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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "4db2bafd-697e-4c56-a0aa-9aba263c72d6",
+ "metadata": {},
+ "source": [
+ "# Development of highways over time\n",
+ "In this notebook we demonstrate how to analyze and visualize the development highways in OSM over time.\n",
+ "\n",
+ "These are the steps you see further down:\n",
+ "\n",
+ "* Set the connection parameters.\n",
+ "* Prepare your input parameters, e.g. define area of interest and time interval.\n",
+ "* **Download data** using DuckDB.\n",
+ "* Create the mapping saturation **plot**.\n",
+ "* Create a **Map**, an **interactive Slider** to filter the map data. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3d674481-bbd1-41e8-af30-06f55ddca03c",
+ "metadata": {},
+ "source": [
+ "# Getting started\n",
+ "Set connection params."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "c9e38420-73a3-45d6-a84a-fb5448b3dedc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "s3_user = \"admin\" # add your user here\n",
+ "s3_password = \"frephIkHageynLangIlpordUrd9FracGorAsEuvafoodVoxwoycsyohoddowtOct\" # add your password here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ed4463d8-471d-4a23-9286-1e7cc8048e92",
+ "metadata": {},
+ "source": [
+ "Configure DuckDB."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "86bcd8e2-9285-44f2-bd3c-948fe08c545f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import duckdb\n",
+ "\n",
+ "con = duckdb.connect(\n",
+ " config={\n",
+ " 'threads': 32,\n",
+ " 'max_memory': '50GB'\n",
+ " }\n",
+ ")\n",
+ "con.install_extension(\"spatial\")\n",
+ "con.load_extension(\"spatial\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1e729452-d070-4d4e-a59c-58cf7d9b54e0",
+ "metadata": {},
+ "source": [
+ "Set connection to MinIO object storage."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "da021474-99e1-40b2-871f-0a817738e60f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "┌─────────┐\n",
+ "│ Success │\n",
+ "│ boolean │\n",
+ "├─────────┤\n",
+ "│ true │\n",
+ "└─────────┘\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "query = f\"\"\"\n",
+ "DROP SECRET IF EXISTS \"__default_s3\";\n",
+ "CREATE SECRET (\n",
+ " TYPE S3,\n",
+ " KEY_ID '{s3_user}',\n",
+ " SECRET '{s3_password}',\n",
+ " REGION 'eu-central-1',\n",
+ " endpoint 'sotm2024.minio.heigit.org',\n",
+ " use_ssl true,\n",
+ " url_style 'path'\n",
+ " );\n",
+ "\"\"\"\n",
+ "con.sql(query).show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4d0dd278-6f1a-44ae-bf06-fbaa885a28ae",
+ "metadata": {},
+ "source": [
+ "## Prepare the input parameters for your analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "fdb4522d-9326-490d-b4fd-8999bda14553",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set s3 path for parquet input data\n",
+ "parquet_data_path = \"s3a://heigit-ohsome-sotm24/data/geo_sort_ext/contributions_germany/**\"\n",
+ "#parquet_data_path = \"s3a://heigit-ohsome-sotm24/data/geo_sort_ext/contributions/**\"\n",
+ "\n",
+ "\n",
+ "# Define location filter\n",
+ "bboxes = {\n",
+ " 'heidelberg': (8.629761, 49.379556, 8.742371, 49.437890),\n",
+ " 'nairobi': (36.650938, -1.444471, 37.103887, -1.163522),\n",
+ " 'mannheim': (8.41416, 49.410362, 8.58999, 49.590489), \n",
+ " 'berlin': (13.088345, 52.338271, 13.761161, 52.675509)\n",
+ "}\n",
+ "\n",
+ "selected_region = 'heidelberg'\n",
+ "xmin, ymin, xmax, ymax = bboxes[selected_region]\n",
+ "#area_of_interest_file =f\"../data/{selected_region}.geojson\"\n",
+ "area_of_interest_file =f\"{selected_region}.geojson\"\n",
+ "\n",
+ "# Define geometry type filter\n",
+ "geometry_type = 'LineString'\n",
+ "\n",
+ "# Define time range\n",
+ "min_timestamp = '2008-01-01'\n",
