One of the largest hydraulic mines (1.6 km2) is located in California’s Sierra Nevada within the Humbug Creek watershed and Malakoff Diggins State Historic Park (MDSHP). MDSHP’s denuded and dissected landscape is composed of weathered Eocene auriferous sediments susceptible to chronic rill and gully erosion whereas block failures and debris flows occur in more cohesive terrain. This data release includes a 2014 digital elevation model (DEM), a study area boundary, and a geomorphic map. The 2014 DEM was derived from an available aerial LiDAR dataset collected in 2014 by the California Department of Conservation. The geomorphic map was derived for the study area from using a multi-scale spatial analysis. A topographic position index (TPI) was created using focal statistics to compare the elevations across the study area. We calculated a fine-scale TPI using a circular neighborhood with a radius of 25-meters and large-scale TPI using a circular neighborhood with a radius of 100-meters. In the resulting raster positive TPI values are assigned to cells with elevations higher than the surrounding area and negative TPI values are assigned to cells with elevations lower than the surrounding area. The geomorphic map was then created using a nested conditional statement to apply classification thresholds on the basis the fine and large-scale TPI rasters and a slope raster. Ten geomorphic feature classes were defined and the map can be symbolized by feature class. The geomorphic map includes both channel and hillslope features and can be used to assess erosional and depositional processes at the landscape scale.

This layer identifies Neighborhood Mobility Areas (NMA) in the region using the attribute Sum_Zscore as the operative attribute. The Tier 2 TAZs in this layer that have a numerical value of the attribute Sum_Zscore at or above the 80th percentile were NMAs for the purpose of Connect SoCal. The attributes in the file Int_Zscore, L_speed_Zs, Luent_Zsco, and Dist_Sum_Z were summed to create the attribute Sum_Zscore.

This part of DS 781 presents data for the bathymetric contours for several seafloor maps of the Offshore Scott Creek map area, California. The vector data file is included in "Contours_OffshoreScottCreek.zip", which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series--Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015-1191, pamphlet 40 p., 10 sheets, scale 1:24,000, http://doi.org/10.3133/ofr20151191. 10-m interval contours of the Offshore Scott Creek map area, California, were generated from bathymetry data collected by California State University, Monterey Bay (CSUMB), by Fugro Pelagos, and by the U.S. Geological Survey (USGS). Mapping was completed between 2006 and 2009, using a combination of 400-kHz Reson 7125 and 244-kHz Reson 8101 multibeam echosounders, as well as a 234-kHz SWATHplus bathymetric sidescan-sonar system. These mapping missions combined to collect bathymetry from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Bathymetric contours at 10-m intervals were generated from a modified 2-m bathymetric surface. The original surface was smoothed using the Focal Mean tool in ArcGIS and a circular neighborhood with a radius of 20 to 30 meters (depending on the area). The contours were generated from this smoothed surface using the ArcGIS Spatial Analyst Contour tool. The most continuous contour segments were preserved while smaller segments and isolated island polygons were excluded from the final output. The contours were then clipped to the boundary of the map area.

This map includes change areas for city and county boundaries filed in accordance with Government Code 54900. The initial dataset was first published on October 20, 2021, and was based on the State Board of Equalization's tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax jurisdictions. The boundaries are continuously being revised when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions and should not be used to determine precise city or county boundary line locations.The data is updated within 10 business days of the CDTFA receiving a copy of the Board of Equalization's acknowledgement letter.BOE_CityAnx Data Dictionary: COFILE = county number - assessment roll year - file number (see note*); CHANGE = affected city, unincorporated county, or boundary correction; EFFECTIVE = date the change was effective by resolution or ordinance (see note*); RECEIVED = date the change was received at the BOE; ACKNOWLEDGED = date the BOE accepted the filing for inclusion into the tax rate area system; NOTES = additional clarifying information about the action.*Note: A COFILE number ending in "000" is a boundary correction and the effective date used is the date the map was corrected.BOE_CityCounty Data Dictionary: COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the Board of Equalization's 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).

