31:T45e,

Highway 70

Highway 25

Highway 83

Highway 285

32:T46b,Plot.carto({ projection: "mercator", marks: [ // base the fitExtent on the first feature Plot.feature(topojson.feature(coloradoCounties, coloradoCounties.objects.cb_2015_colorado_county_20m), { fill: "#222222", }), // draw the mile markers Plot.points(selectedHighwaysOne, { lonLat: d => [parseFloat(d.lon), parseFloat(d.lat)], fill: "ROUTE", fillOpacity: 1, r: 1 }), // draw road kills from Region 1 Plot.points(selectedKillsOne.filter(d => d.count !== null), { lonLat: d => [parseFloat(d.lon), parseFloat(d.lat)], fill: "ROUTE", r: "count", strokeWidth: 1, stroke: "#ffffff", fillOpacity: 0.6, }), // draw cities Plot.points(frontRangeCities, { lonLat: d => [d.lon, d.lat], fill: "#ffffff", r: 2, }), // draw city name Plot.label(frontRangeCities, { lonLat: d => [d.lon, d.lat], text: "city", fill: "#ffffff", stroke: "#222222", strokeWidth: 6, textAnchor: "start", dx: 6, }) ], width: isMobile ? 400 : 800, height: width * 0.5, })33:T573,