+ "max_timestamp = '2015-01-01'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "173104f5-0439-4259-97dd-82cc0ecbea5a",
+ "metadata": {},
+ "source": [
+ "## Get the Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "f6af5983-b350-4a8a-81b1-cdd27a327896",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "48c2ca24a58d4e908aa5cd28df0b46c2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "download took 14.692 sec.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import time\n",
+ "start_time = time.time()\n",
+ "\n",
+ "query = f\"\"\"\n",
+ "DROP TABLE IF EXISTS osm_data;\n",
+ "CREATE TABLE osm_data AS \n",
+ "(\n",
+ "SELECT\n",
+ " a.osm_id,\n",
+ " a.osm_version,\n",
+ " a.status,\n",
+ " a.valid_from,\n",
+ " a.valid_to,\n",
+ " a.tags,\n",
+ " ST_Intersection(ST_GeomFromText(a.geometry), aoi.geom) as clipped_geometry,\n",
+ " ST_Length_Spheroid(clipped_geometry) / 1000 as length_km\n",
+ "FROM\n",
+ " read_parquet('{parquet_data_path}', hive_partitioning=true) as a,\n",
+ " st_read('{area_of_interest_file}') as aoi\n",
+ "WHERE 1=1\n",
+ " and (status = 'latest' or status = 'history')\n",
+ " and geometry_type = '{geometry_type}'\n",
+ " and tags['highway'][1] is not null\n",
+ " -- spatial filtering part\n",
+ " and (a.bbox.xmax >= {xmin} AND a.bbox.xmin <= {xmax})\n",
+ " and (a.bbox.ymax >= {ymin} AND a.bbox.ymin <= {ymax})\n",
+ " and ST_Intersects(st_GeomFromText(a.geometry), aoi.geom)\n",
+ ")\n",
+ ";\n",
+ "\"\"\"\n",
+ "con.sql(query)\n",
+ "\n",
+ "download_time = round(time.time() - start_time, 3)\n",
+ "print(f\"download took {download_time} sec.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f23808c7-ce8f-4612-b90f-24b19f6c8879",
+ "metadata": {},
+ "source": [
+ "## Plot chart"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "24a1943a-9f21-434d-b682-2c18daa94345",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query = f\"\"\"\n",
+ "DROP TABLE IF EXISTS osm_stats;\n",
+ "CREATE TABLE osm_stats AS\n",
+ "(\n",
+ "with \n",
+ "snapshots as (\n",
+ " SELECT \n",
+ " row_number() OVER () as snapshot_index,\n",
+ " range AS datetime_key \n",
+ " FROM RANGE(DATE '{min_timestamp}', DATE '{max_timestamp}', INTERVAL 1 MONTH)\n",
+ ")\n",
+ "SELECT\n",
+ " snapshots.snapshot_index,\n",
+ " snapshots.datetime_key,\n",
+ " osm_id,\n",
+ " valid_from,\n",
+ " valid_to,\n",
+ " length_km,\n",
+ " clipped_geometry\n",
+ "FROM snapshots\n",
+ "JOIN osm_data on (\n",
+ " snapshots.datetime_key >= osm_data.valid_from\n",
+ " and\n",
+ " snapshots.datetime_key <= osm_data.valid_to\n",
+ " )\n",
+ "ORDER BY snapshot_index\n",
+ ");\n",
+ "\"\"\"\n",
+ "con.sql(query)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "07dd3e0f-4002-4669-af00-7a300198c335",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {},
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.holoviews_exec.v0+json": "",
+ "text/html": [
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ ":Curve [month] (length_km)"
+ ]
+ },
+ "metadata": {
+ "application/vnd.holoviews_exec.v0+json": {
+ "id": "p1081"
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "chart_query = f\"\"\"\n",
+ "SELECT\n",
+ " DATE '{min_timestamp}' + INTERVAL (snapshot_index-1) MONTH as month,\n",
+ " --(sum(road_length)/1000)::int8 as length_km\n",
+ " sum(length_km) as length_km\n",
+ "FROM osm_stats\n",
+ "GROUP BY month\n",
+ "ORDER BY month ASC\n",
+ "\"\"\"\n",
+ "\n",
+ "chart_data = con.sql(chart_query).pl()\n",
+ "chart = chart_data.plot.line(x=\"month\", y=\"length_km\", xaxis='top', width=700, responsive=False)\n",
+ "display(chart)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "827c7cb8-147e-4301-a607-9ded8602e06a",
+ "metadata": {},
+ "source": [
+ "## Display OSM highway evolution on map\n",
