# -*- coding: utf-8 -*- """Urban_Tree_Canopy_in_Durham2 Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1X59zPtI7ydiX10ZnfjsNGvnKNTXgwrWs """ ! pip install datasets import csv import json import os from typing import List import datasets import logging from datasets import DatasetBuilder, DownloadManager, SplitGenerator, Split import zipfile import json import pandas as pd import geopandas as gpd class Urban_Tree_Canopy_in_Durham(DatasetBuilder): # Define the `_info` method, which provides dataset metadata def _info(self): return DatasetInfo( description="A description of the dataset.", features=Features( { "objectid": Value("int32"), "streetaddr": Value("string"), "city_x": Value("string"), "zipcode_x": Value("string"), "facilityid_x": Value("string"), "present_x": Value("string"), "genus_x": Value("string"), "species_x": Value("string"), "commonname_x": Value("string"), "plantingda": Value("datetime"), "diameterin_x": Value("float"), "heightft_x": Value("float"), "condition_x": Value("string"), "contractwo": Value("string"), "neighborho": Value("string"), "program_x": Value("string"), "plantingw_x": Value("string"), "plantingco": Value("string"), "underpwerl": Value("string"), "matureheig": Value("float"), "globalid_x": Value("string"), "created_us": Value("string"), "created_da": Value("datetime"), "last_edite": Value("string"), "last_edi_1": Value("datetime"), "isoprene_x": Value("float"), "monoterpen": Value("float"), "vocs_x": Value("float"), "coremoved_": Value("float"), "coremove_1": Value("float"), "o3removed_": Value("float"), "o3remove_1": Value("float"), "no2removed": Value("float"), "no2remov_1": Value("float"), "so2removed": Value("float"), "so2remov_1": Value("float"), "pm10remove": Value("float"), "pm10remo_1": Value("float"), "pm25remove": Value("float"), "o2producti": Value("float"), "replaceval": Value("float"), "carbonstor": Value("float"), "carbonst_1": Value("float"), "grosscarse": Value("float"), "grosscar_1": Value("float"), "avoidrunof": Value("float"), "avoidrun_1": Value("float"), "polremoved": Value("float"), "polremov_1": Value("float"), "totannbene": Value("float"), "leafarea_s": Value("float"), "potevapotr": Value("float"), "evaporatio": Value("float"), "transpirat": Value("float"), "h2ointerce": Value("float"), "avoidrunva": Value("float"), "avoidrun_2": Value("float"), "carbonavoi": Value("float"), "carbonav_1": Value("float"), "heating_mb": Value("float"), "heating_do": Value("float"), "heating_kw": Value("float"), "heating__1": Value("float"), "cooling_kw": Value("float"), "cooling_do": Value("float"), "totalenerg": Value("float"), "geometry_x": Value("string"), "x": Value("float"), "y": Value("float"), "streetaddress_x": Value("string"), "city_y": Value("string"), "zipcode_y": Value("string"), "facilityid_y": Value("string"), "present_y": Value("string"), "genus_y": Value("string"), "species_y": Value("string"), "commonname_y": Value("string"), "plantingdate_x": Value("datetime"), "diameterin_y": Value("float"), "heightft_y": Value("float"), "condition_y": Value("string"), "contractwork_x": Value("string"), "neighborhood_x": Value("string"), "program_y": Value("string"), "plantingw_y": Value("string"), "plantingcond_x": Value("string"), "underpwerlins_x": Value("string"), "matureheight_x": Value("float"), "globalid_y": Value("string"), "created_user_x": Value("string"), "created_date_x": Value("datetime"), "last_edited_user_x": Value("string"), "last_edited_date_x": Value("datetime"), "isoprene_y": Value("float"), "monoterpene_x": Value("float"), "vocs_y": Value("float"), "coremoved_ozperyr_x": Value("float"), "coremoved_dolperyr_x": Value("float"), "o3removed_ozperyr_x": Value("float"), "o3removed_dolperyr_x": Value("float"), "no2removed_ozperyr_x": Value("float"), "no2removed_dolperyr_x": Value("float"), "so2removed_ozperyr_x": Value("float"), "so2removed_dolperyr_x": Value("float"), "pm10removed_dolperyr_y":Value("float"), "pm25removed_ozperyr_y":Value("float"), "o2production_lbperyr_y":Value("float"), "replacevalue_dol_y":Value("float"), "carbonstorage_lb_y":Value("float"), "carbonstorage_dol_y":Value("float"), "grosscarseq_lbperyr_y":Value("float"), "grosscarseq_dolperyr_y":Value("float"), "avoidrunoff_ft2peryr":Value("float"), } ), supervised_keys=None, homepage="https://github.com/AuraMa111/Urban_Tree_Canopy_in_Durham", citation="A citation or reference to the source of the dataset.", ) # ... (include _info method here) def _split_generators(self, dl_manager: DownloadManager): def _split_generators(self, dl_manager: tfds.download.DownloadManager): # Download the source data downloaded_files = dl_manager.download_and_extract({ "csv": "https://raw.githubusercontent.com/AuraMa111/Urban_Tree_Canopy_in_Durham/main/Trees_%2526_Planting_Sites.csv", "geojson_zip": "https://raw.githubusercontent.com/AuraMa111/Urban_Tree_Canopy_in_Durham/main/Trees_%2526_Planting_Sites.geojson.zip", "zip": "https://raw.githubusercontent.com/AuraMa111/Urban_Tree_Canopy_in_Durham/main/TreesPlanting_Sites.zip" }) # Return split generators return [ tfds.core.SplitGenerator( name=tfds.Split.TRAIN, gen_kwargs={ "file_path_csv": downloaded_files["csv"], "file_path_zip": downloaded_files["zip"], "file_path_geojson_zip": downloaded_files["geojson_zip"], }, ), # Add other splits if necessary ] def _generate_examples(self, file_path_csv, file_path_zip, file_path_geojson_zip): # Generate examples from CSV csv_df = self.process_csv_file(file_path_csv) # Generate examples from Shapefiles within ZIP shp_gdf = self.process_zip_shapefiles(file_path_zip) # Generate examples from GeoJSON within ZIP geojson_gdf = self.process_zip_geojson(file_path_geojson_zip) # Merge the DataFrames combined_gdf = self.merge_dataframes(csv_df, shp_gdf, geojson_gdf) # Generate final examples for idx, example in self.generate_examples_from_merged_data(combined_gdf): yield idx, example def process_csv_file(self, file_path): with open(file_path, 'r') as f: csv_df = pd.read_csv(f) csv_df.drop_duplicates(inplace=True) csv_df.fillna(method='bfill', inplace=True) csv_df.columns = csv_df.columns.str.lower().str.replace(' ', '_') csv_df['objectid'] = csv_df['objectid'].astype(int) return csv_df def process_zip_shapefiles(self, file_path): with zipfile.ZipFile(file_path, 'r') as z: for file_name in z.namelist(): if file_name.endswith(".shp"): with z.open(file_name) as file: shp_gdf = gpd.read_file(file) shp_gdf.columns = shp_gdf.columns.str.lower().str.replace(' ', '_') shp_gdf['objectid'] = shp_gdf['objectid'].astype(int) return shp_gdf def process_zip_geojson(self, file_path): with zipfile.ZipFile(file_path, 'r') as z: for file_name in z.namelist(): if file_name.endswith(".geojson"): with z.open(file_name) as file: geojson_data = json.load(file) geojson_gdf = gpd.GeoDataFrame.from_features(geojson_data['features']) geojson_gdf.columns = geojson_gdf.columns.str.lower().str.replace(' ', '_') geojson_gdf['objectid'] = geojson_gdf['objectid'].astype(int) return geojson_gdf def merge_dataframes(self, csv_df, shp_gdf, geojson_gdf): combined_gdf = shp_gdf.merge(csv_df, on='objectid', how='inner') combined_gdf = combined_gdf.merge(geojson_gdf, on='objectid', how='left') return combined_gdf def generate_examples_from_merged_data(self, combined_gdf): for idx, row in combined_gdf.iterrows(): example = row.to_dict() if 'geometry' in row and row['geometry'] is not None: example['geometry'] = json.loads(gpd.GeoSeries([row['geometry']]).to_json())['features'][0]['geometry'] yield idx, example def plot_spatial_distribution(self, gdf, lat_col='latitude', lon_col='longitude', color_col='species', hover_col='species'): """ Visualize the spatial distribution of the data using Plotly. Parameters: - gdf: GeoDataFrame to be visualized. - lat_col: String, name of the column with latitude values. - lon_col: String, name of the column with longitude values. - color_col: String, name of the column to determine the color of points. - hover_col: String, name of the column to show when hovering over points. """ center_lat = gdf[lat_col].mean() center_lon = gdf[lon_col].mean() fig = px.scatter_mapbox(gdf, lat=lat_col, lon=lon_col, color=color_col, hover_name=hover_col, center={"lat": center_lat, "lon": center_lon}, zoom=10, height=600, width=800) fig.update_layout(mapbox_style="open-street-map") fig.show() def plot_correlation_heatmap(self, gdf, columns, figsize=(10, 8), cmap='coolwarm'): """ Plot a heatmap of the correlation matrix for selected columns in the GeoDataFrame. Parameters: - gdf: GeoDataFrame containing the data. - columns: List of columns to include in the correlation matrix. - figsize: Tuple of figure size dimensions (width, height). - cmap: Colormap for the heatmap. """ # Select only the columns with environmental data env_data = gdf[columns] # Compute the correlation matrix corr = env_data.corr() # Set up the matplotlib figure plt.figure(figsize=figsize) # Generate a heatmap sns.heatmap(corr, annot=True, fmt=".2f", cmap=cmap, square=True, linewidths=.5, cbar_kws={"shrink": .5}) # Optional: Adjust the layout plt.tight_layout() # Show the plot plt.show() # Usage example: # data_processor = DataProcessor() # for key, example in data_processor._generate_examples(csv_path, zip_path, geojson_zip_path): # # Do something with key and example # Usage example: # data_processor = DataProcessor() # for key, example in data_processor._generate_examples(csv_path, zip_path, geojson_zip_path): # # Do something with key and example # combined_gdf = data_processor.merge_dataframes(csv_df, shp_gdf, geojson_gdf) # data_processor.plot_spatial_distribution(combined_gdf, lat_col='y', lon_col='x', color_col='species_x', hover_col='species_x')