Boundaries determined by the 2020 California Citizens Redistricting Commission and derived from We Draw the Lines, released December 2021.Member names and contact information updated as-needed by the Board of Equalization.Final approved map by the 2020 California Citizens Redistricting Commission for the California State Board of Equalization Districts; the authoritative and official delineations of the California State Board of Equalization Districts drawn during the 2020 redistricting cycle. The Citizens Redistricting Commission for the State of California has created statewide district maps for the State Assembly, State Senate, State Board of Equalization, and United States Congress in accordance, with the provisions of Article XXI of the California Constitution. The Commission has approved the final maps and certified them to the Secretary of State.Line drawing criteria included population equality as required by the U.S. Constitution, the Federal Voting Rights Act, geographic contiguity, geographic integrity, geographic compactness, and nesting. Geography was defined by U.S. Census Block geometry.

The four Board of Equalization (BOE) districts have a population larger than most other states in the country. In consideration of population equality, the Commission chose to limit the population deviation to under 2%. The BOE is responsible for property tax programs, the alcoholic beverage tax, the tax on insurers, and the private railroad car tax, including conducting appraisals and audits of state-assessed public utility companies and railroad companies, and ensuring statewide uniformity in the assessment of properties by county assessors. Given this, the Commission recognized the relevant shared interests included business and economic interests. In addition, tax revenues are distributed to counties independent of electoral districts. The Commission’s BOE districts reflect a balancing of multiple requirements and interests, including maintaining, to the extent practicable, county, city, neighborhood, and community of interest boundaries. In particular, because the main mission of the BOE focuses on county tax assessment, the Commission attempted to keep counties whole in these districts.

description: This part of DS 781 presents data for the bathymetric contours for several seafloor maps of the Offshore of Aptos map area, California. The vector data file is included in "Contours_OffshoreAptos.zip," which is accessible from http://dx.doi.org/10.5066/F7K35RQB. 10-m interval contours of the Offshore Aptos map area, California, were generated from bathymetry data collected by the U.S. Geological Survey (USGS) and by California State University, Monterey Bay (CSUMB). Mapping was completed between 2006 and 2009 using a combination of a 244-kHz Reson 8101 multibeam echosounder and a 234-kHz SEA SWATHplus bathymetric sidescan-sonar system. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California’s State Waters. Bathymetric contours at 10-m intervals were generated from a modified 2-m bathymetric surface. The original surface was smoothed using the Focal Mean tool in ArcGIS and a circular neighborhood with a radius of 20 to 30 meters (depending on the area). The contours were generated from this smoothed surface using the ArcGIS Spatial Analyst Contour tool. The most continuous contour segments were preserved while smaller segments and isolated island polygons were excluded from the final output. The contours were then clipped to the boundary of the map area.; abstract: This part of DS 781 presents data for the bathymetric contours for several seafloor maps of the Offshore of Aptos map area, California. The vector data file is included in "Contours_OffshoreAptos.zip," which is accessible from http://dx.doi.org/10.5066/F7K35RQB. 10-m interval contours of the Offshore Aptos map area, California, were generated from bathymetry data collected by the U.S. Geological Survey (USGS) and by California State University, Monterey Bay (CSUMB). Mapping was completed between 2006 and 2009 using a combination of a 244-kHz Reson 8101 multibeam echosounder and a 234-kHz SEA SWATHplus bathymetric sidescan-sonar system. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California’s State Waters. Bathymetric contours at 10-m intervals were generated from a modified 2-m bathymetric surface. The original surface was smoothed using the Focal Mean tool in ArcGIS and a circular neighborhood with a radius of 20 to 30 meters (depending on the area). The contours were generated from this smoothed surface using the ArcGIS Spatial Analyst Contour tool. The most continuous contour segments were preserved while smaller segments and isolated island polygons were excluded from the final output. The contours were then clipped to the boundary of the map area.

