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No commits in common. "5c642827933307dbf609a6d4d7e6a99f2ec0b005" and "5859795b3e552bbbdb9c7f1a8f23c80589adb063" have entirely different histories.
5c64282793
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5859795b3e
2
.gitignore
vendored
2
.gitignore
vendored
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@ -5,6 +5,4 @@ dist
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layers/dot-cams/*/data/states.js
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layers/survey-markers/states.js
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layers/tjx/data/chains.js
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layers/crop-history/data/counties.js
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.direnv
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venv
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2
Makefile
2
Makefile
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@ -1,6 +1,6 @@
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.PHONY: deploy
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deploy: build
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rsync --archive --verbose --delete dist/ root@bert:/srv/maps.chandlerswift.com/
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rsync --archive --verbose --delete dist/ zirconium.home.chandlerswift.com:/var/www/maps.chandlerswift.com/
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.PHONY: clean
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clean:
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@ -1,7 +1,5 @@
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# maps.chandlerswift.com
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[![XKCD 2054: "Data Pipeline"](https://imgs.xkcd.com/comics/data_pipeline_2x.png '"Is the pipeline literally running from your laptop?" "Don't be silly, my laptop disconnects far too often to host a service we rely on. It's running on my phone."')](https://xkcd.com/2054/)
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- [ ] My location (from whereis.chandlerswift.com)
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- [ ] Menards
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- [ ] Culver's
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@ -1,60 +0,0 @@
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#!/usr/bin/python
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import geopandas as gpd
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import os
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import fiona
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import sys
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import csv
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states_to_include = ["MN"]
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state_fipses_to_include = []
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county_lookup = {}
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print("Reading county census data...")
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with open('national_cousub2020.txt') as csvfile:
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reader = csv.DictReader(csvfile, delimiter='|')
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for row in reader:
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if row['STATE'] in states_to_include:
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state_fipses_to_include.append(row['STATEFP'])
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county_lookup[row['STATEFP'] + row['COUNTYFP']] = row
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input_file = sys.argv[1]
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print("Reading input gdb...")
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gdf = gpd.read_file(input_file)
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gdf = gdf[gdf['STATEFIPS'].isin(state_fipses_to_include)]
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print("Reprojecting...")
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gdf = gdf.to_crs("EPSG:4326")
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print("Simplifying geometry...")
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gdf['geometry'] = gdf['geometry'].simplify(0.0001, preserve_topology=True)
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print("Calculating FULLFIPS...")
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gdf['FULLFIPS'] = gdf['STATEFIPS'].astype(str) + gdf['CNTYFIPS'].astype(str)
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print("Finding unique FULLFIPS...")
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counties = gdf['FULLFIPS'].unique()
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# TODO: Trim down which fields are included
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#
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# "CSBID", "CSBYEARS", "CSBACRES",
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# "CDL2016", "CDL2017", "CDL2018", "CDL2019", "CDL2020", "CDL2021", "CDL2022", "CDL2023",
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# "STATEFIPS", "STATEASD", "ASD", "CNTY", "CNTYFIPS",
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# "INSIDE_X", "INSIDE_Y", "Shape_Length", "Shape_Area", "FULLFIPS"
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for i, county in enumerate(counties, 1):
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print(f"Processing county ({county}): {i}/{len(counties)}")
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county_gdf = gdf[gdf['FULLFIPS'] == county]
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output_file = f"{county}.geojson"
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county_gdf.to_file(os.path.join("data", output_file), driver="GeoJSON", COORDINATE_PRECISION=5)
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with open('data/counties.js', 'w') as f:
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for county in counties:
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f.write(f"import county{county} from './{county}.geojson?url';\n")
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f.write('\nexport default {\n')
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for county in counties:
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county_name = county_lookup[county]['COUNTYNAME']
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state_name = county_lookup[county]['STATE']
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f.write(f" '{county_name}, {state_name}': county{county},\n")
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f.write("};\n")
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@ -1,17 +0,0 @@
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#!/usr/bin/env nix-shell
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#! nix-shell -i bash --pure
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#! nix-shell -p bash wget unzip python3 python3Packages.geopandas python3Packages.fiona python3Packages.pyproj
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# TODO: do I need all the python packages?