Highway 25

Highway 24

Highway 50A

Highway 50B

Highway 115

34:T46b,Plot.carto({ projection: "mercator", marks: [ // base the fitExtent on the first feature Plot.feature(topojson.feature(coloradoCounties, coloradoCounties.objects.cb_2015_colorado_county_20m), { fill: "#222222", }), // draw the mile markers Plot.points(selectedHighwaysTwo, { lonLat: d => [parseFloat(d.lon), parseFloat(d.lat)], fill: "ROUTE", fillOpacity: 1, r: 1 }), // draw road kills from Region 1 Plot.points(selectedKillsTwo.filter(d => d.count !== null), { lonLat: d => [parseFloat(d.lon), parseFloat(d.lat)], fill: "ROUTE", r: "count", strokeWidth: 1, stroke: "#ffffff", fillOpacity: 0.6, }), // draw cities Plot.points(frontRangeCities, { lonLat: d => [d.lon, d.lat], fill: "#ffffff", r: 2, }), // draw city name Plot.label(frontRangeCities, { lonLat: d => [d.lon, d.lat], text: "city", fill: "#ffffff", stroke: "#222222", strokeWidth: 6, textAnchor: "start", dx: 6, }) ], width: isMobile ? 400 : 800, height: width * 0.5, })35:T93b,// Based on https://observablehq.com/@tomshanley/cheysson-color-palettes pattern = () => svg` `36:[{"id":"1a5dd44f6da63a611ff3746d9f4e991e37a4c3429ec10143713830e28db2295eda520a47b42f65c07ca0d99733099f1c477fb32c39244b25d28e7e4310cfe8be","href":"https://static.observableusercontent.com/files/1a5dd44f6da63a611ff3746d9f4e991e37a4c3429ec10143713830e28db2295eda520a47b42f65c07ca0d99733099f1c477fb32c39244b25d28e7e4310cfe8be","name":"CDOT_roadKill_test - region1_dwnld.csv","type":"text/csv","lastModified":1653423529862.194,"size":793},{"id":"3fb4aaaaefc5d22a9e3c3af3463be6a4c732f3e0590fe09e3f4b0465e192a108e42ef7dab9934116571feefe7ccc1d508e77bac86663eb419640820143bf88f1","href":"https://static.observableusercontent.com/files/3fb4aaaaefc5d22a9e3c3af3463be6a4c732f3e0590fe09e3f4b0465e192a108e42ef7dab9934116571feefe7ccc1d508e77bac86663eb419640820143bf88f1","name":"CDOT_roadKill_test - region2_dwnld (1).csv","type":"text/csv","lastModified":1653515220226.524,"size":1742},{"id":"a76340f7d9ec08367b1f144726459d7a8722f8b0d5908d2e46b31a9c77770e1d86e6ef324fb5a56b2fdf6253331650699e6e7f19b0062741d647d5e49f300cb0","href":"https://static.observableusercontent.com/files/a76340f7d9ec08367b1f144726459d7a8722f8b0d5908d2e46b31a9c77770e1d86e6ef324fb5a56b2fdf6253331650699e6e7f19b0062741d647d5e49f300cb0","name":"frontRangeCities - frontRangeCities (5).csv","type":"text/csv","lastModified":1653516667370.435,"size":243},{"id":"0c8aabfebfcc3399aa6d31ee0fc66100136350046fdaacd5eb705f9fbcb652412f074eb505f8973c0ac937fb63979bb3eaaa61ecc7274089b8e3b6b53e66748c","href":"https://static.observableusercontent.com/files/0c8aabfebfcc3399aa6d31ee0fc66100136350046fdaacd5eb705f9fbcb652412f074eb505f8973c0ac937fb63979bb3eaaa61ecc7274089b8e3b6b53e66748c","name":"roadkill@1.png","type":"image/png","lastModified":1653568376686.334,"size":211489},{"id":"b3b9e1782679051177748b8dc1ad8ff35a1927fde1579009eaf4a1056b65db816c7b41d3505aa561910eb1bff8ff84ed26a58425c9b36be6a2bbb8f71d9d6ac6","href":"https://static.observableusercontent.com/files/b3b9e1782679051177748b8dc1ad8ff35a1927fde1579009eaf4a1056b65db816c7b41d3505aa561910eb1bff8ff84ed26a58425c9b36be6a2bbb8f71d9d6ac6","name":"MILE_MARKERS_GPS.csv","type":"text/csv","lastModified":1653392995257.831,"size":1493117}] 2f:{"title":"Colorado Roadkill","cells":[{"id":0,"value":"# Colorado Roadkill","pinned":false,"hidden":false,"mode":"md"},{"id":775,"value":"Plot.carto({\n marks: [\n // base the fitExtent on the first feature\n Plot.feature(topojson.feature(coloradoCounties, coloradoCounties.objects.cb_2015_colorado_county_20m), { \n }),\n // draw the surrounding states\n Plot.feature(topojson.feature(us, us.objects.states), {\n fill: \"#800000\",\n }),\n // draw road kills from Region 2\n Plot.points(mergedTwo.filter(d => d.count !