This part of DS 781 presents bathymetric contours for several seafloor maps of the Monterey Canyon and Vicinity map area, California. The shapefile is included in "Contours_MontereyCanyon.zip," which is accessible from https://doi.org/10.3133/ofr20161072. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. Bathymetric contours of the Monterey Canyon and Vicinity map area, California, were generated from bathymetry data collected by the U.S. Geological Survey (USGS), by Monterey Bay Aquarium Research Institute (MBARI), and by California State University, Monterey Bay (CSUMB). Mapping was completed between 1998 and 2014 using a combination of 30-kHz Simrad EM-300 and 200-kHz/400-kHz Reson 7125 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHplus bathymetric sidescan-sonar systems. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Bathymetric contours were generated separately from the modified 2-m and 5-m bathymetric surface then merged to one final contour dataset. 10-m intervals were generated in water depths shallower than 100 m, at 50-m intervals from 100 to 200 m, and at 200-m intervals in water depths deeper than 200 m. The original surface was smoothed using the Focal Mean tool in ArcGIS and a circular neighborhood with a radius of 20 to 30 m (depending on the area). The contours were generated from this smoothed surface using the ArcGIS Spatial Analyst Contour tool. The most continuous contour segments were preserved; smaller segments and isolated island polygons were excluded from the final output.

description: This part of DS 781 presents data for the bathymetric contours for several seafloor maps of the Offshore Santa Cruz map area, California. The vector data file is included in "Contours_OffshoreSantaCruz.zip", which is accessible from http://dx.doi.org/10.5066/F7TM785G. 10-m interval contours of the Offshore Santa Cruz map area, California, were generated from bathymetry data collected by the U.S. Geological Survey (USGS). Mapping was completed in 2009 using a 234-kHz SWATHplus bathymetric sidescan-sonar system. The mapping mission collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California ’s State Waters. Bathymetric contours at 10-m intervals were generated from a modified 2-m bathymetric surface. The original surface was smoothed using the Focal Mean tool in ArcGIS and a circular neighborhood with a radius of 20 to 30 meters (depending on the area). The contours were generated from this smoothed surface using the ArcGIS Spatial Analyst Contour tool. The most continuous contour segments were preserved while smaller segments and isolated island polygons were excluded from the final output. The contours were then clipped to the boundary of the map area.; abstract: This part of DS 781 presents data for the bathymetric contours for several seafloor maps of the Offshore Santa Cruz map area, California. The vector data file is included in "Contours_OffshoreSantaCruz.zip", which is accessible from http://dx.doi.org/10.5066/F7TM785G. 10-m interval contours of the Offshore Santa Cruz map area, California, were generated from bathymetry data collected by the U.S. Geological Survey (USGS). Mapping was completed in 2009 using a 234-kHz SWATHplus bathymetric sidescan-sonar system. The mapping mission collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California ’s State Waters. Bathymetric contours at 10-m intervals were generated from a modified 2-m bathymetric surface. The original surface was smoothed using the Focal Mean tool in ArcGIS and a circular neighborhood with a radius of 20 to 30 meters (depending on the area). The contours were generated from this smoothed surface using the ArcGIS Spatial Analyst Contour tool. The most continuous contour segments were preserved while smaller segments and isolated island polygons were excluded from the final output. The contours were then clipped to the boundary of the map area.

