This dataset is a compilation of address point data for the City of Tempe. The dataset contains a point location, the official address (as defined by The Building Safety Division of Community Development) for all occupiable units and any other official addresses in the City. There are several additional attributes that may be populated for an address, but they may not be populated for every address. Contact: Lynn Flaaen-Hanna, Development Services Specialist Contact E-mail Link: Map that Lets You Explore and Export Address Data Data Source: The initial dataset was created by combining several datasets and then reviewing the information to remove duplicates and identify errors. This published dataset is the system of record for Tempe addresses going forward, with the address information being created and maintained by The Building Safety Division of Community Development.Data Source Type: ESRI ArcGIS Enterprise GeodatabasePreparation Method: N/APublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

The FDOT GIS Roads with Median Types feature class provides spatial information on Florida Median Types distinguishing between lawn, paved, painted, and curbed medians. It also notes where a fence, guardrail, or barrier wall divides the two sides of a divided road. A median is defined as a barrier or other physical separation between two lanes of traffic traveling in opposite directions, which can either be raised, painted, or paved. This information is required for all functionally classified roadways On or Off the SHS. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 11/08/2025.For more details please review the FDOT RCI Handbook Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/median_type.zip

This feature contains vector lines representing the shoreline and coastal habitats of California. Line segments are classified according to the Environmental Sensitivity Index (ESI) classification system and are a compilation of the ESI data from the most recent ESI atlas publications. The ESI data includes information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. This California dataset contains only the ESI shoreline data layer and is a merged set of individual ESI data sets to cover the entire California coast. For many parts of the California shoreline, the NOAA-ESI database lists several shoreline types present at a given location, described from landward to seaward. A simplified singular classification [Map_Class] was created to generalize the most dominant features of the multiple shore type attributes present in the raw data. More information can be found at the source citation at ESI Guidelines | response.restoration.noaa.gov Attributes: Line: Type of geographic feature (H: Hydrography, P: Pier, S: Shoreline) Most_sensitive: If multiple shoreline types appear in ESI classification, this field represents the highest value (most sensitive type); otherwise it is the same value as the ESI field. Shore_code: The ESI shoreline type. In many cases shorelines are ranked with multiple codes, such as "6B/3A" (listed landward to seaward). Source: Original year of ESI data. Esi_description: Concatenation of shore type descriptions (listed landward to seaward) Shoretype_1: Numeric classification for the first (most landward) ESI type. Shoretype_1_name: Physical description for the first ESI type. Shoretype_2: Numeric classification for the second ESI type. Shoretype_2_name: Physical description for the second ESI type Shoretype_3: Numeric classification for the third (most seaward) ESI type. Shoretype_3_name: Physical description for the third ESI type. Map_class: Generalized ESI shoreline type for simplified sym

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

This data set consists of an ArcView shapefile set that contains locations of sampled coral reef fish species at the National Marine Sanctuary along the Florida Keys. The dataset contains information about 5 classes of coral reefs, 216 fish species, and 6 benthic habitat.

This data set is an intersection of all 63 vector polygon ranges depicted in the following publication with a statewide grid of 25 square mile hexagon cells. Shuford, W.D., and Gardali, T., editors. 2008. California Bird Species of Special Concern: A ranked assessment of species, subspecies, and distinct populations of birds of immediate conservation concern in California. Studies of Western Birds 1. Western Field Ornithologists, Camarillo, California, and California Department of Fish and Game. Sacramento. http://www.dfg.ca.gov/wildlife/species/ssc/birds.html The vector polygon ranges were hand drawn at a scale of 1:6,600,000 by authors and editors of the Bird Species of Special Concern report and digitized into shapefiles by staff of the Biogeographic Data Branch, California Department of Fish and Game. The hexagon grid for the state was created by Steve Goldman by modifying an AML (Arc Macro Language) script originally written by Eric Kauffman.

