Python implements spatial connection and geographic computing through Geopandas

# Install the necessary libraries!pip install geopandas matplotlib contextily rtree shapely
# Import libraryimport geopandas as gpd
import pandas as pd
import numpy as np
import  as plt
import contextily as ctx
from  import Point, LineString, Polygon

# Load administrative division datadistricts = gpd.read_file("")
# Create POI point datapoi_data = {
    'name': ['Coffee Shop A', 'Restaurant B', 'Mall C', 'School D', 'Hospital E', 'Park F', 'Library G', 'Supermarket H'],
    'type': ['FOOD', 'FOOD', 'Shopping', 'educate', 'Medical', 'Career', 'culture', 'Shopping'],
    'latitude': [39.91, 39.92, 39.93, 39.89, 39.91, 39.88, 39.90, 39.94],
    'longitude': [116.41, 116.42, 116.39, 116.38, 116.44, 116.43, 116.41, 116.40]
}
# Create point geometrygeometry = [Point(xy) for xy in zip(poi_data['longitude'], poi_data['latitude'])]
pois = (poi_data, geometry=geometry, crs="EPSG:4326")

# Ensure the coordinate system is consistentdistricts = districts.to_crs()
# Perform spatial connection - where is the point locatedjoined_data = (pois, districts, how="left", predicate="within")
print(f"The connected data contains {len(joined_data)} OK，This includes point information and area")

# Draw the bottom mapfig, ax = (figsize=(12, 10))
# Draw the area boundaries(ax=ax, color='black', linewidth=1)
# Use categorical variables to draw different types of POIs(ax=ax, column='type', cmap='viridis', 
          legend=True, markersize=100, categorical=True)
# Add a title('POI distribution and administrative division', fontsize=15)
# Add background imagectx.add_basemap(ax, crs=.to_string(), source=)
plt.tight_layout()
()

# Use different spatial relationships to connectwithin_join = (pois, districts, how="inner", predicate="within")
intersects_join = (pois, districts, how="inner", predicate="intersects")
print(f"withinConnection results: {len(within_join)} OK")
print(f"intersectsConnection results: {len(intersects_join)} OK")
# For point data, "within" and "intersects" usually produce the same results# But for line or surface data it is different

# Filter specific types of POIs and determine their administrative regionsrestaurant_pois = pois[pois['type'] == 'FOOD']
restaurant_districts = (restaurant_pois, districts, how="left", predicate="within")
# Statistics the number of catering POIs in each administrative regionpoi_count_by_district = restaurant_districts.groupby('district_name').size()
print("Statistics on the number of catering POIs in each administrative region:")
print(poi_count_by_district)

# Create a 500m buffer for each POI# First convert the data into a projection coordinate system to use meterspois_projected = pois.to_crs(epsg=3857)  # Web Mercator Projectionpois_buffered = pois_projected.copy()
pois_buffered['geometry'] = pois_projected.buffer(500)  # 500m buffer zone# Return to the original coordinate system for displaypois_buffered = pois_buffered.to_crs()
# Draw the bufferfig, ax = (figsize=(12, 10))
(ax=ax, color='black', linewidth=1)
pois_buffered.plot(ax=ax, alpha=0.5, column='type', categorical=True)
(ax=ax, color='red', markersize=20)
ctx.add_basemap(ax, crs=.to_string(), source=)
('Analysis of 500 meters service scope of POI point', fontsize=15)
plt.tight_layout()
()

