Upload urban_tree_canopy_in_durham.py

urban_tree_canopy_in_durham.py

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+# -*- coding: utf-8 -*-
+"""Urban_Tree_Canopy_in_Durham
+
+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')