GIS dataset includes surveyed shoreline positions for most of the larger beach systems along the southern to mid-coast Maine coastline in York, Cumberland, and Sagadahoc counties. Data were collected using a Leica GS-15 network Real Time Kinematic Global Positioning System (RTK-GPS), and in areas with poor cellular coverage, an Ashtech Z-Xtreme RTK-GPS. Both systems typically have horizontal and vertical accuracies of less than 5 cm. In general, surveys are attempted to be repeated at approximately the same month in each consecutive survey year, however this is not always possible. As a result, the number of available shoreline positions may vary by beach.The line feature class includes the following attributes:BEACH_NAME: The name of the beach where a shoreline was surveyed.SURVEY_DATE: The date (year, month, day; for example 20160901 would be September 1, 2016) upon which a shoreline was surveyed.SURVEY_YEAR: The year (e.g., 2016) within which a shoreline was surveyed.SHAPE_LENGTH: The length, in meters, of the surveyed shoreline.

[Metadata] This dataset contains those marine bottom type/seabed classifications within the vicinity of the main Hawaiian Islands and recorded on the nautical charts.

The shared archived combined in Supplementary Datasets represent the actual databases used in the investigation considered in two papers:

Meteorological conditions affecting black vulture (Coragyps atratus) soaring behavior in the southeast of Brazil: Implications for bird strike abatement (in submission)

Remote sensing applications for abating the aircraft-bird strike risks in the southeast of Brazil (Human-Wildlife Interactions Journal, in print)

The papers were based on my Master’s thesis defended in 2016 in the Institute of Biology of the University of Campinas (UNICAMP) in partial fulfilment of the requirements for the degree of Master in Ecology. Our investigation was devoted to reducing the risk of aircraft collision with Black vultures. It had two parts considered in these two papers. In the first one we studied the relationship between soaring activity of Black vultures and meteorological characteristics. In the second one we explored the dependence of soaring activity of vultures on superficial and anthropogenic characteristics. The study was implemented within surroundings of two airports in the southeast of Brazil taken as case studies. We developed the methodological approaches combining application of GIS and remote sensing technologies for data processing, which were used as the main research instrument. By dint of them we joined in the georeferenced databases (shapefiles) the data of bird's observation and three types of environmental factors: (i) meteorological characteristics collected together with the bird’s observation, (ii) superficial parameters (relief and surface temperature) obtained from the products of ASTER imagery; (iii) parameters of surface covering and anthropogenic pressure obtained from the satellite images of high resolution. Based on the analyses of the georeferenced databases, the relationship between soaring activity of vultures and environmental factors was studied; the behavioral patterns of vultures in soaring flight were revealed; the landscape types highly attractive for this species and forming the increased concentration of birds over them were detected; the maps giving a numerical estimation of hazard of bird strike events over the airport vicinities were constructed; the practical recommendations devoted to decrease the risk of collisions with vultures and other bird species were formulated.

This archive contains all materials elaborated and used for the study, including the GIS database for two papers, remote sensing data, and Microsoft Excel datasets. You can find the description of supplementary files in the Description of Supplementary Dataset.docx. The links on supplementary files and their attribution to the text of papers are considered in the Attribution to the text of papers.docx. The supplementary files are in the folders Datasets, GIS_others, GIS_Raster, GIS_Shape.

For any question please write me on this email: natalieenov@gmail.com

Natalia Novoselova

Table from the American Community Survey (ACS) 5-year series on household types and population related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B11003 Family Type by Presence and Age of Own Children under 18 Years, B11005 Households by Presence of People Under 18 Years by Household Type, B11007 Households by Presence of People 65 Years and Over by Household Type, B11001 Household Type (Including Living Alone), B11002 Household Type by Relatives and Nonrelatives for Population in Households, B25003 Tenure, B25008 Total Population in Occupied Housing Units by Tenure, B09019 Household Type (Including Living Alone) by Relationship. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