The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we mapped the landscape of financial services providers and advertisements in low-income neighborhoods in Orange County. Through documenting the presence of alternative financial services (AFS) providers and fringe financial advertisements, alongside traditional financial services providers, we investigated the spatial relationship between these businesses, as well as the factors that create consumers’ sense of (dis)trust in them. This data set contains photographs taken as part of this mapping research. All study materials and procedures were approved by the University of California, Irvine Office of..., Data was collected over the course of five trips throughout Orange County, California, between November 2021 and February 2022, yielding 420 photographs. Areas of focus were determined by utilizing the 2019 Family Financial Stability Index (FFSI; Parsons et al.), a multivariate metric developed for Orange County United Way to measure the financial stability of families with children under 18. Each trip, researchers navigated to financial services providers in neighborhoods of low family financial stability. In addition to photographing these providers, researchers drove block-by-block through the area and documented traditional and fringe financial advertisements found on telephone poles, billboards, bus shelters, and the like. Photographs were only taken in public spaces of material in plain view., Photographs are organized in folders according to trip (labeled A through E). Each photo is labeled by the trip and a number (e.g. “TripX_AdMapping_X.jpeg†). The photo directory associated with each trip contains the photo file names, descriptions and notes, and type (billboard, storefront, phone pole ad, etc.). Trip A took place in southern Santa Ana and western Orange; trip B was in northern Santa Ana and southern Anaheim; trip C was in northern Anaheim, Placentia, and Fullerton; trip D was in western Anaheim and northern Garden Grove; and trip E was in western Anaheim, northern Garden Grove, and Westminster. A map of Orange County coded according to the FFSI is included in the supplemental information (where red and dark orange indicate a neighborhood with a low score). The map also identifies local credit unions, community research partners, alternative financial services providers, and a selection of photographs from the mapping research.,

This part of DS 781 presents bathymetric contours for several seafloor maps of the Offshore of Monterey map area, California. This metadata file refers to the data included in "Contours_OffshoreMonterey.zip," which is accessible from https://doi.org/10.5066/F70Z71C8. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Hartwell, S.R., Cochrane, G.R., Golden, N.E., Watt, J.T., Davenport, C.W., Kvitek, R.G., Erdey, M.D., Krigsman, L.M., Sliter, R.W., and Maier, K.L. (S.Y. Johnson and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Monterey, California: U.S. Geological Survey Open-File Report 2016–1110, pamphlet 44 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161110. Bathymetric contours of the Offshore of Monterey map area, California, were generated from bathymetry data collected by California State University, Monterey Bay (CSUMB) and by Monterey Bay Aquarium Research Institute (MBARI), as well as from bathymetric lidar data collected by the U.S. Army Corps of Engineers, Joint Airborne Lidar Bathymetry Center of Expertise (JALBTCX). Mapping was completed between 1998 and 2012 using a combination of 30-kHz Simrad EM-300 and 200-kHz/400-kHz Reson 7125 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHplus bathymetric sidescan-sonar systems. Bathymetric lidar mapping was completed between 2009 and 2010 for the California Coastal Mapping Project (CCMP). The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Bathymetric contours were generated separately from the modified 2-m and 5-m bathymetric surfaces then merged to one final contour dataset. 10-m intervals were generated in water depths shallower than 100 m, at 50-m intervals from 100 to 200 m, and at 200-m intervals in water depths deeper than 200 m. The original surface was smoothed using the Focal Mean tool in ArcGIS and a circular neighborhood with a radius of 20 to 30 m (depending on the area). The contours were generated from this smoothed surface using the ArcGIS Spatial Analyst Contour tool. The most continuous contour segments were preserved; smaller segments and isolated island polygons were excluded from the final output.

