Indoors Demo for Building L in Esri Redlands Campus.

This map presents a tour of the City of Redlands, California using the detailed map of Redlands included in the community basemap. The City of Redlands is located in Southern California, about 65 miles east of Los Angeles. The map tour highlights some of the unique features in the history of Redlands as well as several of the places and events that make it a very livable community today.The map features a detailed basemap for the City of Redlands, California, including buildings, parcels, vegetation, land use, landmarks, streets, and more. The map features special detail for areas of high interest within the City, including local parks, landmarks, and the ESRI campus.The map references detailed GIS data provided by the City of Redlands, Department of Innovation and Technology, GIS Division. The map was authored using map templates available from ESRI, including:Topographic Map Template - Large ScalesCampus Basemap TemplateThe map was published as part of ESRI's Community Maps Program and is one of several detailed maps of cities and counties in the World Topographic Map.

This map presents a tour of the City of Redlands, California using the detailed map of Redlands included in the community basemap. The City of Redlands is located in Southern California, about 65 miles east of Los Angeles. The map tour highlights some of the unique features in the history of Redlands as well as several of the places and events that make it a very livable community today.The map features a detailed basemap for the City of Redlands, California, including buildings, parcels, vegetation, land use, landmarks, streets, and more. The map features special detail for areas of high interest within the City, including local parks, landmarks, and the ESRI campus.The map references detailed GIS data provided by the City of Redlands, Department of Innovation and Technology, GIS Division. The map was authored using map templates available from ESRI, including:Topographic Map Template - Large ScalesCampus Basemap TemplateThe map was published as part of ESRI's Community Maps Program and is one of several detailed maps of cities and counties in the World Topographic Map.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a GIS-usable format employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM) projection, Zone 16, using North American Datum of 1983 (NAD83). To produce a polygon vector layer for use in ArcGIS, we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format using ArcGIS (Version 9.2, © 2006 Environmental Systems Research Institute, Redlands, California). In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map attribute codes (both map class codes and physiognomic modifier codes) to the polygons, and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer of INDU and immediate environs. At this stage, the map layer has only map attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map class names, physiognomic definitions, link to NVC association and alliance codes), we produced a feature class table along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature class layers produced from this project, including vegetation sample plots, accuracy assessment sites, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Catalina Mountains by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

Redlands Sample GoProHero8

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Valles Calders, upper part of the Jemez River basin by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

Esri Campus Web Scene shows the Esri Campus in Redlands with a focus on the Development Headquarter. Detailed interior floorplans are visualized for buildings M, N and MA.Use the swipe view on the Dev HQ layer to swipe away the exterior walls and get a better look insideHide and unhide the three Dev HQ Level layers to see the interior of a specific floorUse the search to locate a specific rooms or find the nearest restrooms

To download:1. Click the Download button above.2. A side panel will appear showing download options. Under Shapefile, click the Download button.3. When the download completes, browse to the location of the downloaded .zip, copy it to the location where you manage your redistricting files, then right-click to extract the contents. You will then be able to use the file in GIS software.If, rather than downloading the data, you wish the reference online versions of these datasets directly to ensure you are always using the most up-to-date data, please contact the County of San Bernardino Innovation and Technology Departments at 909-884-4884 or by emailing OpenData@isd.sbcounty.gov for informations and instructions for doing so.This dataset should only be used for the purpose of establishing election divisions within a district. It will be removed once the redistricting process has concluded.

New Group Layer

Tracklines and associated observations were mapped and analyzed using ArcMap (ESRI, Redlands, CA). GPS data were recorded in NAD27 map datum and projected to an USGS Albers Equal Area Conic map projection for presentation and subsequent density analyses. Concatenated GPS and observation data were then used to generate point and line coverages in ArcMap (ESRI, Redlands, CA). We designed a custom analytic tool using ArcMap Model Builder that allows for the construction and export of user-specified and effort-adjusted spatial binning of species observations along continuous trackines. For the purposes of this report, we calculated seabird density estimates and marine mammal counts along continuous 3.0-kilometer and 7.7-kilometer trackline segments (i.e., 3.0 kilometer and 7.7 kilometer bins). Therefore, marine bird densities (at 3-kilometer scale, for example) are based on a composite strip area ranging from 0.15 per kilometer squared (one observer on effort) to 0.30 per kilometer squared (two observers on effort). We made no effort to adjust densities such that they would be proportional to variations in the area of buffered transect strip bin (i.e., weighted offset variable). These data are associated with the following publication: Mason, J.W., McChesney, G.J., McIver, W.R., Carter, H.R., Takekawa, J.Y., Golightly, R.T., Ackerman, J.T., Orthmeyer, D.L., Perry, W.M., Yee, J.L. and Pierson, M.O. 2007. At-sea distribution and abundance of seabirds off southern California: a 20-Year comparison. Cooper Ornithological Society, Studies in Avian Biology Vol. 33. References- ESRI. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute.

