CDFW BIOS GIS Dataset, Contact: John Radke, Description: This modeled data represents inundation location and depth (meters) for the California Coast resulting from 1.41 m sea level rise coupled with extreme storm events. This research is unique and innovative in its dynamic spatial detail and the fact that it incorporates real, time series water level data from past (near 100 year) storm events to capture the dynamic effect of storm surges in modeling inundation using 3Di.

This map allows users to view predicted flood data for 10, 50, 100 and 500 year floods.Users can also report issues in real time to map administrators by using the Arc Collector App. Photos and comments can be inserted with a mobile device to show areas of interest or concern.

This publication contains a georeferenced 1936 map of a control survey by the United States Coast and Geodetic Survey, the United States Geologic Survey, the United States Forest Service and other surveys. It was surveyed from 1933 to 1936 under the supervision of the Forest Supervisor. Four inch (4") field sheets were prepared from aerial and ground surveys and reduced at the regional office in Atlanta, GA. The map was traced in 1935 and 1936.This map indicates property ownership in Berkeley County, South Carolina in 1936 and includes the area of the Santee Experimental Forest (SEF).The map has been georeferenced so that other SEF spatial data can be overlaid on the map in a GIS program. The SEF is located in the southeastern portion of the map, as the rest of the ownership parcels are within Berkeley County.

Projection: UTM, zone 10, datum NAD83.

GIS file format: ESRI Shapefile for vector, ESRI arcinfo binary GRID format for raster.

Data Sources: National Center for Airborne Laser Mapping (NCALM, http://ncalm.berkeley.edu): Lidar DEM of the South Fork Eel watershed at Angelo reserve was created by NCALM.

This data is new and still is being post processed. The dem is extremely high quality (1m resolution). California Spatial Information Library (CASIL, http://gis.ca.gov): public and federal datasets, including USGS drg, doqq, and blue-line datasets.

Naming Conventions: This is not strictly followed. Files start with their spatial scale and end with their projection. Maps will often end with their DPI resolution. Eel: entire eel watershed Sfk: South Fork Eel Nfk: North Fork Eel Angelo: Angelo Reserve

The Eel River CZO operates on several spatial scales from a zero order hillslope to the entire Eel River on the north coast of California. Rivendell, Angelo, Sagehorn, South Fork, and Eel River GIS boundaries. GIS polygon shapefiles. All files are in geographic projection (Lat/Long) with a datum of WGS84.

The watershed boundaries are from USGS Watershed Boundary Dataset (WBD) http://nhd.usgs.gov/wbd.html. Rivendell and Angelo boundaries are created from LiDAR by the CZO. Sagehorn Ranch is a privately held, active commercial ranch with no public access. Please contact the CZO if you are interested in data from Sagehorn Ranch.

Shapefiles

Eel River Watershed (drainage area 9534 km^2): Entire eel river. Greatest extent of CZO research.

South Fork Eel Watershed (drainage area 1784 km^2).

Angelo Reserve Boundary (30.0 km^2): Angelo Coast Range Reserve is a University of California Natural Reserve System protected land. It is the central focus of CZO research. http://angelo.berkeley.edu

Sagehorn Ranch Boundary (21.1 km^2): Sagehorn Ranch is a private ranch with active cattle raising. The owners have allowed the CZO to place instrumentation on their lands. Access is only by explicit agreement by owners.

Rivendell Cachement (0.0076 km^2): Rivendell is a small, heavily instrumented hillslope within the Angelo Reserve. It has roughly 700 instruments deployed as of 2016. Data is online at http://sensor.berkeley.edu

berkeley floods

Geologic map of the White Hall Quadrangle, Frederick Co., Virginia, and Berkeley Co., West Virginia. GIS files available for this geologic map. The base maps for this series were developed from U.S. Geological Survey topographic 7.5-minute quadrangle maps (1:24,000 scale). Contour interval is in feet. For more information on this resource or to download the map PDF, please see the links provided.