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set -x -euo pipefail
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wget -nc https://www.nass.usda.gov/Research_and_Science/Crop-Sequence-Boundaries/datasets/NationalCSB_2016-2023_rev23.zip
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wget -nc https://www2.census.gov/geo/docs/reference/codes2020/national_cousub2020.txt
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unzip -u NationalCSB_2016-2023_rev23.zip
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mkdir -p data
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# HEADS UP: this script takes something like 40GB of RAM. In theory, I could
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# probably do something clever with streaming...but I have 40 GB of RAM, so this
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# works!
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python -i extract_counties.py NationalCSB_2016-2023_rev23/CSB1623.gdb
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@ -1,111 +0,0 @@
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import VectorLayer from 'ol/layer/Vector';
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import {Vector as VectorSource} from 'ol/source.js';
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import GeoJSON from 'ol/format/GeoJSON.js';
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import {Style} from 'ol/style.js';
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import counties from './data/counties.js';
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// from https://www.nass.usda.gov/Research_and_Science/Crop-Sequence-Boundaries/metadata_Crop-Sequence-Boundaries-2023.htm
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const crops = {
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"1": "Corn",
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"2": "Cotton",
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"3": "Rice",
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"4": "Sorghum",
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"5": "Soybeans",
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"6": "Sunflower",
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"10": "Peanuts",
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"11": "Tobacco",
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"12": "Sweet Corn",
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"13": "Pop or Orn Corn",
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"14": "Mint",
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"21": "Barley",
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"22": "Durum Wheat",
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"23": "Spring Wheat",
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"24": "Winter Wheat",
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"25": "Other Small Grains",
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"26": "Dbl Crop WinWht/Soybeans",
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"27": "Rye",
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"28": "Oats",
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"29": "Millet",
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"30": "Speltz",
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"31": "Canola",
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"32": "Flaxseed",
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"33": "Safflower",
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"34": "Rape Seed",
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"35": "Mustard",
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"36": "Alfalfa",
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"37": "Other Hay/Non Alfalfa",
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"38": "Camelina",
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"39": "Buckwheat",
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"41": "Sugarbeets",
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"42": "Dry Beans",
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"43": "Potatoes",
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"44": "Other Crops",
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"45": "Sugarcane",
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"46": "Sweet Potatoes",
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"47": "Misc Vegs & Fruits",
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"48": "Watermelons",
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"49": "Onions",
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"50": "Cucumbers",
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"51": "Chick Peas",
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"52": "Lentils",
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"53": "Peas",
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"54": "Tomatoes",
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"55": "Caneberries",
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"56": "Hops",
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"57": "Herbs",
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"58": "Clover/Wildflowers",
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"59": "Sod/Grass Seed",
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"60": "Switchgrass",
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}
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const category = {
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name: "County Crop History",
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details: `<a href="https://www.nass.usda.gov/Research_and_Science/Crop-Sequence-Boundaries/index.php">https://www.nass.usda.gov/Research_and_Science/Crop-Sequence-Boundaries/index.php</a>`,
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layers: [],
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};
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for (let [county, url] of Object.entries(counties)) {
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const geojsonSource = new VectorSource({
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url: url,
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format: new GeoJSON,
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});
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geojsonSource.on('featuresloadend', function(event) {
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event.features.forEach(feature => {
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for (let year = 2016; year <= 2023; year++) {
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const cropid = feature.get(`CDL${year}`);
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// Check if the value exists in the key, then replace it
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if (cropid in crops) {
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feature.set(String(year), crops[cropid]);
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} else {
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feature.set(String(year), cropid);
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}
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feature.unset(`CDL${year}`);
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}
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});
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});
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const vectorLayer = new VectorLayer({
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source: geojsonSource,
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});
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category.layers.push({
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name: county,
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layer: vectorLayer,
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});
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}
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category.layers.sort(function (a, b) {
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const a_state = a.name.substr(a.length - 2);
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const b_state = a.name.substr(b.length - 2);
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// Sort by state...
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if (a_state != b_state) {
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return a_state > b_state ? 1 : -1;
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}
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// ...then by county
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return a.name > b.name ? 1 : -1;
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});
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export default category;
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@ -20,7 +20,6 @@ import dot_cams from './dot-cams/index.js';
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import survey_markers from './survey-markers/index.js';
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import tjx from './tjx/index.js';
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import minnesotaAdventureTrails from './minnesota-adventure-trails/index.js';
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import cropHistory from './crop-history/index.js';
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const layerCategories = [
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{ // Base maps
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@ -104,7 +103,6 @@ const layerCategories = [
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cellular,
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light_pollution,
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tjx,
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cropHistory,
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];
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export default layerCategories;
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