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‎.DS_Store

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‎Lectures/.DS_Store

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‎Lectures/Lecture_5/Geopandas1.ipynb

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‎Lectures/Lecture_6/Folium.ipynb

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‎Lectures/Lecture_6/cluster_heatmap_bono.html

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‎Lectures/Lecture_7/.DS_Store

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‎Lectures/Lecture_7/.vdoc.py

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# type: ignore
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# flake8: noqa
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!wget -cq --show-progress "https://github.com/alexanderquispe/Diplomado_PUCP/raw/lecture14/Lecture_14/temp/LC08_20200518_webMC.tif"
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fix, ax = plt.subplots(dpi=200)
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with gw.open(
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"/content/LC08_20200518_webMC.tif",
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) as src:
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src.where(src !=0).plot.imshow(robust=True, ax=ax)
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plt.tight_layout(pad=1)
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‎Lectures/Lecture_7/Assignment_10/GIS.qmd

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‎Lectures/Lecture_7/Assignment_10/Group_5_ass_10_2024.ipynb

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‎Lectures/Lecture_7/Assignment_10/group_3_ass10_2024_Ramirez.ipynb

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‎Lectures/Lecture_7/Assignment_10/group_3_ass_10_2024.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b4c12bc8",
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"metadata": {},
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"source": [
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"## Group #3\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e2750e5d",
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"metadata": {},
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"source": [
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"1. Get the 15 variables from this raster for all Peru departments polygons. This is the link where shapefiles are located. This is the link of the source raster. The values should be the percentage of district area cover by this specific Morphological Settlement Zone."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5c9d4aa8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# pip install rasterio\n",
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"# %pip install geopandas matplotlib shapely rasterio numpy pandas sklearn-xarray -q\n",
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"# !pip install ipywidgets\n",
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"# !jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
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"# pip install rasterstats\n",
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"# %pip install geopandas matplotlib shapely rasterio numpy pandas sklearn-xarray -q\n",
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"# %pip install git+https://github.com/jgrss/geowombat -q"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4789905c",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"import geopandas as gpd\n",
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"import rasterio\n",
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"from rasterio.merge import merge\n",
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"from rasterio.plot import show\n",
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"from shapely.geometry import mapping\n",
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"import rasterio\n",
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"from pathlib import Path\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.patheffects as pe\n",
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"import os\n",
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"from rasterio.mask import mask\n",
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"from rasterstats import zonal_stats\n",
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"import pandas as pd\n",
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"#import geowombat as gw ## Chequear gw en collab\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a3813d15",
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"metadata": {},
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"outputs": [],
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"source": [
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"import geopandas as gpd\n",
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"\n",
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"# Ruta al shapefile\n",
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"shapefile_path = r'../../_data/INEI_LIMITE_DEPARTAMENTAL'\n",
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"\n",
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"# Leer el shapefile usando Geopandas\n",
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"departmentos = gpd.read_file(shapefile_path)\n",
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"departmentos = departments.to_crs('esri:54009')\n",
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"\n",
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"print(departmentos.head())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3c91017c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from google.colab import drive\n",
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"drive.mount('/content/drive')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3ac57118",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Rutas en collab\n",
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"\n",
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"raster = [\"drive/content/diplomado/assignment_10/tif_files/GHS_BUILT_C_MSZ_E2018_GLOBE_R2023A_54009_10_V1_0_R10_C11.tif\",\n",
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" \"drive/content/diplomado/assignment_10/tif_files/GHS_BUILT_C_MSZ_E2018_GLOBE_R2023A_54009_10_V1_0_R10_C12.tif\",\n",
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" \"drive/content/diplomado/assignment_10/tif_files/GHS_BUILT_C_MSZ_E2018_GLOBE_R2023A_54009_10_V1_0_R11_C11.tif\",\n",
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" \"drive/content/diplomado/assignment_10/tif_files/GHS_BUILT_C_MSZ_E2018_GLOBE_R2023A_54009_10_V1_0_R11_C12.tif\",\n",
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" \"drive/content/diplomado/assignment_10/tif_files/GHS_BUILT_C_MSZ_E2018_GLOBE_R2023A_54009_10_V1_0_R12_C11.tif\",\n",
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" \"drive/content/diplomado/assignment_10/tif_files/GHS_BUILT_C_MSZ_E2018_GLOBE_R2023A_54009_10_V1_0_R12_C12.tif\"\n",
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" ]\n",
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"\n",
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"statistics = []"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7786c4c9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Iterar sobre cada archivo raster\n",
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"for raster_path in raster:\n",
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" # estadísticas zonales\n",
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" stats = zonal_stats(departmentos, raster_path, stats=\"count sum\", categorical=True, all_touched=True)\n",
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" # transforma DF\n",
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" stats_df = pd.DataFrame(stats)\n",
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" # Concatenar \n",
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" df = pd.concat([departmentos.reset_index(drop=True), stats_df], axis=1)\n",
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" statistics.append(df)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f5476429",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Concatenar todos los DataFrames en all_stats en un único DataFrame\n",
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"final_df = pd.concat(statistics, ignore_index=True)\n",
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"\n",
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"print(final_df.head())\n",
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"\n",
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"#Reemplazar todos los NaN por 0\n",
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"final_df.fillna(0, inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "14984616",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "9724792d",
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"metadata": {},
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"source": [
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"2. Then you are going to generate choropleth map using folium for these 15 variables."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a15a1cd1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Definir la área de pixeles (resolución del raster al cuadrado)\n",
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"spatial_resolution = 10 # meters\n",
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"pixel_area = spatial_resolution ** 2\n",
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"\n",
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"# Iterar por cada una de las 15 categorías MSZ\n",
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"for category in range(1, 16):\n",
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" # Crear una nueva columna para cada categoría MSZ\n",
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" column_name = f'MSZ_{category}_coverage'\n",
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" \n",
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" # Usar una función lambda con .apply para calcular el porcentaje de cobertura\n",
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" coverage_function = lambda row: ((row.get(str(category), 0) * pixel_area) / row['geometry'].area) * 100 if str(category) in row else 0\n",
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" \n",
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" # Aplicar función al DataFrame\n",
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" final_df[column_name] = final_df.apply(coverage_function, axis=1)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f2fbdab7",
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"metadata": {},
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"source": [
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"3. Save your html in the same folder of your JN. Name your HTML as your branch. This HTML should have all these layers. Please do not forget to use Layer Control.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c4c88d45",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Crear un mapa centrado en una ubicación específica (puedes ajustar esto según tus datos)\n",
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"map_center = [-12.0757538, -76.9863174]\n",
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"mymap = folium.Map(location=map_center, zoom_start=5)\n",
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"\n",
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"# Agregar las capas al mapa\n",
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"for category in range(1, 16):\n",
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" layer_name = f'MSZ_{category}_coverage'\n",
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" geojson_data = final_df.to_json()\n",
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" \n",
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" folium.Choropleth(\n",
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" geo_data=geojson_data,\n",
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" data=final_df,\n",
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" columns=['geometry', layer_name],\n",
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" key_on='feature.properties.geometry',\n",
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" fill_color='YlOrRd', \n",
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" fill_opacity=0.7,\n",
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" line_opacity=0.2,\n",
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" legend_name=f'MSZ {category} Coverage'\n",
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" ).add_to(mymap)\n",
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"\n",
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"# Agregar el control de capas al mapa\n",
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"folium.LayerControl().add_to(mymap)\n",
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"\n",
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"# Guardar el mapa como un archivo HTML\n",
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"g3a10DFG_html = 'g3a10DFG.html' \n",
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"mymap.save(g3a10DFG_html)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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