A. Takhtajan defined 35 floristic regions in the world (Takhtajan 1986). The delineation of such floristic regions has been manually georeferenced and is provided here as a spatial vectorial data file (geopackage), suitable to be used in any GIS or mapping software (coordinate reference system: EPSG 4326).

If using this dataset, please cite both Takhtajan's book as well as this data source:

Takhtajan, A. 1986. Floristic Regions of the World. Berkeley: University of California Press.

Rodríguez-Sánchez, Francisco. 2023. Takhtajan's floristic regions of the world (geopackage). https://doi.org/10.5281/zenodo.8206377

Funding: Fondo Europeo de Desarrollo Regional (FEDER) and Consejería de Transformación Económica, Industria, Conocimiento y Universidades of Junta de Andalucía (proyecto US-1381388, Universidad de Sevilla).

This dataset contains a 30-year rolling average of annual average minimum and maximum temperatures from the four models and two greenhouse gas (RCP) scenarios included in the four model ensemble for the years 1950-2099.The year identified is the mid-point of the 30-year average. eg. The year 2050 includes the values from 2036 to 2065.

The downscaling and selection of models for inclusion in ten and four model ensembles is described in Pierce et al. 2018, but summarized here. Thirty two global climate models (GCMs) were identified to meet the modeling requirements. From those, ten that closely simulate California’s climate were selected for additional analysis (Table 1, Pierce et al. 2018) and to form a ten model ensemble. From the ten model ensemble, four models, forming a four model ensemble, were identified to provide coverage of the range of potential climate outcomes in California. The models in the four model ensemble and their general climate projection for California are:

HadGEM2-ES (warm/dry),CanESM2 (average), CNRM-CM5 (cooler/wetter), and MIROC5 the model least like the others to improve coverage of the range of outcomes.

These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff.

Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved from https://cal-adapt.org/

Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.

