Created
June 16, 2019 21:37
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| # METHOD 1: INDEX ON GRID, LOOP THROUGH ISOCHRONES | |
| # Add columns of zero and empty dictionaries | |
| grid_copy['count_unique'] = 0 | |
| grid_copy['sum_times'] = 0 | |
| empty = {key: np.inf for key in isochrones.origin.unique()} | |
| grid_copy['unique_ids'] = [[] for i in range(len(grid_copy))] | |
| grid_copy['min_times'] = [{key: np.inf for key in isochrones.origin.unique()} for i in range(len(grid_copy))] | |
| spatial_index = grid_copy.sindex | |
| for idx, isochrone in isochrones.iterrows(): | |
| possible_matches_index = list(spatial_index.intersection(isochrone.geometry.bounds)) | |
| possible_matches = grid_copy.iloc[possible_matches_index] | |
| precise_matches = possible_matches[possible_matches.intersects(isochrone.geometry)] | |
| grid_copy.iloc[precise_matches.index].apply(lambda x: x['unique_ids'].append(isochrone.origin), axis=1) | |
| grid_copy.iloc[precise_matches.index].apply(lambda x: x['min_times'].update({isochrone.origin:isochrone.time}) if x['min_times'][isochrone.origin] > isochrone.time else x, axis=1) | |
| grid_copy['sum_times'] = grid_copy.apply(lambda x: sum(x['min_times'].values()), axis=1) | |
| grid_copy['count_unique'] = grid_copy.apply(lambda x: len(set(x['unique_ids'])), axis=1) | |
| # METHOD 2: GEOPANDAS SPATIAL JOIN | |
| spatial_join = gpd.sjoin(grid, isochrones, how='left') | |
| min_times = spatial_join.groupby([spatial_join.index, 'origin'])['time'].min() | |
| total_times = min_times.groupby(level=[0]).sum() | |
| count_unique = min_times.groupby(level=[0]).count() | |
| # Add columns of zero | |
| grid['count_unique'] = 0 | |
| grid['sum_times'] = 0 | |
| grid.loc[count_unique.index, 'count_unique'] = count_unique | |
| grid.loc[total_times.index, 'sum_times'] = total_times | |
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