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

Welcome to Apiscrapy, your ultimate destination for comprehensive location-based intelligence. As an AI-driven web scraping and automation platform, Apiscrapy excels in converting raw web data into polished, ready-to-use data APIs. With a unique capability to collect Google Address Data, Google Address API, Google Location API, Google Map, and Google Location Data with 100% accuracy, we redefine possibilities in location intelligence.

Key Features:

Unparalleled Data Variety: Apiscrapy offers a diverse range of address-related datasets, including Google Address Data and Google Location Data. Whether you seek B2B address data or detailed insights for various industries, we cover it all.

Integration with Google Address API: Seamlessly integrate our datasets with the powerful Google Address API. This collaboration ensures not just accessibility but a robust combination that amplifies the precision of your location-based insights.

Business Location Precision: Experience a new level of precision in business decision-making with our address data. Apiscrapy delivers accurate and up-to-date business locations, enhancing your strategic planning and expansion efforts.

Tailored B2B Marketing: Customize your B2B marketing strategies with precision using our detailed B2B address data. Target specific geographic areas, refine your approach, and maximize the impact of your marketing efforts.

Use Cases:

Location-Based Services: Companies use Google Address Data to provide location-based services such as navigation, local search, and location-aware advertisements.

Logistics and Transportation: Logistics companies utilize Google Address Data for route optimization, fleet management, and delivery tracking.

E-commerce: Online retailers integrate address autocomplete features powered by Google Address Data to simplify the checkout process and ensure accurate delivery addresses.

Real Estate: Real estate agents and property websites leverage Google Address Data to provide accurate property listings, neighborhood information, and proximity to amenities.

Urban Planning and Development: City planners and developers utilize Google Address Data to analyze population density, traffic patterns, and infrastructure needs for urban planning and development projects.

Market Analysis: Businesses use Google Address Data for market analysis, including identifying target demographics, analyzing competitor locations, and selecting optimal locations for new stores or offices.

Geographic Information Systems (GIS): GIS professionals use Google Address Data as a foundational layer for mapping and spatial analysis in fields such as environmental science, public health, and natural resource management.

Government Services: Government agencies utilize Google Address Data for census enumeration, voter registration, tax assessment, and planning public infrastructure projects.

Tourism and Hospitality: Travel agencies, hotels, and tourism websites incorporate Google Address Data to provide location-based recommendations, itinerary planning, and booking services for travelers.

Discover the difference with Apiscrapy – where accuracy meets diversity in address-related datasets, including Google Address Data, Google Address API, Google Location API, and more. Redefine your approach to location intelligence and make data-driven decisions with confidence. Revolutionize your business strategies today!

Note: As of April 16, 2021, this dataset will update daily with a five-day data lag.

A. SUMMARY This dataset includes COVID-19 tests by resident neighborhood and specimen collection date (the day the test was collected). Specifically, this dataset includes tests of San Francisco residents who listed a San Francisco home address at the time of testing. These resident addresses were then geo-located and mapped to neighborhoods. The resident address associated with each test is hand-entered and susceptible to errors, therefore neighborhood data should be interpreted as an approximation, not a precise nor comprehensive total.

In recent months, about 5% of tests are missing addresses and therefore cannot be included in any neighborhood totals. In earlier months, more tests were missing address data. Because of this high percentage of tests missing resident address data, this neighborhood testing data for March, April, and May should be interpreted with caution (see below)

Percentage of tests missing address information, by month in 2020 Mar - 33.6% Apr - 25.9% May - 11.1% Jun - 7.2% Jul - 5.8% Aug - 5.4% Sep - 5.1% Oct (Oct 1-12) - 5.1%

To protect the privacy of residents, the City does not disclose the number of tests in neighborhoods with resident populations of fewer than 1,000 people. These neighborhoods are omitted from the data (they include Golden Gate Park, John McLaren Park, and Lands End).

Tests for residents that listed a Skilled Nursing Facility as their home address are not included in this neighborhood-level testing data. Skilled Nursing Facilities have required and repeated testing of residents, which would change neighborhood trends and not reflect the broader neighborhood's testing data.

This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected).