Hydrologically conditioned digital elevation model (DEM) generated from lidar data clipped to the Difficult Run watershed with a 500-m buffer in ArcGIS 10.3.1 (ESRI, Redlands, CA). The DEM was hydrologically corrected by breaching through pits with no downslope neighboring cells to force surface flow to continuously move downslope using Whitebox Geospatial Analysis Tools (Lindsay and Dhun 2015, Lindsay 2016). Pits that were not properly breached were manually adjusted using elevation information from the DEM and aerial imagery to locate culverts under roadways.

BackgroundJapanese encephalitis (JE) is a vector-borne disease with a high prevalence in Yunnan Province, China. However, there has been a lack of a JE epidemic systematic analysis, which is urgently needed to guide control and prevention efforts.MethodsThis study explored and described the spatiotemporal distribution of JE cases observed among two different age groups in Yunnan Province from 2007 to 2017. The epidemiological features and spatial features were analyzed according to basic statistics, ArcGIS software (version 9.3; ESRI, Redlands, CA) and SPSS software (version 20; IBM Corp., Armonk, New York).ResultsOverall, the whole province had a high incidence of JE. The annual incidence rates in 2007 and 2017 were 1.668/100,000 and 0.158/100,000, respectively. The annual mortality was under 0.095/100,000 for these years. Although the whole province was in danger of JE, the Diqing autonomous prefecture and the Lijiang autonomous prefecture had no JE cases recorded for over 10 years. The JE cases were reported by hospitals located in 60 counties of 14 municipalities. The top ten areas with the most JE cases were Kunming City, Zhaotong City, Jinghong City, Wenshan City, Mangshi City, Pu’er City, Baoshan City, Dali City, Chuxiong City, and Gejiu City. The incidence declined smoothly, with a peak occurring from June to September, which accounted for 96.1% of the total cases. Children whose age was equal or less than 10 years old (LEQ10) still maintained a high frequency of JEV infection, and a large number of cases were reported in August, despite the Expanded Program on Immunization (EPI), which was established in April 2008. There was no difference in the quantity of cases between the two groups (t = -0.411, P>0.05); additionally, the number of JE cases among patients LEQ10 were significantly greater than those among patients older than 10 years (GTR10). Further analysis using local indicators of spatial association (LISA) revealed that the distribution of JE exhibited a high-high cluster characteristic (Z = 2.06, P

1) Spatial data: This dataset includes raw shapefiles used for an overlay analysis of digitized imagery, pre- and post-herbicide treatment from a remotely piloted aircraft system to invasive Phragmites australis. Images were digitized in ArcGIS Pro (v. 3.1.0, ESRI Inc., Redlands, CA). Files include the targeted treatment area, pre-treatment (2022) digitized P. australis classification, and post-treatment (2023) digitized vegetation damage. Site codes correspond to the following sites: BDD- Baie du Doré, RPP- Rondeau Provincial Park, SL- Spongy Lake, WD- Wood Drive. Note: there is no post-treatment classification at Wood Drive. 2) Data: This dataset includes outputted area and calculated percentage values from the overlay analysis, maximum drift measurements, and vegetation transect data/metadata.

This geodatabase contains the spatial datasets that represent the Edwards-Trinity aquifer system in the States of Arkansas, Oklahoma, and Texas. Included are: (1) polygon extents; datasets that represent the aquifer system extent, the entire extent subdivided into subareas or subunits, and any polygon extents of special interest (no data available, areas underlying other aquifers, anomalies, for example), (2) raster datasets for the altitude of each aquifer subarea or subunit, (3) altitude, and/or if applicable, thickness contours used to generate the surface rasters, (4) georeferenced images of the figures that were digitized to create the altitude or thickness contours. The images and digitized contours are supplied for reference. The extent of the Edwards-Trinity aquifer system encompasses all subunits. It is delineated from the linework of the Edwards-Trinity aquifer system extent and outcrop maps of the U.S. Geological Survey Hydrologic Atlas 730-E (USGS HA 730-E) , available at http://water.usgs.gov/ogw/NatlAqCode-reflist.html. Included are the "no data available" extent polygons where there were no altitude data available for the bottom surface of the Edwards-Trinity aquifer system. These were digitized from USGS HA-730-E, figure 81, and U.S. Geological Survey Water-Resources Investigations Report 85-4116 (USGS WRIR 85-4116), plate 9, and U.S. Geological Survey Water-Resources Investigations Paper 91-4071 (USGS WRIR 91-4071), plate 1. The Edwards-Trinity aquifer system has three aquifer subunits, but for the purposes of this geodatabase only the ultimate top and bottom surface rasters are published. The altitudes for the top surface raster are from georeferenced images of altitude contours from USGS HA-730-E, figures 84, 98 and 114, and USGS WRIR 85-4116, plate 8. In the areas where the Edwards-Trinity top surface underlies the Pecos River alluvial aquifer (USGS HA 730-E, Pecos River Basin alluvial aquifer), and the High Plains aquifer (see USGS HA 730-E, High Plains aquifer), the altitude of the bottom those two aquifers is the top of the Edwards-Trinity aquifer system. The altitudes of the bottom surface raster are from georeferenced images of altitude contours from USGS HA-730-E figure 81, USGS WRIR 85-4116 plate 9, and USGS WRIR 91-4071 plate 1. The altitude contours were interpolated into surface rasters within a GIS using tools that create hydrologically correct surfaces from contour data, derives the altitude from the thickness (depth from the land surface) if necessary, and merges the subareas into a single surface. The primary tool was "Topo to Raster" used in ArcGIS, ArcMap, Esri 2014. ArcGIS Desktop: Release 10.2 Redlands, CA: Environmental Systems Research Institute.