This GIS dataset consists of polygons that represent the footprints of solar powered electric generation facilities and related infrastructure in California called Solar Footprints. The location of solar footprints was identified using other existing solar footprint datasets from various sources along with imagery interpretation. CEC staff reviewed footprints identified with imagery and digitized polygons to match the visual extent of each facility. Previous datasets of existing solar footprints used to locate solar facilities include: GIS Layers: (1) California Solar Footprints, (2) UC Berkeley Solar Points, (3) Kruitwagen et al. 2021, (4) BLM Renewable Project Facilities, (5) Quarterly Fuel and Energy Report (QFER)Imagery Datasets: Esri World Imagery, USGS National Agriculture Imagery Program (NAIP), 2020 SENTINEL 2 Satellite Imagery, 2023Solar facilities with large footprints such as parking lot solar, large rooftop solar, and ground solar were included in the solar footprint dataset. Small scale solar (approximately less than 0.5 acre) and residential footprints were not included. No other data was used in the production of these shapes. Definitions for the solar facilities identified via imagery are subjective and described as follows: Rooftop Solar: Solar arrays located on rooftops of large buildings. Parking lot Solar: Solar panels on parking lots roughly larger than 1 acre, or clusters of solar panels in adjacent parking lots. Ground Solar: Solar panels located on ground roughly larger than 1 acre, or large clusters of smaller scale footprints. Once all footprints identified by the above criteria were digitized for all California counties, the features were visually classified into ground, parking and rooftop categories. The features were also classified into rural and urban types using the 42 U.S. Code § 1490 definition for rural. In addition, the distance to the closest substation and the percentile category of this distance (e.g. 0-25th percentile, 25th-50th percentile) was also calculated. The coverage provided by this data set should not be assumed to be a complete accounting of solar footprints in California. Rather, this dataset represents an attempt to improve upon existing solar feature datasets and to update the inventory of "large" solar footprints via imagery, especially in recent years since previous datasets were published. This procedure produced a total solar project footprint of 150,250 acres. Attempts to classify these footprints and isolate the large utility-scale projects from the smaller rooftop solar projects identified in the data set is difficult. The data was gathered based on imagery, and project information that could link multiple adjacent solar footprints under one larger project is not known. However, partitioning all solar footprints that are at least partly outside of the techno-economic exclusions and greater than 7 acres yields a total footprint size of 133,493 acres. These can be approximated as utility-scale footprints. Metadata: (1) CBI Solar FootprintsAbstract: Conservation Biology Institute (CBI) created this dataset of solar footprints in California after it was found that no such dataset was publicly available at the time (Dec 2015-Jan 2016). This dataset is used to help identify where current ground based, mostly utility scale, solar facilities are being constructed and will be used in a larger landscape intactness model to help guide future development of renewable energy projects. The process of digitizing these footprints first began by utilizing an excel file from the California Energy Commission with lat/long coordinates of some of the older and bigger locations. After projecting those points and locating the facilities utilizing NAIP 2014 imagery, the developed area around each facility was digitized. While interpreting imagery, there were some instances where a fenced perimeter was clearly seen and was slightly larger than the actual footprint. For those cases the footprint followed the fenced perimeter since it limits wildlife movement through the area. In other instances, it was clear that the top soil had been scraped of any vegetation, even outside of the primary facility footprint. These footprints included the areas that were scraped within the fencing since, especially in desert systems, it has been near permanently altered. Other sources that guided the search for solar facilities included the Energy Justice Map, developed by the Energy Justice Network which can be found here:https://www.energyjustice.net/map/searchobject.php?gsMapsize=large&giCurrentpageiFacilityid;=1&gsTable;=facility&gsSearchtype;=advancedThe Solar Energy Industries Association’s “Project Location Map” which can be found here: https://www.seia.org/map/majorprojectsmap.phpalso assisted in locating newer facilities along with the "Power Plants" shapefile, updated in December 16th, 2015, downloaded from the U.S. Energy Information Administration located here:https://www.eia.gov/maps/layer_info-m.cfmThere were some facilities that were stumbled upon while searching for others, most of these are smaller scale sites located near farm infrastructure. Other sites were located by contacting counties that had solar developments within the county. Still, others were located by sleuthing around for proposals and company websites that had images of the completed facility. These helped to locate the most recently developed sites and these sites were digitized based on landmarks such as ditches, trees, roads and other permanent structures.Metadata: (2) UC Berkeley Solar PointsUC Berkeley report containing point location for energy facilities across the United States.2022_utility-scale_solar_data_update.xlsm (live.com)Metadata: (3) Kruitwagen et al. 2021Abstract: Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 2009. Energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 2040. Geospatial data describing the energy system are required to manage generation intermittency, mitigate climate change risks, and identify trade-offs with biodiversity, conservation and land protection priorities caused by the land-use and land-cover change necessary for PV deployment. Currently available inventories of solar generating capacity cannot fully address these needs. Here we provide a global inventory of commercial-, industrial- and utility-scale PV installations (that is, PV generating stations in excess of 10 kilowatts nameplate capacity) by using a longitudinal corpus of remote sensing imagery, machine learning and a large cloud computation infrastructure. We locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423 gigawatts (−75/+77 gigawatts) at the end of 2018. Enrichment of our dataset with estimates of facility installation date, historic land-cover classification and proximity to vulnerable areas allows us to show that most of the PV solar energy facilities are sited on cropland, followed by arid lands and grassland. Our inventory could aid PV delivery aligned with the Sustainable Development GoalsEnergy Resource Land Use Planning - Kruitwagen_etal_Nature.pdf - All Documents (sharepoint.com)Metadata: (4) BLM Renewable ProjectTo identify renewable energy approved and pending lease areas on BLM administered lands. To provide information about solar and wind energy applications and completed projects within the State of California for analysis and display internally and externally. This feature class denotes "verified" renewable energy projects at the California State BLM Office, displayed in GIS. The term "Verified" refers to the GIS data being constructed at the California State Office, using the actual application/maps with legal descriptions obtained from the renewable energy company. https://www.blm.gov/wo/st/en/prog/energy/renewable_energy

