urbantreecanopyindurham2dataset.py

# -*- 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()