Upload urbantreecanopyindurham2dataset.py

urbantreecanopyindurham2dataset.py

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+# -*- coding: utf-8 -*-
+"""UrbanTreeCanopyInDurham2Dataset
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1X59zPtI7ydiX10ZnfjsNGvnKNTXgwrWs
+"""
+
+from datasets import DatasetInfo, Features, Value, load_dataset, BuilderConfig, DatasetBuilder
+import csv
+import json
+import os
+from typing import List
+import datasets
+import logging
+from datasets import DownloadManager, SplitGenerator, Split
+import zipfile
+import pandas as pd
+import geopandas as gpd
+import tempfile
+import shutil
+import plotly.express as px
+from datasets import GeneratorBasedBuilder
+
+class UrbanTreeCanopyInDurham2Dataset(GeneratorBasedBuilder):
+
+    def _info(self):
+        return DatasetInfo(
+            description="Urban_Tree_Canopy_in_Durham2",
+            features=Features(
+                {
+                    "objectid": Value("int32"),
+                    "streetaddr": Value("string"),
+                    "city_x": Value("string"),
+                    "zipcode_x": Value("string"),
+                    "species_x": Value("string"),
+                    "commonname_x": Value("string"),
+                    "plantingda": datasets.Value("timestamp[us]"),
+                    "diameterin_x": Value("float"),
+                    "heightft_x": Value("float"),
+                    "condition_x": Value("string"),
+                    "program_x": Value("string"),
+                    "matureheig": Value("float"),
+                    "created_da": datasets.Value("timestamp[us]"),
+                    "last_edi_1": datasets.Value("timestamp[us]"),
+                    "geometry_x": Value("string"),
+                    "x": Value("float"),
+                    "y": 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"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/AuraMa111/Urban_Tree_Canopy_in_Durham",
+            citation="A citation or reference to the source of the dataset.",
+        )
+
+
+
+    def _split_generators(self, dl_manager):
+        csv_url = "https://drive.google.com/uc?export=download&id=18HmgMbtbntWsvAySoZr4nV1KNu-i7GCy"
+        geojson_url = "https://drive.google.com/uc?export=download&id=1jpFVanNGy7L5tVO-Z_nltbBXKvrcAoDo"
+
+        # Extract the file ID from the SHP Google Drive sharing URL and construct a direct download link
+        shp_file_id = "1DYcug0xiWYlsKZorbbEcrjZWEAB0y4MY"
+        shp_url = f"https://drive.google.com/uc?export=download&id={shp_file_id}"
+
+        # Use dl_manager to download the files
+        csv_path = dl_manager.download_and_extract(csv_url)
+        shp_path = dl_manager.download_and_extract(shp_url)
+        geojson_path = dl_manager.download_and_extract(geojson_url)
+
+        # Assuming the paths are directories, construct the full paths to the files
+        csv_file_path = os.path.join(csv_path, 'Trees_%26_Planting_Sites.csv')
+        shp_file_path = os.path.join(shp_path, 'GS_TreeInventory.shp')  # Adjust if necessary
+        geojson_file_path = os.path.join(geojson_path, 'Trees_%26_Planting_Sites.geojson')
+
+        # Now you can return the paths for use in your data processing
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "csv_path": csv_file_path,
+                    "shp_path": shp_file_path,
+                    "geojson_path": geojson_file_path,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, csv_path, shp_path, geojson_path):
+        """Yields examples as (key, example) tuples."""
+
+        # Load the datasets
+        csv_df = pd.read_csv(csv_path)
+        shp_gdf = gpd.read_file(shp_path)
+        with open(geojson_path, 'r') as f:
+            geojson_data = json.load(f)
+        geojson_gdf = gpd.GeoDataFrame.from_features(geojson_data["features"])
+
+        # Standardize column names
+        csv_df.columns = csv_df.columns.str.lower().str.replace(' ', '_')
+        shp_gdf.columns = shp_gdf.columns.str.lower().str.replace(' ', '_')
+        geojson_gdf.columns = geojson_gdf.columns.str.lower().str.replace(' ', '_')
+
+        # Convert 'objectid' to int
+        csv_df['objectid'] = csv_df['objectid'].astype(int)
+        shp_gdf['objectid'] = shp_gdf['objectid'].astype(int)
+        geojson_gdf['objectid'] = geojson_gdf['objectid'].astype(int)
+
+        # Merge the dataframes on 'objectid'
+        combined_gdf = shp_gdf.merge(csv_df, on='objectid', how='inner')
+        combined_gdf = combined_gdf.merge(geojson_gdf, on='objectid', how='inner')
+        combined_gdf=combined_gdf[["objectid", "streetaddr", "city_x", "zipcode_x",
+                                    "species_x", "commonname_x", "plantingda", "diameterin_x",
+                                    "heightft_x", "condition_x", "program_x", "matureheig",
+                                    "created_da", "last_edi_1", "geometry_x",
+                                    "x", "y",
+                                    "coremoved_", "coremove_1",
+                                    "o3removed_", "o3remove_1",
+                                    "no2removed", "no2remov_1",
+                                    "so2removed", "so2remov_1",
+                                    "pm10remove", "pm10remo_1",
+                                    "pm25remove", "o2producti",
+                                ]]
+
+        # Yield the combined data
+        for idx, row in combined_gdf.iterrows():
+            # Yield each row as an example, using the index as the key
+            yield idx, row.to_dict()
+
+    @staticmethod
+    def plot_spatial_distribution(combined_gdf, lat_col='x', lon_col='y', color_col='species_x', hover_col='species_x'):
+            # Calculate the mean latitude and longitude for the center of the map
+        center_lat = combined_gdf[lat_col].mean()
+        center_lon = combined_gdf[lon_col].mean()
+
+            # Create a scatter mapbox plot
+        fig = px.scatter_mapbox(combined_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)
+
+            # Set the mapbox style to "open-street-map"
+        fig.update_layout(mapbox_style="open-street-map")
+
+            # Display the figure
+        fig.show()
+