This data publication contains two collections of raster maps of forest attributes across Canada, the first collection for year 2001, and the second for year 2011. The 2001 collection is actually an improved version of an earlier set of maps produced also for year 2001 (Beaudoin et al 2014, DOI: https://doi.org/10.1139/cjfr-2013-0401) that is itself available through the web site “http://nfi-nfis.org”. Each collection contains 93 maps of forest attributes: four land cover classes, 11 continuous stand-level structure variables such as age, volume, biomass and height, and 78 continuous values of percent composition for tree species or genus. The mapping was done at a spatial resolution of 250m along the MODIS grid. Briefly the method uses forest polygon information from the first version of photoplots database from Canada’s National Forest Inventory as reference data, and the non-parametric k-nearest neighbors procedure (kNN) to create the raster maps of forest attributes. The approach uses a set of 20 predictive variables that include MODIS spectral reflectance data, as well as topographic and climate data. Estimates are carried out on target pixels across all Canada treed landmass that are stratified as either forest or non-forest with 25% forest cover used as a threshold. Forest cover information was extracted from the global forest cover product of Hansen et al (2013) (DOI: https://doi.org/10.1126/science.1244693). The mapping methodology and resultant datasets were intended to address the discontinuities across provincial borders created by their large differences in forest inventory standards. Analysis of residuals has failed to reveal residual discontinuities across provincial boundaries in the current raster dataset, meaning that our goal of providing discontinuity-free maps has been reached. The dataset was developed specifically to address strategic issues related to phenomena that span multiple provinces such as fire risk, insect spread and drought. In addition, the use of the kNN approach results in the maintenance of a realistic covariance structure among the different variable maps, an important property when the data are extracted to be used in models of ecosystem processes. For example, within each pixel, the composition values of all tree species add to 100%. * Details on the product development and validation can be found in the following publication: Beaudoin, A., Bernier, P.Y., Villemaire, P., Guindon, L., Guo, X.-J. 2017. Tracking forest attributes across Canada between 2001 and 2011 using a kNN mapping approach applied to MODIS imagery, Canadian Journal of Forest Research 48: 85–93. DOI: https://doi.org/10.1139/cjfr-2017-0184 * Please cite this dataset as: Beaudoin A, Bernier PY, Villemaire P, Guindon L, Guo XJ. 2017. Species composition, forest properties and land cover types across Canada’s forests at 250m resolution for 2001 and 2011. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990 * This dataset contains these NFI forest attributes: ## LAND COVER : landbase vegetated, landbase non-vegetated, landcover treed, landcover non-treed ## TREE STRUCTURE : total above ground biomass, tree branches biomass, tree foliage biomass, stem bark biomass, stem wood biomass, total dead trees biomass, stand age, crown closure, tree stand heigth, merchantable volume, total volume ## TREE SPECIES : abies amabilis (amabilis fir), abies balsamea (balsam fir), abies lasiocarpa (subalpine fir), abies spp. (unidentified fir), acer macrophyllum (bigleaf maple), acer negundo (manitoba maple, box-elder), acer pensylvanicum (striped maple), acer rubrum (red maple), acer saccharinum (silver maple), acer saccharum (sugar maple), acer spicatum (mountain maple), acer spp. (unidentified maple), alnus rubra (red alder), alnus spp. (unidentified alder), arbutus menziesii (arbutus), betula alleghaniensis (yellow birch), betula papyrifera (white birch), betula populifolia (gray birch), betula spp. (unidentified birch), carpinus caroliniana (blue-beech), carya cordiformis (bitternut hickory), chamaecyparis nootkatensis (yellow-cedar), fagus grandifolia (american beech), fraxinus americana (white ash), fraxinus nigra (black ash), fraxinus pennsylvanica (red ash), juglans cinerea (butternut), juglans nigra (black walnut), juniperus virginiana (eastern redcedar), larix laricina (tamarack), larix lyallii (subalpine larch), larix occidentalis (western larch), larix spp. (unidentified larch), malus spp. (unidentified apple), ostrya virginiana (ironwood, hop-hornbeam), picea abies (norway spruce), picea engelmannii (engelmann spruce), picea glauca (white spruce), picea mariana (black spruce), picea rubens (red spruce), picea sitchensis (sitka spruce), picea spp. (unidentified spruce), pinus albicaulis (whitebark pine), pinus banksiana (jack pine), pinus contorta (lodgepole pine), pinus monticola (western white pine), pinus ponderosa (ponderosa pine), pinus resinosa (red pine), pinus spp. (unidentified pine), pinus strobus (eastern white pine), pinus sylvestris (scots pine), populus balsamifera (balsam poplar), populus grandidentata (largetooth aspen), populus spp. (unidentified poplar), populus tremuloides (trembling aspen), populus trichocarpa (black cottonwood), prunus pensylvanica (pin cherry), prunus serotina (black cherry), pseudotsuga menziesii (douglas-fir), quercus alba (white oak), quercus macrocarpa (bur oak), quercus rubra (red oak), quercus spp. (unidentified oak), salix spp. (unidentified willow), sorbus americana (american mountain-ash), thuja occidentalis (eastern white-cedar), thuja plicata (western redcedar), tilia americana (basswood), tsuga canadensis (eastern hemlock), tsuga heterophylla (western hemlock), tsuga mertensiana (mountain hemlock), tsuga spp. (unidentified hemlock), ulmus americana (white elm), unidentified needleaf, unidentified broadleaf, broadleaf species, needleaf species, unknown species