This line layer contains centerlines of all paved and unpaved roads, ramps, and bridges in the City of Cambridge. Each centerline segment contains attributes including street name, street type, address ranges, and one-way designations.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription ID type: Stringwidth: 40precision: 0 Unique identifier for each street segment

ROADWAYS type: Stringwidth: 1precision: 0 Divided street designation

ValueDescription TDivided FNot divided

Street type: Stringwidth: 40precision: 0 Full street name with street type

Street_Name type: Stringwidth: 30precision: 0 Street name only

Street_Type type: Stringwidth: 10precision: 0 Street type

Street_ID type: Doublewidth: 8precision: 38 Street ID number from master address database

Alias type: Stringwidth: 50precision: 0 Alternate street name

L_From type: Integerwidth: 4precision: 10 Left side address range 'from'

L_To type: Integerwidth: 4precision: 10 Left side address range 'to'

R_From type: Integerwidth: 4precision: 10 Right side address range 'from'

R_To type: Integerwidth: 4precision: 10 Right side address range 'to

FromNode type: Doublewidth: 8precision: 38 New 'from node' number

ToNode type: Doublewidth: 8precision: 38 New 'to node' number

Direction type: Stringwidth: 5precision: 0 One-way designation

ValueDescription 0Two-way street segment 1One-way street in same direction as street segment -1One-way street in opposite direction of street segment

Restriction type: Stringwidth: 1precision: 0 Truck restriction designation

Label type: Stringwidth: 50precision: 0 Street name field for labels when mapping

MajorRoad type: SmallIntegerwidth: 2precision: 5 Major road designation

ZIP_Left type: Stringwidth: 8precision: 0 Left side zip code

ZIP_Right type: Stringwidth: 8precision: 0 Right side zip code

EditDate type: Stringwidth: 4precision: 0 Date of last edit

Potential_L_From type: Integerwidth: 4precision: 10 Potential left side address range 'from'

Potential_L_To type: Integerwidth: 4precision: 10 Potential left side address range 'to'

Potential_R_From type: Integerwidth: 4precision: 10 Potential right side address range 'from'

Potential_R_To type: Integerwidth: 4precision: 10 Potential right side address range 'to'

Potentail_Range_Done type: SmallIntegerwidth: 2precision: 5 Potential range researched and populated

created_date type: Datewidth: 8precision: 0

last_edited_date type: Datewidth: 8precision: 0

City of Cambridge, MA, GIS basemap development project encompasses the land area of City of Cambridge with a 200-foot fringe surrounding the area and Charles River shoreline towards Boston. The basemap data was developed at 1" = 40' mapping scale using digital photogrammetric techniques. Planimetric features; both man-made and natural features like vegetation, rivers have been depicted. These features are important to all GIS/mapping applications and publication. A set of data layers such as Buildings, Roads, Rivers, Utility structures, 1 ft interval contours are developed and represented in the geodatabase. The features are labeled and coded in order to represent specific feature class for thematic representation and topology between the features is maintained for an accurate representation at the 1:40 mapping scale for both publication and analysis. The basemap data has been developed using procedures designed to produce data to the National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 40 ' mapping scale. Where applicable, the vertical datum is NAVD1988.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription TYPE type: Stringwidth: 50precision: 0 Type of building (building, outbuilding, ruin)

TOP_GL type: Doublewidth: 8precision: 38 Elevation of highest point above ground level (NAVD88)

ELEV_SL type: Doublewidth: 8precision: 38 Elevation of roofline edge above sea level (NAVD88)

TOP_SL type: Doublewidth: 8precision: 38 Elevation of highest point above sea level (NAVD88)

BASE_ELEV type: Doublewidth: 8precision: 38 Elevation of structure (NAVD88)

BldgID type: Stringwidth: 50precision: 0 Unique building ID in master address block. BLDGID values with a 999- prefix are non addressable buildings - often small garages, storage sheds, outbuildings, etc.