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times (which is common). To see the number of new confirmed cases by neighborhood, reference this map: https://data.sfgov.org/stories/s/Map-of-Cumulative-Cases/adm5-wq8i#new-cases-map

B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information. All testing data is then geo-coded by resident address. Then data is aggregated by "https://data.sfgov.org/Geographic-Locations-and-Boundaries/Analysis-Neighborhoods/p5b7-5n3h ">analysis neighborhood and specimen collection date.

Data are prepared by close of business Monday through Saturday for public display.

C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure.

D. HOW TO USE THIS DATASET Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a data user can analyze this data by "specimen_collection_date".

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. Percent positivity indicates how widesprea

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

Important Note: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.

The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations.Manager Type provides a coarse level land manager description from the PAD-US "Agency Type" Domain, "Manager Type" Field (for example, Federal, State, Local Government, Private).PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.Dataset SummaryPhenomenon Mapped: This layer displays protected areas symbolized by manager type.Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean IslandsVisible Scale: 1:1,000,000 and largerSource: U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP) PAD-US version 3.0Publication Date: July 2022Attributes included in this layer are: CategoryOwner TypeOwner NameLocal OwnerManager TypeManager NameLocal ManagerDesignation TypeLocal DesignationUnit NameLocal NameSourcePublic AccessGAP Status - Status 1, 2, 3 or 4GAP Status DescriptionInternational Union for Conservation of Nature (IUCN) Description - I: Strict Nature Reserve, II: National Park, III: Natural Monument or Feature, IV: Habitat/Species Management Area, V: Protected Landscape/Seascape, VI: Protected area with sustainable use of natural resources, Other conservation area, UnassignedDate of EstablishmentThe source data for this layer are available here. What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for Gap Status Code = 3 to create a map of only the GAP Status 3 areas.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Note that many features in the PAD-US database overlap. For example wilderness area designations overlap US Forest Service and other federal lands. Any analysis should take this into consideration. An imagery layer created from the same data set can be used for geoprocessing analysis with larger extents and eliminates some of the complications arising from overlapping polygons.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

These maps also referred to as “type c maps” represent the parts of territories likely to contain buildings exceeding the limit values referred to in Article L571-6 of the Environmental Code and laid down by Article 7 of the Order of 4 April 2006.

Layer produced by GIS workshop (June 2018) by compiling type C noise zones related to road infrastructure (CEEREMA report on strategic noise maps) du Jura — Unconceded January 2018), Ferrovières (CEEREMA report on Strategic Noise Maps — September 2017 rail network) and motorway (June 2018 data)

For type C maps (CBSTYPE), each noise zone shall be bounded by the lower limit isophone curve (Lden > 68 or 73, Ln > 62 or 65). For roads, the limit values correspond to a Lden of 68 dB(A) and a Ln of 62 dB(A). For conventional railways, the limit values correspond to a Lden of 73 dB(A) and a Ln of 65 dB(A). The noise level on a noise map is represented on the basis of regulatory indicators: the “Ln” (Level night) (indicator used for this geographical layer) and the “Lden” (Level day-evening-night) which are harmonised indicators at European level. The Ln is the average sound level for the night period (22h-6h). The Lden is the 24-hour weighted average sound level to account for the perceived discomfort with the same noise level, which is greater in the evening and at night compared to the day.

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The assignment of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity.For PPRTs the hazard levels are determined by effect effect on maps by type of effect and overall on an aggregated level on a synthesis map.All hazard areas represented on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered to be subject to hazard (cases of breakage or inadequacy of the structure).The hazard zones may be classified as data compiled in so far as they result from a synthesis using several sources of calculated, modelled or observed hazard data. These source data are not covered by this class of objects but by another standard dealing with the knowledge of hazards.Some areas of the study perimeter are considered “zero or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones.

These data include the individual responses for the City of Tempe Annual Business Survey conducted by ETC Institute. These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Business Survey results are used as indicators for city performance measures. The performance measures with indicators from the Business Survey include the following (as of 2023):1. Financial Stability and Vitality5.01 Quality of Business ServicesThe location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city.Additional InformationSource: Business SurveyContact (author): Adam SamuelsContact E-Mail (author): Adam_Samuels@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData DictionaryMethods:The survey is mailed to a random sample of businesses in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used.To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city.Processing and Limitations:The location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city.The data are used by the ETC Institute in the final published PDF report.