# Intersection operation between buffer zone and administrative divisiondistricts_projected = districts.to_crs(epsg=3857)
pois_buffered_projected = pois_buffered.to_crs(epsg=3857)
# Select a specific area for exampletarget_district = districts_projected.iloc[0]
# Calculate the intersection of this area and each POI bufferintersections = []
for idx, poi_buffer in pois_buffered_projected.iterrows():
    if poi_buffer.(target_district.geometry):
        intersection = poi_buffer.(target_district.geometry)
        ({
            'poi_name': poi_buffer['name'],
            'poi_type': poi_buffer['type'],
            'geometry': intersection,
            'area':   # square meters        })
# Create an intersection GeoDataFrameintersections_gdf = (intersections, crs=pois_buffered_projected.crs)
# Calculate coveragedistrict_area = target_district.
total_coverage_area = ([geom['geometry'] for geom in intersections]).unary_union.area
coverage_ratio = total_coverage_area / district_area * 100
print(f"Region name: {target_district['district_name']}")
print(f"Total area: {district_area/1e6:.2f} Square km")
print(f"POIService coverage area: {total_coverage_area/1e6:.2f} Square km")
print(f"Coverage: {coverage_ratio:.2f}%")

# Calculate the distance matrix (in meters) between all POIsdef calculate_distance_matrix(gdf):
    n = len(gdf)
    dist_matrix = ((n, n))
    for i in range(n):
        for j in range(i+1, n):
            dist = [i].([j].geometry)
            dist_matrix[i, j] = dist
            dist_matrix[j, i] = dist
    return (dist_matrix, 
                        index=, 
                        columns=)
# Calculate the distance matrixdistance_matrix = calculate_distance_matrix(pois_projected)
# Find the nearest neighbor of each POInearest_neighbors = {}
for idx in :
    distances = distance_matrix[idx].drop(idx)  # Exclude yourself    nearest = ()
    nearest_neighbors[[idx, 'name']] = {
        'nearest_poi': [nearest, 'name'],
        'distance_meters': ()
    }
print("Nearest neighbor facilities for each POI:")
for poi, info in nearest_neighbors.items():
    print(f"{poi} -> {info['nearest_poi']} (distance: {info['distance_meters']:.2f}rice)")

# Use KDE for hot spot analysisfrom  import gaussian_kde
# Extract POI coordinatesx = pois_projected.
y = pois_projected.
positions = ([x, y])
# Calculate KDEkernel = gaussian_kde(positions)
# Create a grid for evaluationx_grid, y_grid = [():():100j, ():():100j]
positions_grid = ([x_grid.ravel(), y_grid.ravel()])
# Calculate densityz = kernel(positions_grid).reshape(x_grid.shape)
# Visualize the resultsfig, ax = (figsize=(12, 10))
districts_projected.(ax=ax, color='black', linewidth=1)
contour = (x_grid, y_grid, z, cmap='viridis', levels=20, alpha=0.75)
pois_projected.plot(ax=ax, color='red', markersize=20)
(contour, ax=ax, label='density')
('POI Hot Spot Density Analysis', fontsize=15)
plt.tight_layout()
()

from  import DBSCAN
# Extract coordinatescoords = ((pois_projected., pois_projected.)).T
# Use DBSCAN for clusteringclustering = DBSCAN(eps=1000, min_samples=2).fit(coords)  # At least 2 points within a range of 1000 meters form clustering# Add clustering results to original datapois_projected['cluster'] = clustering.labels_
# Visualize clustering resultsfig, ax = (figsize=(12, 10))
districts_projected.(ax=ax, color='black', linewidth=1)
pois_projected.plot(ax=ax, column='cluster', categorical=True, 
                   legend=True, markersize=100)
('POISpatial clustering analysis (DBSCAN)', fontsize=15)
plt.tight_layout()
()
# Statistics the number of POIs for each clustercluster_stats = pois_projected.groupby('cluster').size()
print("Number of POIs in each cluster:")
print(cluster_stats)