== null), {\n lonLat: d => [parseFloat(d.lon), parseFloat(d.lat)],\n fill: \"#f9d9d1\",\n fillOpacity: 1,\n r: \"count\",\n strokeWidth: 1,\n stroke: \"#ffffff\",\n fillOpacity: 0.65,\n })\n ],\n width,\n height: width * 0.2,\n marginTop: -140,\n marginBottom: -10,\n marginLeft: -200,\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"- I'd like to do something with the annual roadkill reports from Colorado highways. Like [this one](https://www.codot.gov/programs/environmental/wildlife/data/annual-roadkill-reports/eoy2021.pdf) from 2021.\n- My first thought here is to map the incidents. As we can see in the PDF, we have the highway and the mile posting (or marker) and how many incidents were reported there: ","pinned":false,"hidden":false,"mode":"md"},{"id":7,"value":"roadkillPDF = FileAttachment(\"roadkill@1.png\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"- What we're going to need is to figure out the latitude and longitude of mile markers, in order to position points on the map. So I googled for `CDOT highway mile marker data` and found [this dataset](https://data.colorado.gov/Transportation/Highway-Mileposts-in-Colorado/trm9-dm4m) from CDOT (Colorado Department of Transportation) that was created in 2004 and is, apparently, updated monthly.\n- Let's add it to our notebook!","pinned":false,"hidden":false,"mode":"md"},{"id":13,"value":"cdotMileMarkers = FileAttachment(\"MILE_MARKERS_GPS.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"- If we open up that array of objects, we're going to see something annoying. The latitude and longitude data are in a key called `the_geom` like this: `\"POINT (-104.61518758044102 38.3157862807021)\"` So we're going to have to parse it! I'm going to do this in steps for simplicity's sake and because I'm not a good enough programmer to chain everything.","pinned":false,"hidden":false,"mode":"md"},{"id":22,"value":"cdotMileMarkersSlice = cdotMileMarkers.map(object => {\n return {\n ...object, \n latLon: object.the_geom.slice(7) // remove first 7 characters in the string\n }; \n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":38,"value":"cdotMileMarkersSplitSlice = cdotMileMarkersSlice.map(object => {\n return {\n ...object, \n lon: object.latLon.split(' ').slice(0, -1), // split at the space, remove last character in the string\n lat: object.latLon.split(' ').pop().slice(0, -1)\n }; // split, take the second part, remove last character in the string\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":51,"value":"cdotMileMarkersReduce = cdotMileMarkersSplitSlice.map(object => {\n return {\n ...object, \n lon: object.lon.reduce(function (acc, obj) { \n return obj; \n }, 0)}; // reduce this weird array we created and get a clean lat\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":61,"value":"mileMarkersClean = cdotMileMarkersReduce.map(({\n the_geom, \n latLon, \n ...rest\n}) => {\n return rest; // clean up our data by removing what we don't need. \n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":87,"value":"- Alright, we've cleaned our lat and lons. In the data we can also see that `ROUTE` is the highway name and `REF_PT` seems to the mile posting, marker, point (these terms seem to be sometimes interchangeable – but will need to verify that).\n- Let's draw the mile markers from the data: ","pinned":false,"hidden":false,"mode":"md"},{"id":132,"value":"Plot.carto({\n projection: \"mercator\",\n marks: [\n // fill the counties\n // and base the fitExtent on the first feature\n Plot.feature(topojson.feature(coloradoCounties, coloradoCounties.objects.cb_2015_colorado_county_20m), { \n fill: \"#f0f0f0\",\n }),\n // draw the surrounding states\n // Plot.feature(topojson.feature(us, us.objects.states), {\n // strokeWidth: 1,\n // stroke: \"#222222\",\n // }),\n // draw the mile markers\n Plot.points(mileMarkersClean, {\n lonLat: d => [parseFloat(d.lon), parseFloat(d.lat)],\n fill: \"#222222\",\n fillOpacity: 1,\n r: 0.5\n })\n ],\n width: isMobile ? 