PubAdmin', 'Real Estate and Rental and Leasing': 'RealEstate', 'Retail Trade': 'Retail', 'Transportation and Warehousing': 'TransWare', 'Utilities': 'Utilities', 'Wholesale Trade': 'Wholesale' } # Rename the columns using the column_mapping dictionary result.rename(columns=column_mapping, inplace=True) result.head()

Out[91]:

	label_num	geometry	HobbiesPas	ReligionId	SciEdu	CareerBiz	SocialAct	SportHeal	Movements	Retail	...	TransWare	Manufac	AdminSup	Info	PubAdmin	Wholesale	Construct	Utilities	MgmtEnterp	AgriFish
0	0	POLYGON ((746003.453 2051234.240, 745803.453 2...	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	0	POLYGON ((746603.453 2050887.830, 746403.453 2...	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	POLYGON ((747203.453 2051234.240, 747003.453 2...	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	POLYGON ((746603.453 2051580.650, 746403.453 2...	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	0	POLYGON ((747203.453 2051927.060, 747003.453 2...	0	0	0	0	0	0	0	1	...	0	0	1	0	1	0	0	0	0	0

5 rows × 28 columns

In [92]:

result.columns

result.columns

Out[92]:

Index(['label_num', 'geometry', 'HobbiesPas', 'ReligionId', 'SciEdu',
       'CareerBiz', 'SocialAct', 'SportHeal', 'Movements', 'Retail',
       'ProfSvcs', 'HealthCare', 'OtherSvcs', 'FinanceIns', 'AccomFood',
       'EduSvcs', 'RealEstate', 'ArtsRec', 'TransWare', 'Manufac', 'AdminSup',
       'Info', 'PubAdmin', 'Wholesale', 'Construct', 'Utilities', 'MgmtEnterp',
       'AgriFish'],
      dtype='object')

In [85]:

# Define a function to transform values
def transform_value(value):
    return 1 if value > 0 else 0

# Apply the transformation to the DataFrame
result.loc[:,(list(poi_types) + list(event_types))] = result.loc[:,(list(poi_types) + list(event_types))].applymap(transform_value)

# Define a function to transform values def transform_value(value): return 1 if value > 0 else 0 # Apply the transformation to the DataFrame result.loc[:,(list(poi_types) + list(event_types))] = result.loc[:,(list(poi_types) + list(event_types))].applymap(transform_value)

In [86]:

for cate in list(poi_types) + list(event_types):
    print(cate)
    print(result[cate].value_counts())

for cate in list(poi_types) + list(event_types): print(cate) print(result[cate].value_counts())

Retail Trade
Retail Trade
0    15688
1     6816
Name: count, dtype: int64
Professional, Scientific, and Technical Services
Professional, Scientific, and Technical Services
0    21079
1     1425
Name: count, dtype: int64
Health Care and Social Assistance
Health Care and Social Assistance
0    17069
1     5435
Name: count, dtype: int64
Other Services (except Public Administration)
Other Services (except Public Administration)
0    16620
1     5884
Name: count, dtype: int64
Finance and Insurance
Finance and Insurance
0    19165
1     3339
Name: count, dtype: int64
Accommodation and Food Services
Accommodation and Food Services
0    17668
1     4836
Name: count, dtype: int64
Educational Services
Educational Services
0    19890
1     2614
Name: count, dtype: int64
Real Estate and Rental and Leasing
Real Estate and Rental and Leasing
0    20665
1     1839
Name: count, dtype: int64
Arts, Entertainment, and Recreation
Arts, Entertainment, and Recreation
0    19198
1     3306
Name: count, dtype: int64
Transportation and Warehousing
Transportation and Warehousing
0    21439
1     1065
Name: count, dtype: int64
Manufacturing
Manufacturing
0    21716
1      788
Name: count, dtype: int64
Administrative and Support and Waste Management and Remediation Services
Administrative and Support and Waste Management and Remediation Services
0    21629
1      875
Name: count, dtype: int64
Information
Information
0    21411
1     1093
Name: count, dtype: int64
Public Administration
Public Administration
0    21725
1      779
Name: count, dtype: int64
Wholesale Trade
Wholesale Trade
0    21868
1      636
Name: count, dtype: int64
Construction
Construction
0    22326
1      178
Name: count, dtype: int64
Utilities
Utilities
0    22400
1      104
Name: count, dtype: int64
Management of Companies and Enterprises
Management of Companies and Enterprises
0    22496
1        8
Name: count, dtype: int64
Agriculture, Forestry, Fishing and Hunting
Agriculture, Forestry, Fishing and Hunting
0    22497
1        7
Name: count, dtype: int64
Hobbies & Passions
Hobbies & Passions
0    22231
1      273
Name: count, dtype: int64
Religon & Identity
Religon & Identity
0    22374
1      130
Name: count, dtype: int64
Science & Education
Science & Education
0    22344
1      160
Name: count, dtype: int64
Career & Business
Career & Business
0    22374
1      130
Name: count, dtype: int64
Social Activities
Social Activities
0    22268
1      236
Name: count, dtype: int64
Spots & Health
Spots & Health
0    22212
1      292
Name: count, dtype: int64
Movements
Movements
0    22421
1       83
Name: count, dtype: int64