# Note: This requires pre-preparation of road network data and networkx librariesimport networkx as nx
# Loading road network dataroads = gpd.read_file("")
# Create a network diagramG = ()
# Build a network from road datafor idx, road in ():
    coords = list()
    for i in range(len(coords) - 1):
        G.add_edge(coords[i], coords[i+1], 
                  weight=,
                  road_id=road['id'])
# Find the nearest road node to each POInearest_nodes = {}
for idx, poi in pois_projected.iterrows():
    min_dist = float('inf')
    nearest_node = None
    for node in ():
        dist = (Point(node))
        if dist < min_dist:
            min_dist = dist
            nearest_node = node
    nearest_nodes[idx] = nearest_node
# Calculate the shortest path between two POIsorigin_idx = 0  # POI index from the starting pointdest_idx = 3    # Endpoint POI indexorigin_node = nearest_nodes[origin_idx]
dest_node = nearest_nodes[dest_idx]
try:
    # Calculate the shortest path    shortest_path = nx.shortest_path(G, origin_node, dest_node, weight='weight')
    path_length = nx.shortest_path_length(G, origin_node, dest_node, weight='weight')
    print(f"from {[origin_idx, 'name']} arrive {[dest_idx, 'name']} The shortest path length: {path_length:.2f}rice")
    # Visualize the path    path_coords = shortest_path
    path_line = LineString(path_coords)
    path_gdf = ({'geometry': [path_line]}, crs=pois_projected.crs)
    fig, ax = (figsize=(12, 10))
    (ax=ax, color='gray', linewidth=1)
    path_gdf.plot(ax=ax, color='red', linewidth=3)
    pois_projected.iloc[[origin_idx, dest_idx]].plot(ax=ax, color='blue', markersize=100)
    (f'from {[origin_idx, "name"]} arrive {[dest_idx, "name"]} The shortest path to', fontsize=15)
    plt.tight_layout()
    ()
except :
    print(f"无法找arrivefrom {[origin_idx, 'name']} arrive {[dest_idx, 'name']} The path")

# Make sure the rtree library is installed# pip install rtree
# Create a spatial indexdistricts = districts.set_index('district_id', drop=False)
districts_sindex = 
# Use spatial index to speed up querydef fast_sjoin_points_to_polygons(points_gdf, polygons_gdf):
    # Create a result list    results = []
    # traverse points    for idx, point in points_gdf.iterrows():
        # Use spatial index to find possible intersection polygons        possible_matches_idx = list(polygons_gdf.())
        possible_matches = polygons_gdf.iloc[possible_matches_idx]
        # Exactly check whether the point is inside the polygon        precise_matches = possible_matches[possible_matches.contains()]
        # If a match is found        if len(precise_matches) > 0:
            for match_idx, match in precise_matches.iterrows():
                result = point.to_dict()
                ({
                    'district_id': match['district_id'],
                    'district_name': match['district_name']
                })
                (result)
    if results:
        return (results, crs=points_gdf.crs)
    else:
        return ([], crs=points_gdf.crs)
# Use optimized functions for spatial connectionimport time
start_time = ()
optimized_result = fast_sjoin_points_to_polygons(pois, districts)
end_time = ()
print(f"Optimized space connection time-consuming: {end_time - start_time:.4f}Second")
# Compare standard sjoinstart_time = ()
standard_result = (pois, districts, how="left", predicate="within")
end_time = ()
print(f"Time-consuming for standard space connections: {end_time - start_time:.4f}Second")

from joblib import Parallel, delayed
# Parallel processing of buffer calculationsdef parallel_buffer(gdf, buffer_distance, n_jobs=-1):
    # Define a single geometry buffer calculation function    def buffer_geom(geom):
        return (buffer_distance)
    # Parallel execution    buffered_geoms = Parallel(n_jobs=n_jobs)(
        delayed(buffer_geom)(geom) for geom in 
    )
    # Create a new GeoDataFrame    result = ()
    result['geometry'] = buffered_geoms
    return result
# Create a buffer using parallel processingstart_time = ()
pois_buffered_parallel = parallel_buffer(pois_projected, 500)
end_time = ()
print(f"Time-consuming for parallel buffer calculation: {end_time - start_time:.4f}Second")
# Comparison standard processingstart_time = ()
pois_buffered_standard = pois_projected.copy()
pois_buffered_standard['geometry'] = pois_projected.buffer(500)
end_time = ()
print(f"Time-consuming for standard buffer calculation: {end_time - start_time:.4f}Second")