400 : 600,\n // height,\n // marginTop: 25,\n // marginBottom: 30,\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":864,"value":"An aside: Isn't run to see where Colorado's Front Range starts? The highways in the east follow a much more grid-like pattern than the highways out west because of the Rockies. ","pinned":false,"hidden":false,"mode":"md"},{"id":162,"value":"- Sooner or later we're going to have to get that roadkill data from PDF into a tabular form. Might as well do it now! I did the first and second region in 2021 by hand in Google Sheets:","pinned":false,"hidden":false,"mode":"md"},{"id":166,"value":"regionOne = FileAttachment(\"CDOT_roadKill_test - region1_dwnld.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":583,"value":"regionTwo= FileAttachment(\"CDOT_roadKill_test - region2_dwnld (1).csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":193,"value":"- Let's write a little function to join our datasets by the highway and mile marker, then merge them.","pinned":false,"hidden":false,"mode":"md"},{"id":168,"value":"mergeById = (a1, a2) =>\n a1.map(itm => ({\n ...a2.find((item) => (item.hw === itm.ROUTE && item.mm === parseFloat(itm.REF_PT)) && item), // make sure the string is matching with the number \n ...itm,\n }));","pinned":false,"hidden":false,"mode":"ojs"},{"id":769,"value":"- And run each region's data through the function:","pinned":false,"hidden":false,"mode":"md"},{"id":172,"value":"mergedOne = mergeById(mileMarkersClean, regionOne);","pinned":false,"hidden":false,"mode":"ojs"},{"id":602,"value":"mergedTwo = mergeById(mileMarkersClean, regionTwo);","pinned":false,"hidden":false,"mode":"ojs"},{"id":196,"value":"- Just want to test by searching for a specific data point where we know we have roadkill data:","pinned":false,"hidden":false,"mode":"md"},{"id":177,"value":"// a test that...weirdly returns two arrays of objects\n// going to have to figure out if that's an issue or not.\ntestArray = mergedOne.filter(d => d.ROUTE === \"006G\") ","pinned":false,"hidden":false,"mode":"ojs"},{"id":219,"value":"⚠️ One of the issues I've noticed is that the roadkill data has stretches of ten miles as the position of the roadkill. That means, it sometimes matches the highway mile marker data and it sometimes doesn't. Will want to come back and solve that eventually.","pinned":false,"hidden":false,"mode":"md"},{"id":206,"value":"- And let's just pop that into the map quickly to see that everything is in order. Looks like region one includes Denver:","pinned":false,"hidden":false,"mode":"md"},{"id":686,"value":"### Region One ","pinned":false,"hidden":false,"mode":"md"},{"id":199,"value":"Plot.carto({\n projection: \"mercator\",\n marks: [\n // base the fitExtent on the first feature\n Plot.feature(topojson.feature(coloradoCounties, coloradoCounties.objects.cb_2015_colorado_county_20m), { \n fill: \"#f0f0f0\",\n }),\n // draw the surrounding states\n Plot.feature(topojson.feature(us, us.objects.states), {\n strokeWidth: 1,\n stroke: \"#222222\",\n }),\n // draw the mile markers\n Plot.points(mileMarkersClean, {\n lonLat: d => [parseFloat(d.lon), parseFloat(d.lat)],\n fill: \"#222222\",\n fillOpacity: 1,\n r: 0.5\n }),\n // draw road kills from Region 1 \n Plot.points(mergedOne.filter(d => d.count !== null), {\n lonLat: d => [parseFloat(d.lon), parseFloat(d.lat)],\n fill: \"#800000\",\n fillOpacity: 1,\n r: \"count\",\n strokeWidth: 1,\n stroke: \"#ffffff\",\n fillOpacity: 0.6,\n })\n ],\n width,\n height: width * 0.5,\n marginTop: 25,\n marginBottom: 30,\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":688,"value":"### Region Two","pinned":false,"hidden":false,"mode":"md"},{"id":611,"value":"Plot.carto({\n projection: \"mercator\",\n marks: [\n // base the fitExtent on the first feature\n Plot.feature(topojson.feature(coloradoCounties, coloradoCounties.objects.cb_2015_colorado_county_20m), { \n fill: \"#f0f0f0\",\n }),\n // draw the surrounding states\n Plot.feature(topojson.feature(us, us.objects.states), {\n strokeWidth: 1,\n stroke: \"#222222\",\n }),\n // draw the mile markers\n Plot.points(mileMarkersClean, {\n lonLat: d => [parseFloat(d.lon), parseFloat(d.lat)],\n fill: \"#222222\",\n fillOpacity: 1,\n r: 0.5\n }),\n // draw road kills from Region 2\n Plot.points(mergedTwo.filter(d => d.count !