This data layer represents California General Plan Map information in the Russian River watershed. Features from the original source data (see Originator information) were selected based on their spatial location within the Russian River watershed in Sonoma and Mendocino counties in California (the study area boundary of the Russian River Watershed Adaptive Management Plan project). This metadata file incorporates much of the information that was available in the metadata of the original source data. This information is indicated in quotation marks. "We undertook creating the first ever seamless statewide General Plan map for California. All county general plans and many city general plans were integrated into 1 statewide Geographic Information System (GIS) dataset. The data was then standardized to thirteen consistent land use classifications for the intent of natural resource and infrastructure planning. This data represents one of the two-part GIS datasets. This part is the 'source' General Plans. The sister dataset is a likely scenario of current landuse more adequately representing current residential growth. This work took place at the University of California Davis. The data is freely available and distributed through the California Resources Agency. "
This data layer was edited from its original source data set for inclusion in the Russian River Watershed Adaptive Management Plan (RRWAMP) and RRWAMP Baseline Watershed Assessment project. The intended use of this data layer in this project is to examine landscape or human factors to promote ecological health and sustainability within the Russian River watershed.
"In order for the Resources Agency to be successful in its mission, a clear understanding of the present condition, and future direction of land use patterns needs to be analyzed. What makes the analysis of these patterns remarkably difficult, is that general planning, zoning, and land-use designation are all maintained at the County or local government level. While the Governor's Office of Planning and Research provides planning assistance and general planning guidelines, and many Council of Governments exist to help facilitate regional planning, there is no single entity mandated to coordinate how these plans look statewide. Moreover, county and local entities use a plethora of non-standardized language to classify land use designations. While many of these classifications might mean the same or similar things, few are articulated in exactly the same manner, impeding any cross boundary analysis. To meet the above challenges, the California Resources Agency Legacy Project entered into an Interagency Agreement with the University of California Davis, Information Center for the Environment. The agreement was charged with collecting all available digital general plan maps from county and local entities in California. Where these digital maps where not available, the agreement provided for creating the digital maps from paper maps provided by the county or local entity. The second phase of the project required the University to build a crosswalk of the source data's land use classification system, which would standardize the language defining land use in these maps. A significant impact to California's natural resources, human and built infrastructures is the increasing development of rural ranches. Developments of individual houses on 20 to 40 acre parcels has the potential of not just occupying a tremendous amount of California's open space, it might also negatively affect the states farming capacity. Moreover, this kind of development can easily overwhelm the rural transportation infrastructure build for much lower densities and have significant impact on water use and air quality. As such, this agreement requires the University to research and put in place a 'Very Low Density, Rural Residential' land use class. This class requires academic research and expert opinion to articulate where on the landscape this kind of development a) has happened b) is planned, and c) is likely to happen. The end product of this effort will be two Geographic Information Systems (GIS) digital maps. These maps will be placed in the Resources Agency Legacy Projects California Digital Conservation Atlas, and the California Environmental Research Evaluation System metadata Catalogue. The data will be free to download for anyone to use."
publication date
The user assumes the entire risk related to the use of this data. The developers of this project shall not have any liability to any person or entity with respect to loss or damage caused or alleged to be caused directly or indirectly by information contained in this file. The developers of this project make no warranty, expressed or implied, nor does the fact of distribution of this data constitute such warranty. This data is not for navigational purposes or for use in litigation. In all cases, the user should refer to the original source data and metadata for accuracy, currentness and appropriate contact information. " "
3909 Halls Ferry Road
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Data were checked for spatial/geographic logic and consistency.
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This data layer was created for the Russian River Watershed Adaptive Management Plan (RRWAMP) and RRWAMP Baseline Watershed Assessment project through minor editing to the original source data. Data were clipped to the project study area boundary (rr_watershed_bndy.shp) and projected using ESRI's ArcGIS GeoProcessing tools; and renamed to the current file name.