The Blue Map responds to the direction of the Independent Redistricting Commission to create draft maps that show variations on two student-focused districts, and the direction to show a map that has a north to south orientation for a single West Berkeley District. This map also meets the six universal map criteria to varying degrees. The universal criteria are: 1) Maximum of 10% population deviation; 2) Contiguous districts; 3) Maintain Communities of Interest and Neighborhoods; 4) Use major arterial streets as boundaries where possible; 5) Correct the features of the 2010 map that accounted for prior Councilmember residences; and 6) Include at least one compact student district in every map.The Blue Map follows the Commission direction by making the following noteworthy modifications:Create two “student-focused” districts with an east-west orientation (4,7);Create a single West Berkeley District west of San Pablo Avenue and including the neighborhood surrounding San Pablo Park (2);Unify the Westbrae Neighborhood in District 1;Move the Poet’s Corner Neighborhood to District 1;Unify the Lorin Neighborhood in District 3;Unify the Halcyon Neighborhood in District 8;Unify the Bateman Neighborhood in District 8;Unify LeConte Neighborhood in District 8;Move the District 5 and District 6 border from Spruce Street to Arlington Avenue;Move the Panoramic Hill Neighborhood and the Clark Kerr Campus into the eastern student district (District 7);Move the portion of the Northside Neighborhood south of LeConte Avenue into the western student district (District 4);Move a portion of Central Berkeley and Downtown Neighborhoods into District 3;The above changes necessarily create a lower degree of neighborhood cohesion for the following neighborhoods: North Berkeley, Central Berkeley, Downtown, Southside, North Shattuck;Correct map features for prior Councilmember residences in District 4 and District 7;Use of the major arterials, San Pablo Avenue, Sacramento Street, Spruce Street, Arlington Avenue, Adeline Avenue, and Telegraph Avenue, as council district boundaries;Commission direction on topography/wildfire risk/transit access is reflected in higher elevation neighborhoods contained in three council districts (6, 7, 8).

The following surveys were carried out:
• GIS mapping of bus routes, carried out by survey staff travelling bus routes with GPS enabled phones, using GPS Essantials

The objective of these surveys was to develop a comprehensive picture of the urban transport sector, including, on the supply side, the financing and provision of vehicles and the incomes in the sector, and on the demand side, the needs of passengers and identification of the level of the number of passengers on different routes.
The first phase of data gathering was in May 2019. This was entirely within Ramadan, which naturally throws off ordinary travel patterns. During this phase, only data on route geography was gathered, not on frequencies or capacities. Passenger responses may also have been skewed compared to a typical month. A second set of data was gathered in October (a more typical period), and it is on this phase of data gathering that the frequencies and capacities of routes are based on.
GIS data on routes was processed and analysed using ArcMap. Data was gathered as location ‘pings’, which were processed into lines. These were in turn compiled into routes, based on matching the lines to the street network on Open Street Map.
Some judgment and debate among the survey staff was used to resolve what constitutes a ‘route’. Some routes are circular, while others make a loop at the end. Some return trips are also carried out in a single beat by the vehicle, with no halt at the end. Passengers may or may not be expected to disembark, and/or to pay for another trip, depending on where they boarded. Different routes also have some variability in the streets they take, and others fully overlap longer routes.
There was an attempt to capture all routes in the city, but given the level of manpower and time frame, this may have only been partially achieved. The mutability of routes and their proliferation mean that, while we believe we captured all major destinations, there may well be precise routes that are not captured. This is particularly the case in Balbala, where routes are served by rickshaws and 8-seat Bajaj vehicles, which have a great deal of flexibility and can be difficult to separate from taxi routes.