EditDate type: Stringwidth: 4precision: 0 Date of last edit

ELEV_GL type: Doublewidth: 8precision: 38 Elevation of roofline edge above sea level (NAVD88)

created_date type: Datewidth: 8precision: 0

last_edited_date type: Datewidth: 8precision: 0

Environmental Sensitivity Index (ESI) data characterize the marine and coastal environments and wildlife based on sensitivity to spilled oil. Coastal species that are listed as threatened, endangered, or as a species of concern, by either federal or state governments, are a primary focus. A subset of the ESI data, the ESI Threatened and Endangered Species (T&E) databases focus strictly on these...

This dataset contains soil type and soil classification, by area.

This dataset is harvested on a weekly basis from Allegheny County’s GIS data portal. The full metadata record for this dataset can also be found on Allegheny County's GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the "Explore button (and choosing the "Go to resource" option) to the right of the "ArcGIS Open Dataset" text below.

Category: Environment

Department: Geographic Information Systems Group; Department of Administrative Services

Development Notes: This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey.

Related Documents: Data Dictionary for SOIL_CODE, https://www.nrcs.usda.gov/Internet/FSE_MANUSCRIPTS/pennsylvania/PA003/0/legends.pdf (the last page includes the soil legend for this dataset)

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

The City of Cambridge owns and maintains most street lights in the City. These were purchased from NSTAR in 2005. Types of lights include Cobrahead (most typical), and various types of decorative lighting. Park Lights are in a separate layer. Some street lights are owned by other organizations. DCR, the State, and Universities all own lights along the roadways they maintain. These street lights are in a layer 'StreetLightsNonCity'Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription PoleID type: Stringwidth: 15precision: 0 Unique ID for each street light. This number is also marked on each pole.

StreetName type: Stringwidth: 24precision: 0 Name of street the light is on

StreetSuffix type: Stringwidth: 4precision: 0 Street suffix for the street which the light is on.

Intersection type: Stringwidth: 20precision: 0 Street intersection if the light is at one

NumLamps type: Integerwidth: 4precision: 10 Number of lamps on a pole

EditDate type: Stringwidth: 8precision: 0 Date (year) lasted edited

Description type: Stringwidth: 25precision: 0 Type of head on the light.

Owner type: Stringwidth: 25precision: 0 Owner - always City of Cambridge

LEDchk type: Stringwidth: 6precision: 0 Field check by Cambridge GIS

Neighborhood type: Doublewidth: 8precision: 38 CDD Neighborhood each light is in.

Street_ID type: Stringwidth: 254precision: 0 Street ID. Each street has a unique ID. The first part of the PoleID.

Pole_Num type: Stringwidth: 254precision: 0 ID for each pole on a street. Part of the unique PoleID

FixtureType type: Stringwidth: 254precision: 0 Coding for LED project. Corresponds to the number of LED lights in a fixture. A1 = 120 bulbs, A2 = 80 bulbs, A3 = 40 bulbs per lamp

Lamp1 type: Stringwidth: 50precision: 0 Unique ID for a pole with 2 lamps.

Lamp2 type: Stringwidth: 50precision: 0 Unique ID for a pole with 2 lamps.

created_date type: Datewidth: 8precision: 0

last_edited_date type: Datewidth: 8precision: 0

DescriptionThe features in this layer have been created from information extracted from SAP. When an SAP user is mapping a project from the CJ20N transaction, these GIS representations are created.Used by SAP GIS Locator web app to read/write projects GIS data from SAP PRD environment. From 9/19/2016 onward.Last UpdateContinuouslyUpdate FrequencyContinuouslyData OwnerDivision of Transportation DevelopmentData ContactGIS Support UnitCollection MethodProjectionNAD83 / UTM zone 13NCoverage AreaStatewideTemporalDisclaimer/LimitationsThere are no restrictions and legal prerequisites for using the data set. The State of Colorado assumes no liability relating to the completeness, correctness, or fitness for use of this data.