== null), {\n lonLat: d => [parseFloat(d.lon), parseFloat(d.lat)],\n fill: \"#3e6085\",\n fillOpacity: 1,\n r: \"count\",\n strokeWidth: 1,\n stroke: \"#ffffff\",\n fillOpacity: 0.6,\n })\n ],\n width,\n height: width * 0.5,\n marginTop: 25,\n marginBottom: 30,\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":228,"value":"- I think it would be fun to chart the volume of roadkill per highway in region one. So let's prepare the data for that.","pinned":false,"hidden":false,"mode":"md"},{"id":763,"value":" Region One summing roadkill reports by highway","pinned":false,"hidden":false,"mode":"md"},{"id":239,"value":"summedKillsOne = Array.from(regionOne.reduce(\n (m, {\n hw, \n count\n }) => m.set(hw, (m.get(hw) || 0) + count), \n new Map\n), ([hw, count]) => ({hw, count}));","pinned":false,"hidden":false,"mode":"ojs"},{"id":766,"value":" Region Two summing roadkill reports by highway","pinned":false,"hidden":false,"mode":"md"},{"id":647,"value":"summedKillsTwo = Array.from(regionTwo.reduce(\n (m, {\n hw, \n count\n }) => m.set(hw, (m.get(hw) || 0) + count), \n new Map\n), ([hw, count]) => ({hw, count}));","pinned":false,"hidden":false,"mode":"ojs"},{"id":309,"value":"- Let's plot it, maybe with some fun textures:","pinned":false,"hidden":false,"mode":"md"},{"id":690,"value":"### Region One ","pinned":false,"hidden":false,"mode":"md"},{"id":246,"value":"Plot.plot({\n color: {\n range: [\n \"url(#pattern-red)\",\n ],\n },\n marks: [\n pattern,\n // outline\n Plot.barY(summedKillsOne, {\n x: \"hw\", \n y: \"count\", \n stroke: \"#222222\",\n fill: \"#f9d9d1\",\n sort: {\n x: \"y\", \n reverse: true\n }}),\n Plot.barY(summedKillsOne, {\n x: \"hw\", \n y: \"count\", \n fill: \"hw\",\n inset: 2,\n sort: {\n x: \"y\", \n reverse: true\n }}),\n \n Plot.ruleY([0])\n ],\n height: 575,\n width: isMobile ? 400 : width,\n x: {\n // grid: true,\n label: `Highway in Region One`,\n },\n y: {\n label: `Road kill reports`,\n grid: true,\n },\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":692,"value":"### Region Two","pinned":false,"hidden":false,"mode":"md"},{"id":639,"value":"Plot.plot({\n color: {\n range: [\n \"url(#pattern-blue)\",\n ],\n },\n marks: [\n pattern,\n // outline\n Plot.barY(summedKillsTwo, {\n x: \"hw\", \n y: \"count\", \n stroke: \"#222222\",\n fill: \"#dbe4ed\",\n sort: {\n x: \"y\", \n reverse: true\n }}),\n Plot.barY(summedKillsTwo, {\n x: \"hw\", \n y: \"count\", \n fill: \"hw\",\n inset: 2,\n sort: {\n x: \"y\", \n reverse: true\n }}),\n \n Plot.ruleY([0])\n ],\n height: 575,\n width: isMobile ? 400 : width,\n x: {\n // grid: true,\n label: `Highway in Region Two`,\n },\n y: {\n label: `Road kill reports`,\n grid: true,\n },\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":319,"value":"- For Region One, it looks like highway `25`, `83`, `70` and `285` have the highest reporting numbers. For Region Two, that's `24`, `115`, `50A` and `50B` and `285`. Let's map those to remind us where in Colorado they are, I'll start by filtering the data for only the highways we want: ","pinned":false,"hidden":false,"mode":"md"},{"id":755,"value":" Region One filtering highway and roadkill reports","pinned":false,"hidden":false,"mode":"md"},{"id":322,"value":"selectedHighwaysOne = mileMarkersClean.filter(d => d.ROUTE === \"025A\" || d.ROUTE === \"083A\" || d.ROUTE === \"070A\" || d.ROUTE === \"285D\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":334,"value":"selectedKillsOne = mergedOne.filter(d => d.ROUTE === \"025A\" || d.ROUTE === \"083A\" || d.ROUTE === \"070A\" || d.ROUTE === \"285D\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":750,"value":" Region Two filtering highway and roadkill reports","pinned":false,"hidden":false,"mode":"md"},{"id":670,"value":"selectedHighwaysTwo = mileMarkersClean.filter(d => d.ROUTE === \"024A\" || d.ROUTE === \"115A\" || d.ROUTE === \"050B\" || d.ROUTE === \"050A\" || d.ROUTE === \"025A\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":658,"value":"selectedKillsTwo = mergedTwo.filter(d => d.ROUTE === \"024A\" || d.ROUTE === \"115A\" || d.ROUTE === \"050B\" || d.ROUTE === \"050A\" || d.ROUTE === \"025A\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":489,"value":"- I'd like to add some of the Colorado Front Range cities to this map, so I put a quick list together in Google Sheets and then upload it as a CSV into my notebook. ","pinned":false,"hidden":false,"mode":"md"},{"id":491,"value":"frontRangeCities = FileAttachment(\"frontRangeCities - frontRangeCities (5).csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":757,"value":"- Then I'll map these two regions!","pinned":false,"hidden":false,"mode":"md"},{"id":759,"value":"### Region One","pinned":false,"hidden":false,"mode":"md"},{"id":381,"value":"$31","pinned":false,"hidden":false,"mode":"html"},{"id":315,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":761,"value":"### Region Two","pinned":false,"hidden":false,"mode":"md"},{"id":698,"value":"$33","pinned":false,"hidden":false,"mode":"html"},{"id":655,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":98,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":100,"value":"### Imports and libraries","pinned":false,"hidden":false,"mode":"md"},{"id":102,"value":"projectionNames = [\n \"aitoff\",\n \"polyconic\",\n \"august\",\n \"baker\",\n \"boggs\",\n \"bromley\",\n \"collignon\",\n \"conicEqualArea\",\n \"craster\",\n \"eckert1\",\n \"eckert2\",\n \"eckert3\",\n \"eckert4\",\n \"eckert5\",\n \"eckert6\",\n \"eisenlohr\",\n \"equalEarth\",\n \"equirectangular\",\n \"fahey\",\n \"mtFlatPolarParabolic\",\n \"mtFlatPolarQuartic\",\n \"mtFlatPolarSinusoidal\",\n \"foucaut\",\n \"ginzburg5\",\n \"ginzburg6\",\n \"ginzburg8\",\n \"ginzburg9\",\n \"homolosine\",\n \"kavrayskiy7\",\n \"larrivee\",\n \"laskowski\",\n \"mercator\",\n \"miller\",\n \"mollweide\",\n \"naturalEarth1\",\n \"naturalEarth2\",\n \"nellHammer\",\n \"nicolosi\",\n \"patterson\",\n \"robinson\",\n \"sinusoidal\",\n \"sinuMollweide\",\n \"times\",\n \"vanDerGrinten\",\n \"vanDerGrinten2\",\n \"vanDerGrinten3\",\n \"vanDerGrinten4\",\n \"wagner4\",\n \"wagner6\",\n \"winkel3\"\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":113,"value":"import {us} from \"7fe9cd66e7ed57fb\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":115,"value":"import {Plot} from \"@fil/plot-carto-0-5\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":120,"value":"topojson = require(\"topojson-client@3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":125,"value":"import {coloradoCounties} from \"@wiederkehra/call-boxes\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":129,"value":"coloradoFeatures = topojson.feature(coloradoCounties, coloradoCounties.objects.cb_2015_colorado_county_20m)","pinned":false,"hidden":false,"mode":"ojs"},{"id":230,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":259,"value":"isMobile = width < 768","pinned":false,"hidden":false,"mode":"ojs"},{"id":856,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":860,"value":"### Data","pinned":false,"hidden":false,"mode":"md"},{"id":858,"value":"Both the [mile post data](https://data.colorado.gov/Transportation/Highway-Mileposts-in-Colorado/trm9-dm4m) and the [annual roadkill reports](https://www.codot.gov/programs/environmental/wildlife/data/annual-roadkill-reports) are collected by the Colorado Department of Transportation.","pinned":false,"hidden":false,"mode":"md"},{"id":851,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":853,"value":"Little note to myself: Region Two data is [2020](https://www.codot.gov/programs/environmental/wildlife/data/annual-roadkill-reports/roadkill-data-2020.pdf), need to go back and grab [2021](https://www.codot.gov/programs/environmental/wildlife/data/annual-roadkill-reports/eoy2021.pdf) from the PDFs. ","pinned":false,"hidden":false,"mode":"md"}],"files":"$W36","stdlib":"1","titling":"auto"}