This line shapefile represents the major rivers within the world at 1:15,000,000 scale. This layer is part of the 2014 ESRI Data and Maps collection for ArcGIS 10.2.World Rivers provides a base map layer of major rivers of the world.

Dataset of net, winter and summer mass balances of Austre Gronfjordbreen, a valley glacier in western part of Nordenskiold land. Following dataset covers the period of 2012/2013 - 2018/2019. The data is planned to be published in a peer-reviewed journal in 2020. The dates of field surveys, the number of stakes and methods of winter measurements are also attached.

Quality

An annual mass balance was calculated by summing the winter balance and summer balance. Using observations from stake data, the glaciological mass balance of the entire glacier was calculated as sum of weighted mean values of separate elevation bands. The interval of elevation difference is 50 m. A mean mass balance was for each individual elevation zone using Topo To Raster interpolation in ArcGIS software (Esri, Redlands, CA, USA) (© ESRI). The mass balance value of each elevation zone is multiplied by the zone area, summed and then divided by the total glacier area to obtain the mass balance of the entire glacier. The total glacier area was estimated for each year by available satellite images.

To assess the current topography of the tidal marshes we conducted survey-grade elevation surveys at all sites between 2009 and 2013 using a Leica RX1200 Real Time Kinematic (RTK)Global Positioning System (GPS) rover (±1 cm horizontal, ±2 cm vertical accuracy; Leica Geosystems Inc., Norcross, GA; Figure 4). At sites with RTK network coverage (San Pablo, Petaluma, Pt. Mugu, and Newport), rover positions were received in real time from the Leica Smartnet system via a CDMA modem (www.lecia-geosystems.com). At sites without network coverage (Humboldt, Bolinas, Morro and Tijuana), rover positions were received in real time from a Leica GS10 antenna base station via radio link. When using the base station, we adjusted all elevation measurements using an OPUS correction (www.ngs.noaa.gov/OPUS). We used the WGS84 ellipsoid model for vertical and horizontal positioning. We verified rover accuracy and precision by measuring positions at local National Geodetic Survey (NGS) benchmarks and temporary benchmarks established at each site (Table 1). Average measured vertical errors at benchmarks were 1-2 cm throughout the study, comparable to the stated error of the GPS. At each site, we surveyed marsh surface elevation along transects oriented perpendicular to the major tidal sediment source, with a survey point taken every 12.5 m; 50 m separated transect lines. We used the Geoid09 model to calculate orthometric heights from ellipsoid values (m, NAVD88; North American Vertical Datum of 1988) and projected all points to NAD83 UTM zone 10 or zone 11 using Leica GeoOffice (Leica Geosystems Inc, Norcross, GA, v. 7.0.1).We synthesized the elevation survey data to create a digital elevation model (DEM) at each site in ArcGIS 10.2.1 Spatial Analyst (ESRI 2013; Redlands, CA) with exponential ordinary kriging methods (5 x 5 mcell size) after adjusting model parameters to minimize the root-mean-square error (RMS). We used elevation models as the baseline conditions for subsequent analyses in this study including tidal inundation patterns, SLR response modeling, and mapping of sites by specific elevation (flooding) zones.

Aerial light detection and ranging (lidar) data were collected over the study site between April 12 – 14, 2012 as part of the Fauquier, Fairfax, Frederick (MD), and Jefferson County acquisition for FEMA Region 3 FY12 VA lidar (Dewberry 2012). Lidar points classified as ground and water were used to create a 3-m digital elevation model (DEM) clipped to the Difficult Run watershed with a 500-m buffer in ArcGIS 10.3.1 (ESRI, Redlands, CA).