This part of DS 781 presents data for the bathymetric contours for several seafloor maps of the Offshore Pigeon Point map area, California. The vector data file is included in "Contours_OffshorePigeonPoint.zip", which is accessible from https://doi.org/10.5066/F7513W80. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Pigeon Point, California: U.S. Geological Survey Open-File Report 2015–1232, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151232. 10-m interval contours of the Offshore Pigeon Point map area, California, were generated from bathymetry data collected by the U.S. Geological Survey (USGS) and by California State University, Monterey Bay (CSUMB). Mapping was completed between 2006 and 2009 using a combination of a 244-kHz Reson 8101 multibeam echosounder and a 234-kHz SEA SWATHplus bathymetric sidescan-sonar system. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Bathymetric contours at 10-m intervals were generated from a modified 2-m bathymetric surface. The original surface was smoothed using the Focal Mean tool in ArcGIS and a circular neighborhood with a radius of 20 to 30 meters (depending on the area). The contours were generated from this smoothed surface using the ArcGIS Spatial Analyst Contour tool. The most continuous contour segments were preserved while smaller segments and isolated island polygons were excluded from the final output. The contours were then clipped to the boundary of the map area. These data are not intended for navigational purposes.

The raster maps depict a suite of forest attributes in 2001* and 2011 at 250 m by 250 m spatial resolution. The maps were produced using the k nearest neighbours method applied to MODIS imagery and trained from National Forest Inventory photo plot data. For detailed information about map production methods please refer to Beaudoin et al. (2018) "Tracking forest attributes across Canada between 2001 and 2011 using the k nearest neighbours mapping approach applied to MODIS imagery." Canadian Journal of Forest Research 48, 85-93. https://cfs.nrcan.gc.ca/publications?id=38979 The map datasets may be downloaded from https://nfi.nfis.org/downloads/nfi_knn2011.zip or https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990 Note: the forest composition (leading tree genus) map depicts forest attributes in 2001. How can this data be used? The resolution and accuracy of these map products are best suited for strategic-level forest reporting and informing policy and decision making at regional to national scales. As these maps also offer a coherent set of quantitative values for a large suite of forest attributes, they can be used as baseline information for modelling and in calculations such as merchantable forest volume or percentage of tree species. It is also possible to overlay these maps with other maps produced on the same pixel grid to make assessments of disturbance impacts, such as fire and harvests.

This line shapefile contains bathymetric contours at 10 meter intervals for the offshore area of Tomales Point, California. This layer was generated from bathymetry data collected by Fugro Pelagos and California State University, Monterey Bay, Seafloor Mapping Lab (CSUMB) and by the U.S. Geological Survey. Mapping was completed between 2004 and 2010, using a combination of 200-kHz and 400-kHz Reson 7125, and 244-kHz Reson 8101 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHPlus phase-differencing sidescan sonars. These mapping missions combined to collect bathymetry from about the 10-m isobath to beyond the 3-nautical-mile limit of Californiaís State Waters. Bathymetric contours at 10-m intervals were generated from a modified 2-m bathymetric surface. The original surface was smoothed using the Focal Mean tool in ArcGIS and a circular neighborhood with a radius of 20 to 30 meters (depending on the area). The contours were generated from this smoothed surface using the ArcGIS Spatial Analyst Contour tool. The most continuous contour segments were preserved while smaller segments and isolated island polygons were excluded from the final output. The contours were then clipped to the boundary of the map area. A map that shows these data is published in Open-File Report 2015-1088, "California State Waters Map Series--Offshore of Tomales Point, California." This layer is part of USGS Data Series 781.