+ "Get data from DucDKB into GeoPandas dataframe."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "efab832a-569a-4bea-9422-0a50ac1afb49",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import geopandas as gpd\n",
+ "\n",
+ "map_query = f\"\"\"\n",
+ "SELECT\n",
+ " snapshot_index,\n",
+ " osm_id,\n",
+ " ST_asText(clipped_geometry) as geometry\n",
+ "FROM osm_stats;\n",
+ "\"\"\"\n",
+ "\n",
+ "df = con.sql(map_query).df()\n",
+ "\n",
+ "# convert the data to geodata\n",
+ "gdf = gpd.GeoDataFrame(\n",
+ " df,\n",
+ " geometry=gpd.GeoSeries.from_wkt(df['geometry'])\n",
+ ").set_crs('epsg:4326')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6bb0b3fe-7c12-4381-b906-ecfa95a9c4c7",
+ "metadata": {},
+ "source": [
+ "Define map parameters and style."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "ff149fef-9f16-47fb-8f5d-953ca5b4b53c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import lonboard\n",
+ "\n",
+ "# compute lonboard color style for contious color map\n",
+ "min_snapshot_index = gdf[\"snapshot_index\"].min()\n",
+ "max_snapshot_index = gdf[\"snapshot_index\"].max()\n",
+ "\n",
+ "# lonboard gpu filtering\n",
+ "filter_values = gdf[\"snapshot_index\"]\n",
+ "\n",
+ "# the lonboard map definition\n",
+ "layer = lonboard.PathLayer.from_geopandas(\n",
+ " gdf,\n",
+ " extensions=[lonboard.layer_extension.DataFilterExtension(filter_size=1)],\n",
+ " get_filter_value=filter_values, # replace with desired column\n",
+ " filter_range=[216, 216], # replace with desired filter range\n",
+ " get_color=[0,255,255,255],\n",
+ " width_min_pixels=0.8\n",
+ ")\n",
+ "\n",
+ "view_state = {\n",
+ " \"longitude\": xmin + ((xmax - xmin) / 2),\n",
+ " \"latitude\": ymin + ((ymax - ymin) / 2),\n",
+ " \"zoom\": 9,\n",
+ "}\n",
+ "\n",
+ "osm_evolution_map = lonboard.Map(\n",
+ " basemap_style=lonboard.basemap.CartoBasemap.Positron,\n",
+ " layers=[layer],\n",
+ " view_state=view_state\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eaaaba55-2a47-45ec-94a7-3c7c4aa07d8e",
+ "metadata": {},
+ "source": [
+ "Set date slider."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "f53b828c-fbf2-4e7b-980a-a9799d621434",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import datetime\n",
+ "from dateutil.relativedelta import relativedelta\n",
+ "import ipywidgets\n",
+ "from traitlets import directional_link\n",
+ "\n",
+ "dates = pd.date_range(min_timestamp, max_timestamp, freq='MS').tolist()\n",
+ "options = [(date.strftime('%d-%b-%Y'), i+1) for i, date in enumerate(dates)]\n",
+ "\n",
+ "date_slider = ipywidgets.SelectionSlider(\n",
+ " options=options,\n",
+ " description='Day:',\n",
+ " layout=ipywidgets.Layout(width='1000px'),\n",
+ " disabled=False\n",
+ ")\n",
+ "\n",
+ "directional_link(\n",
+ " (date_slider, 'value'),\n",
+ " (layer, \"filter_range\"),\n",
+ " transform=lambda v: (v,v)\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fad04322-bb80-4be1-801f-23ffddc36b58",
+ "metadata": {},
+ "source": [
+ "Display the map. Have fun exploring and moving around the time slider!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "42ac6bb9-fd86-41a2-a84a-141802abeaf9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8211d7d05a6c4e89bc72c3e8d201d671",
+ "version_major": 2,
+ "version_minor": 1
+ },
+ "text/plain": [
+ "Map(basemap_style=, la…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c7945535e14f49f7b3d539e9dfe171b5",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "SelectionSlider(description='Day:', layout=Layout(width='1000px'), options=(('01-Jan-2008', 1), ('01-Feb-2008'…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(osm_evolution_map, date_slider)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6e2db5c7-1c6a-4884-8c84-54f251be2bae",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}