# Assume there is population datapopulation = gpd.read_file("population_grid.geojson")
# Select a transportation facility POItransport_pois = pois[pois['type'].isin(['Bus Station', 'metro station', 'TRAIN STATION'])]
# Create a traffic buffer zone (500 meters walking distance)transport_pois_projected = transport_pois.to_crs(epsg=3857)
transport_buffers = transport_pois_projected.copy()
transport_buffers['geometry'] = transport_pois_projected.buffer(500)
# Spatial connection with population gridpopulation_projected = population.to_crs(epsg=3857)
population_covered = (population_projected, transport_buffers.to_crs(population_projected.crs), 
                              how="inner", predicate="intersects")
# Calculate the total population coveredtotal_population = population_projected['population'].sum()
covered_population = population_covered['population'].sum()
coverage_percentage = (covered_population / total_population) * 100
print(f"Total population: {total_population}")
print(f"Public Transport500Rice covers the population: {covered_population}")
print(f"Coverage: {coverage_percentage:.2f}%")

# Suppose we have the following data layer# - Population density# - Competitor position# - Traffic Accessibility# - Business Center# Create a grid evaluation systemfrom  import box
# Create a study area gridxmin, ymin, xmax, ymax = districts_projected.total_bounds
cell_size = 500  # 500m gridrows = int((ymax - ymin) / cell_size)
cols = int((xmax - xmin) / cell_size)
grid_cells = []
for i in range(cols):
    for j in range(rows):
        x1 = xmin + i * cell_size
        y1 = ymin + j * cell_size
        x2 = xmin + (i + 1) * cell_size
        y2 = ymin + (j + 1) * cell_size
        grid_cells.append(box(x1, y1, x2, y2))
grid = ({'geometry': grid_cells}, crs=districts_projected.crs)
# Calculate the scores of each factor# 1. Population density scoregrid['pop_score'] = (1, 10, size=len(grid))  # Simulate data# 2. Competitor Distance Scorecompetitors = pois_projected[pois_projected['type'] == 'FOOD']
grid['comp_dist'] = (
    lambda g: min([(comp) for comp in ]) if len(competitors) > 0 else 0
)
grid['comp_score'] = grid['comp_dist'] / grid['comp_dist'].max() * 10
# 3. Traffic Accessibility Scoretransit = pois_projected[pois_projected['type'].isin(['Bus Station', 'metro station'])]
grid['transit_dist'] = (
    lambda g: min([(t) for t in ]) if len(transit) > 0 else float('inf')
)
grid['transit_score'] = 10 - (grid['transit_dist'] / grid['transit_dist'].max() * 10)
# 4. Business Center Distance Scorebusiness_centers = pois_projected[pois_projected['type'] == 'Shopping']
grid['biz_dist'] = (
    lambda g: min([(bc) for bc in business_centers.geometry]) if len(business_centers) > 0 else float('inf')
)
grid['biz_score'] = 10 - (grid['biz_dist'] / grid['biz_dist'].max() * 10)
# Calculate comprehensive scoresgrid['total_score'] = (
    grid['pop_score'] * 0.4 + 
    grid['comp_score'] * 0.2 + 
    grid['transit_score'] * 0.2 + 
    grid['biz_score'] * 0.2
)
# Visualize the resultsfig, ax = (figsize=(12, 10))
districts_projected.(ax=ax, color='black', linewidth=1)
(ax=ax, column='total_score', cmap='RdYlGn', alpha=0.7, 
          legend=True, legend_kwds={'label': 'Site selection suitability score'})
pois_projected.plot(ax=ax, color='blue', markersize=10)
('Commercial site selection suitability evaluation', fontsize=15)
plt.tight_layout()
()
# Find the highest scorebest_locations = grid.sort_values('total_score', ascending=False).head(3)
print("The best 3 locations to open a new store:")
for idx, loc in best_locations.iterrows():
    centroid = 
    print(f"Location {idx}: coordinate ({}, {}), Score: {loc['total_score']:.2f}")