3909 Halls Ferry Road
"Processing Notes The following steps were taken in ArcView software to define LDR and VLDR: 1. Select luc = 1 in the county general plan table and convert them into a shapefile, which is named countyname_gpa. This shapefile is all of the agricultural land use designation. The low density residential designations in the county general plan, if any, will be kept as low density residential, as they are not in the agricultural polygons and so are not affected by our calculations. 2. Calculate acre/du for each census block, for 2000, in the census population layer countyname_cenblk00_90. 3. Use countyname_gpa to clip countyname_cenblk00_90 to get acres/du of each census block, but only within the general plan agriculture designation. The new shapefile is named as countyname_clip1. 4. In countyname_clip1, if acre/du < 100, then luc = 7, which is LDR; if acre/du > 100 and < 160, then luc = 13, which is VLDR. In the table of this shapefile, a new field "le_gpdes" which means "Legacy Program general plan land use description" is created so that both the new and original land use designations in general plan can be kept. We use a cutoff of 1 du < 100 acres, rather than the density threshold for our LDR category, because we are calculating density on whole census blocks and so a density of greater than 1 du on 100 acres implies smaller parcels somewhere within the block. We validated this method by examining developed parcel sizes in several counties for which we have parcel data. The developed parcels in these test counties are about 20-acres or smaller in the census blocks when we set the average density threshold for the blocks at 1 du/100 acres. We assume, due to legal and political considerations, that once a significant fraction of parcels are developed at LDR density that nearby parcels will also be able to develop at this density. So, we use the blocks as the initial spatial unit in which to make this calculation. 5. Union countyname_clip1 with countyname_gp, the county general plan layer, and name it countyname_gpt which represents the Legacy program temporary general plan file 6. Combine polygons to create spatial continuity of land use designations in countyname_gpt to create a new general plan which is named as countyname_gple in which le means Legacy Program. We do this judgementally, to create areas that consist of mostly census blocks with an average density > 1 du/100 acres. As these areas represent zoning designations, we included the intervening blocks as LDR, in order to tie together the blocks that met the density threshold. In general, our LDR designations look similar in size, or smaller than, those in the county plans that contain such designations. Also, zoning, to be legal, should generally include many parcels or large areas of parcels, so most counties use fairly big polygons for each category in rural parts of the county. Where county general plans included LDR and VLDR categories, we tested these against the census data, but in most cases simply kept the county designations. Post Processing Notes The following steps where taken in Arc/Info software to post process that data. 1) The Resources Agency is interested in delivering seamless data for conservation planning and resource investment planning. We would like to have this data set meet the general needs of 'seamlessness', data consistency and data quality. 2) The General Plan layer should have a consistent attribute structure. 3) The Resources Agency added and ensured consistency for County Names, FIPS Codes, and Alpha-numeric (county number) codes. 4) The Resources Agency ensuree a consistent spatial context for county boundaries The shapefiles delivered had excellent consistency for the LUC coding scheme. However, it had poor consistency for county boundaries. This problem stems from the multiple sources, multiple jurisdiction and use of Tiger files (most likely). Resources Agency went through the following process per county shapefile. 1) Convert to Arc/Info coverage 2) Ensure attribute consistency (item definition and population) 3) Dissolve on LUC 4) Clip to a standard county boundary (Using the Teale County Coverage). This step necessitated removing all General Plan polygons outside the Teale County Coverage, and attributing all new polygons inside the Teale County coverage and outside the General Plan coverage with the General Plan LUC for the adjoining General Plan polygon. A dissolve on LUC then happened again. If we did not run this step, then there would be tens of thousands of sliver polygons along the county edges. 5) Add and populate county name, FIPS and number 6) Assemble statewide coverage per county. 7) Frequency the LUC fields to show what percent of LUC class are present. 8) The process was performed for the GP data and the LE data. "
Metadata imported.
Internal feature number.
ESRI
Feature geometry.
ESRI
download from http://www.russianriverwatershed.net/Content/10006/GISDataforDownload.html