The Maroon Map responds to the direction of the Independent Redistricting Commission to create draft maps that show variations on two student-focused districts. This map shows West Berkeley in its current configuration of two districts. This map also meets the six universal map criteria to varying degrees. The universal criteria are: 1) Maximum of 10% population deviation; 2) Contiguous districts; 3) Maintain Communities of Interest and Neighborhoods; 4) Use major arterial streets as boundaries where possible; 5) Correct the features of the 2010 map that accounted for prior Councilmember residences; and 6) Include at least one compact student district in every map.The Maroon Map follows the Commission direction by making the following noteworthy modifications:Create two “student-focused” districts with a north-south orientation (4, 7);Use the current configuration for two West Berkeley districts;Unify the Westbrae Neighborhood in District 1;Unify the Poet’s Corner Neighborhood in District 2;Unify the Lorin Neighborhood in District 3;Unify the Halcyon Neighborhood in District 8;Unify the Bateman Neighborhood in District 8;Unify the Willard Neighborhood in District 8;Move the border between District 5 and District 6 from Spruce Street to Arlington Avenue;Move the Panoramic Hill Neighborhood and the Clark Kerr Campus from District 8 to the south student district (District 7);Move the portion of the Northside Neighborhood south of LeConte Avenue into the north student district (District 4);The above changes necessarily create a lower degree of Neighborhood cohesion for the following neighborhoods: LeConte, Northside, North Shattuck;As compared to the Blue Map, this configuration of the student-focused districts results in a comparatively lower density of student residents in District 4 with the inclusion of the Central Berkeley Neighborhood;Correct map features for prior Councilmember residences in District 4 and District 7;Use of the major arterials, University Avenue, Sacramento Street, Spruce Street, Arlington Avenue, Adeline Avenue, Dwight Way, and Bancroft Way, as district boundaries;Commission direction on topography/wildfire risk/transit access is reflected in higher elevation neighborhoods contained in four council districts (4, 6, 7, 8).

A map used in the Catch Basin Viewer app to view catch basins and their adoption status.

The Amber Map responds to the direction of the Independent Redistricting Commission to create a draft map that has a high level of continuity with the existing council district boundaries and includes changes only as needed to meet the six universal map criteria. The universal criteria are: Maximum of 10% population deviation; Contiguous districts; Maintain Communities of Interest and Neighborhoods; Use major arterial streets as boundaries where possible; Correct the features of the 2010 map that accounted for prior Councilmember residences; and Include at least one compact student district in every map.Version two of the Amber Map also responds to the Commission direction to adjust the border between District 3 and District 8 near Ashby BART.The Amber Map follows the Commission direction by making the following noteworthy modifications:Move the border between District 3 and District 8 east from Adeline Street to Shattuck Avenue to include the Ed Roberts Campus, the Ashby BART east lot, and St. Paul AME Church in District 3.Unify the Westbrae Neighborhood in District 1;Unify the Poets Corner Neighborhood in District 2;Unify the LeConte Neighborhood in District 3;Unify the Lorin Neighborhood in District 3;Unify the Halcyon Neighborhood in District 8;Unify the Bateman Neighborhood in District 8;Unify the Willard Neighborhood in District 8;Unify Lower Spruce/Arch Street with the Northside Neighborhood in District 6;Move the census block that contains the International House from District 8 to the existing student district (District 7);Correct map features for prior Councilmember residences in District 4 and District 7;Maximize the use of the major arterials, University Avenue, Telegraph Avenue, Sacramento Street, Spruce Street, Oxford Street, and Cedar Street, as council district boundaries;Commission direction on topography/wildfire risk/transit access is reflected in higher elevation neighborhoods contained in two council districts (6, 8).