The raster maps depict a suite of forest attributes in 2001* and 2011 at 250 m by 250 m spatial resolution. The maps were produced using the k nearest neighbours method applied to MODIS imagery and trained from National Forest Inventory photo plot data. For detailed information about map production methods please refer to Beaudoin et al. (2018) "Tracking forest attributes across Canada between 2001 and 2011 using the k nearest neighbours mapping approach applied to MODIS imagery." Canadian Journal of Forest Research 48, 85-93. https://cfs.nrcan.gc.ca/publications?id=38979 The map datasets may be downloaded from https://nfi.nfis.org/downloads/nfi_knn2011.zip or https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990 Note: the forest composition (leading tree genus) map depicts forest attributes in 2001. How can this data be used? The resolution and accuracy of these map products are best suited for strategic-level forest reporting and informing policy and decision making at regional to national scales. As these maps also offer a coherent set of quantitative values for a large suite of forest attributes, they can be used as baseline information for modelling and in calculations such as merchantable forest volume or percentage of tree species. It is also possible to overlay these maps with other maps produced on the same pixel grid to make assessments of disturbance impacts, such as fire and harvests.

The raster maps depict a suite of forest attributes in 2001* and 2011 at 250 m by 250 m spatial resolution. The maps were produced using the k nearest neighbours method applied to MODIS imagery and trained from National Forest Inventory photo plot data. For detailed information about map production methods please refer to Beaudoin et al. (2018) "Tracking forest attributes across Canada between 2001 and 2011 using the k nearest neighbours mapping approach applied to MODIS imagery." Canadian Journal of Forest Research 48, 85-93. https://cfs.nrcan.gc.ca/publications?id=38979 The map datasets may be downloaded from https://nfi.nfis.org/downloads/nfi_knn2011.zip or https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990 Note: the forest composition (leading tree genus) map depicts forest attributes in 2001. How can this data be used? The resolution and accuracy of these map products are best suited for strategic-level forest reporting and informing policy and decision making at regional to national scales. As these maps also offer a coherent set of quantitative values for a large suite of forest attributes, they can be used as baseline information for modelling and in calculations such as merchantable forest volume or percentage of tree species. It is also possible to overlay these maps with other maps produced on the same pixel grid to make assessments of disturbance impacts, such as fire and harvests.

This EnviroAtlas dataset estimates tree cover along walkable roads. The road width is estimated for each road and percent tree cover is calculated in a 8.5 meter strip beginning at the estimated road edge. Percent tree cover is calculated for each block between road intersections. Tree cover provides valuable benefits to neighborhood residents and walkers by providing shade, improved aesthetics, and outdoor gathering spaces. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The raster maps depict a suite of forest attributes in 2001* and 2011 at 250 m by 250 m spatial resolution. The maps were produced using the k nearest neighbours method applied to MODIS imagery and trained from National Forest Inventory photo plot data. For detailed information about map production methods please refer to Beaudoin et al. (2018) "Tracking forest attributes across Canada between 2001 and 2011 using the k nearest neighbours mapping approach applied to MODIS imagery." Canadian Journal of Forest Research 48, 85-93. https://cfs.nrcan.gc.ca/publications?id=38979 The map datasets may be downloaded from https://nfi.nfis.org/downloads/nfi_knn2011.zip or https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990 Note: the forest composition (leading tree genus) map depicts forest attributes in 2001. How can this data be used? The resolution and accuracy of these map products are best suited for strategic-level forest reporting and informing policy and decision making at regional to national scales. As these maps also offer a coherent set of quantitative values for a large suite of forest attributes, they can be used as baseline information for modelling and in calculations such as merchantable forest volume or percentage of tree species. It is also possible to overlay these maps with other maps produced on the same pixel grid to make assessments of disturbance impacts, such as fire and harvests.

This map depicts whether each neighborhood contained more housing in 2010 or in 2000. The data compares the 2010 and 2000 Census counts of housing units at state, count, tract and block level.The map has a mask applied to highlight a particular congressional district. Edit the filter on the congressional districts layer to focus on another district.