Execute for all shift area¶

In [93]:

for idx in list(range(0,6)):
    input_file = f"../../Data/Output/Study_Area_{idx}.shp"
    print(input_file)
    temp = gpd.read_file(input_file)
    # Spatial joins 
    temp = temp.to_crs(poi.crs)
    poi_joined = gpd.sjoin(poi,temp, how='left', predicate='intersects')
    events_joined = gpd.sjoin(events,temp, how='left', predicate='intersects')
    
    for event_type in event_types:
        temp[event_type] = 0

    for poi_type in poi_types:
        temp[poi_type] = 0
        
    for row in poi_joined.iloc:
        if(math.isnan(row['index_right'])):
            pass
        else:
            temp.loc[int(row['index_right']),(row['Category'])] += 1
            
    for row in events_joined.iloc:
        if(math.isnan(row['index_right'])):
            pass
        else:
            temp.loc[int(row['index_right']),(row['new_cate'])] += 1
            
    # Apply the transformation to the DataFrame
    temp.loc[:,(list(poi_types) + list(event_types))] = temp.loc[:,(list(poi_types) + list(event_types))].applymap(transform_value)
    
    # Rename the columns using the column_mapping dictionary
    temp.rename(columns=column_mapping, inplace=True)
    
    out_name = f"../../Data/Shift/Event_POI_occur_{idx}.shp"
    print(out_name)
    temp.to_file(out_name)

for idx in list(range(0,6)): input_file = f"../../Data/Output/Study_Area_{idx}.shp" print(input_file) temp = gpd.read_file(input_file) # Spatial joins temp = temp.to_crs(poi.crs) poi_joined = gpd.sjoin(poi,temp, how='left', predicate='intersects') events_joined = gpd.sjoin(events,temp, how='left', predicate='intersects') for event_type in event_types: temp[event_type] = 0 for poi_type in poi_types: temp[poi_type] = 0 for row in poi_joined.iloc: if(math.isnan(row['index_right'])): pass else: temp.loc[int(row['index_right']),(row['Category'])] += 1 for row in events_joined.iloc: if(math.isnan(row['index_right'])): pass else: temp.loc[int(row['index_right']),(row['new_cate'])] += 1 # Apply the transformation to the DataFrame temp.loc[:,(list(poi_types) + list(event_types))] = temp.loc[:,(list(poi_types) + list(event_types))].applymap(transform_value) # Rename the columns using the column_mapping dictionary temp.rename(columns=column_mapping, inplace=True) out_name = f"../../Data/Shift/Event_POI_occur_{idx}.shp" print(out_name) temp.to_file(out_name)

../../Data/Output/Study_Area_0.shp
../../Data/Shift/Event_POI_occur_0.shp
../../Data/Output/Study_Area_1.shp
../../Data/Shift/Event_POI_occur_1.shp
../../Data/Output/Study_Area_2.shp
../../Data/Shift/Event_POI_occur_2.shp
../../Data/Output/Study_Area_3.shp
../../Data/Shift/Event_POI_occur_3.shp
../../Data/Output/Study_Area_4.shp
../../Data/Shift/Event_POI_occur_4.shp
../../Data/Output/Study_Area_5.shp
../../Data/Shift/Event_POI_occur_5.shp