This layer was created for the redistricting project map. BUSD provided a powerpoint file that showed the boundaries since they could not locate the original shapefile that was used. The core information used the generate the boundaries are the image in the powerpoint file and the 2020 census block boundaries. The source of image used is described below by the original contractor Bruce Wicinas. I was drafted to help BUSD around 1991. At that time they used planning software authored by a San Jose company, "Educational Data Systems." This was long before ESRI was known to the likes of school districts or acknowledged by the Census Bureau. "Educational Data Systems," which had many school district clients around the U.S., performed their own particle-ization of school district geography. They divided districts into rectangles of approximately 4 - 8 city blocks. These they called "planning areas." They were convenient. BUSD they divided into 445, a number neither too fine nor too coarse.Many years later, .shp files became widely available. Alas, not all Planning Area perimeters coincide with line segments of .shp files. In the Berkeley flatlands the discrepancies are not so bad. But in the hills, there aren't "blocks" but meandering strips. "Planning Areas" have line segments which don't correspond to streets or perimeters of .shp files.About 15 years ago I enhanced my custom software to read shp files. Thus I could superimpose Planning Areas and .shp files, observing the overlap discrepancies. I'll omit for now the rest of this story; what I did about the discrepancy between census Block Groups and Planning Areas. I could go into that if you are interested.I got "Planning Areas" into my custom software from the ancient EdSys data, somehow ,decades ago. I may have read a file containing polygon coordinates. At that time I could export the planning area polygons via DXF. But they have no relationship to .shp. I could provide a representation of GIS planning areas in coordinates such as "State Plane" but this probably does you no good. I have never written an ".shp" file exporter. The .shp file format is mind-boggling; archaic compared to modern methods.About 25 years ago I wrote an on-line means by which staff at BUSD can type in a Berkeley address and get the corresponding socio-ec category number. It does this by determining the "planning area number" - 1 through 445 - containing the address. That on-line software could provide the attendance zone as well but no one ever asked for that. The student assignment software used by the high school and by admissions performs that function internally. Every student has an attendance zone number as soon as they get added to the database.

Static Map of Traffic Circles within the City of Berkeley in PDF form.

City of Berkeley council redistricting plan submission

The Violet Map responds to the direction of the Independent Redistricting Commission to create a draft map that uses the Amber Map as the base map and moves the portion of the Northside Neighborhood south of Le Conte Avenue into the student-focused district, modifies the boundary between District 3 and District 8 to prevent division of the community near Ashby BART, adjusts District 4 in consideration of students and renters, and further creates two student/renter-focused districts in a side-by-side orientation. The Violet Map adheres to the universal criteria of: Maximum of 10% population deviation; Contiguous districts; Maintain Communities of Interest and Neighborhoods; Use major arterial streets as boundaries where possible; Correct the features of the 2010 map that accounted for prior Councilmember residences; and Include at least one compact student district in every map.The Violet Map follows the Commission direction by making the following noteworthy modifications:Unify the Westbrae Neighborhood in District 1;Unify the Poets Corner Neighborhood in District 2;Unify the LeConte Neighborhood in District 3;Unify the Lorin Neighborhood in District 3;Unify the Bateman Neighborhood in District 8;Unify the Willard Neighborhood in District 8;Unify Lower Spruce/Arch Street with the Northside Neighborhood in District 6;Move the border between District 3 and District 8 east from Adeline Street to Shattuck Avenue to include the Ed Roberts Campus, the Ashby BART east parking lot, and St. Paul AME Church into District 3.Move the District 5 and District 6 border from Spruce Street to Arlington Avenue north of the Marin Circle;Move the portion of Northside Neighborhood south of Ridge Road into District 7; Move the census blocks that contain the International House, Lawrence Berkeley Lab, and Foothill Dormitory to the existing student district (District 7);Correct map features for prior Councilmember residences in Dist. 4 and Dist. 7;Move the border between District 5 and District 4 south to Hearst Avenue;Move the border between District 4 and District 7 east to Dana Street;Move the border between District 4 and District 3 south to Parker Street;Maximize the use of the major arterials, University Avenue, Telegraph Avenue, Sacramento Street, Spruce Street, Oxford Street, Hearst Avenue, Arlington Avenue, and Cedar Street, as council district boundaries;Commission direction on topography/wildfire risk/transit access is reflected in higher elevation neighborhoods contained in three council districts (6, 7, 8).

An ArcGIS Media Map app used by the general public to view Storm Drains and their adoption status.