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Layer: Campgrounds_April (ID:3)

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Name: Campgrounds_April

Display Field: Name

Type: Feature Layer

Geometry Type: esriGeometryPoint

Description: Data used: California State Parks Campsites https://www.parks.ca.gov/?page_id=29682 (retrieved 12/5/23)Campgrounds (BLM and USFS) https://www.arcgis.com/home/item.html?id=77f2d0c34cb849338fac047d3a709d28 (retrieved 12/5/23)OpenStreetMap Northern California and Southern California https://download.geofabrik.de/north-america/us/california.html (retrieved 12/13/23)USGS North America Climate – Monthly Mean Temperature https://www.sciencebase.gov/catalog/item/4fb5528ee4b04cb937751d9e (retrieved 12/20/23)California Native Diversity Databasehttps://wildlife.ca.gov/Data/CNDDB (retrieved 12/10/23)California State Boundary https://data.ca.gov/dataset/ca-geographic-boundaries (retrieved 12/5/23)Methods: To prepare the campgrounds layer, which would be the destination of my analysis products, I first merged the datasets from California State Parks and the BLM and USFS campgrounds, mapping like fields together and cleaning unnecessary fields. I then clipped the new layer to the boundary of California from the Tiger layer. I then generated isochrones for the state of California. The first step of this was to create a network of roads to conduct the network analysis on. I used the Open Street Map data, which came in Northern and Southern California segments. I merged the two to create a statewide road network, then selected and removed any non driving roads, such as hiking paths of private roads. Many roads had speed limit fields, which I used as the basis for the travel speed field that I created. For roads that didn’t have a speed limit, I assumed a travel speed of 25mph, or 40kph, which although is just a ballpark, should be fairly representative averaging faster and slower roads. This processed roads layer was brought in to QGIS, where I used the QNEAT3 plugin to generate isochrones. I based the analysis on my home address in El Cerrito, in the East Bay, using a cell size of 1000, and an area of 38000 seconds total, which covered the entire state. The tool was set to find the quickest speed based on the travel speed field I created, and produced a raster that covered all of the campsite points. To assess climate at each site, I retrieved the average climate per month layers from the USGS. These were opened and georeferenced in QGIS as they were a format that ArcGIS could not open. I then exported each as a GeoTIFF and opened them in ArcGIS, where clipped to the state boundary, then converted them to points. I then used kriging to extrapolate a few pixels width along the coast, as the data didn’t quite cover all of the coastal sites. This left me with 12 rasters with temperature values for the entirety of the state.The next component of my analysis was to evaluate the biodiversity of each campsite, which I used the California Native Diversity Database as a proxy for. This is a restricted access database, and as such the information will not be provided in this analysis. I downloaded the most recent shapefile, and buffered the polygons by 1km. I then ran the following script in Arcade [var pol = FeatureSetByName($datastore, "CNDDB_Buffered"); var countPol = Count(Intersects(pol, $feature)); Return countPol;] in the field calculator to create a new field counting the CNDDB polygons overlapping each campsite. I then extracted point values for the created isochrones layer and each of the 12 temperature layers to their own respective fields. These raw values were scored on a scale of 0-20 depending on how favorable they were for the campsite. For context, I favored drive times between 1 and 5 hours, with it decaying towards 0 in the farther reaches of the state. For average temperature, I favored 7.5C to 17.5C, with the score of 20 between 12.5C and 15C. I believe this maintains daily highs and lows in a comfortable zone. I created a similar scale for overlapping CNDDB polygons, where 80-40 overlapping were awarded 20, 40-20 were awarded 18, but then decaying more rapidly to a base score of 1. Finally, I created 12 fields titled Total_(month) and summed the scores for each month into them. This gives all campsites a score out of 60 of how appropriate they would be in the selected month.A Guide to Fields:drivetimeminutes: time it takes in minutes to drive to campsitedrivevalue: score out of 20 of how acceptable the drive to the campsite is, in terms of timedrivetimehours: time it takes in hours to drive to campsite(month)_exp: average temperature of the campsite in degrees C X 10(month)_score: score out of 20 of how acceptable the temperature is for campingoverlap_count: number of buffered CNDDB polygons that overlap each campsiteCNDDB_score: score out of 20 of how many buffered CNDDB polygons each site overlapsTotal_(month): score out of 60, summing drivevalue, (month)_score, and CNDDB_score. This is the final score that appraises the suitability of the campsite, in which higher values indicate likeliness to be a good site

Copyright Text: USGS, California State Parks, OpenStreetMaps, California Department of Fish and Wildlife, Bureau of Land Management, US Forest Service, State of California

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Max Record Count: 2000

Supported query Formats: JSON, geoJSON, PBF

Use Standardized Queries: True

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HTML Popup Type: esriServerHTMLPopupTypeAsHTMLText

Object ID Field: OBJECTID

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Supports Rollback On Failure Parameter: true

Last Edit Date: 1/8/2024 11:11:43 PM

Schema Last Edit Date: 1/8/2024 11:11:43 PM

Data Last Edit Date: 1/8/2024 11:11:43 PM

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