This is the study area associated with the project: “Status and Trends of Deciduous Communities in the Bighorn Mountains”. The aim of the study is to assess the current trends of deciduous communities in the Bighorn National Forest in north-central Wyoming. The data here represents phase I of the project, completed in FY2017. The USGS created a synthesis map of coniferous and deciduous communities in the Bighorn Mountains of Wyoming using a species distribution modeling approach developed in the Wyoming Landscape Conservation Initiative (WLCI) (Assal et al. 2015). The modeling framework utilized a number of topographic covariates and temporal remote sensing data from the early, mid and late growing season to capitalize on phenological differences in vegetation types. We used the program RandomForest in the R statistical program to generate probability of occurrence models for deciduous and coniferous vegetation. The binary maps were combined into a synthesis map using the procedure from Assal et al. 2015. In Phase II of this project (to be completed in FY2018 and 2019), the USGS will conduct a preliminary assessment on the baseline condition of riparian deciduous communities. This will be a proof-of-concept study where the USGS will apply a framework used in prior research in upland aspen and sagebrush communities to detect trends in riparian vegetation condition from the mid-1980s to present. Literature Cited Assal et al. 2015: https://doi.org/10.1080/2150704X.2015.1072289

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The assignment of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity.For PPRTs the hazard levels are determined by effect effect on maps by type of effect and overall on an aggregated level on a synthesis map.All hazard areas represented on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered to be subject to hazard (cases of breakage or inadequacy of the structure).The hazard zones may be classified as data compiled in so far as they result from a synthesis using several sources of calculated, modelled or observed hazard data. These source data are not covered by this class of objects but by another standard dealing with the knowledge of hazards.Some areas of the study perimeter are considered “zero or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones.

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:

• Metadata is missing or incomplete for some layers at this time and will be continuously improved.
  
• We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.

Purpose

County and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, coastal buffers are removed, leaving the land-based portions of jurisdictions. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers
   • Without Coastal Buffers (this dataset)
4. Place Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places (Coming Soon)
   
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

Field and Abbreviation Definitions

• 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
• Place Name: CDTFA incorporated (city) or county name
• County: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information System
• Place Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area names
• CNTY Abbr: CalTrans Division of Local Assistance abbreviations of county names
• Area_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.

Accuracy

CDTFA"s source data notes the following about accuracy:

City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization 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 rates. The boundaries are continuously being revised to align with aerial imagery 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 for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI =

The Address Points dataset shows Utah address points for all twenty-nine Utah counties. An address point represents a geographic location that has been assigned a US Postal Service (USPS) address by the local address authority (i.e., county or municipality) but does not necessarily receive mail. Address points may include several pieces of information about the structure or location that’s being mapped, such as:the full address (i.e., the USPS mailing address, if the address is for a physical location [rather than a PO box]);the landmark name; whether the location is a building;the type of unit;the city and ZIP code; unique code identifiers of the specific geographic location, including the Federal Information Processing Standard Publication (FIPS) county code and the US National Grid (USNG) spatial address;the address source; andthe date that the address point was loaded into the map layer.This dataset is mapping grade; it is a framework layer that receives regular updates. As with all our datasets, the Utah Geospatial Resource Center (UGRC) works to ensure the quality and accuracy of our data to the best of our abilities. Maintaining the dataset is now an ongoing effort between UGRC, counties, and municipalities. Specifically, UGRC works with each county or municipality’s Master Address List (MAL) authority to continually improve the address point data. Counties have been placed on an update schedule depending on the rate of new development and change within them. Populous counties, such as Weber, Davis, Salt Lake, Utah, and Washington, are more complete and are updated monthly, while rural or less populous counties may be updated quarterly or every six months.The information in the Address Points dataset was originally compiled by Utah counties and municipalities and was aggregated by UGRC for the MAL grant initiative in 2012. The purpose of this initiative was to make sure that all state entities were using the same verified, accurate county and municipal address information. Since 2012, more data has been added to the Address Points GIS data and is used for geocoding, 911 response, and analysis and planning purposes. The Address Point data is also used as reference data for the api.mapserv.utah.gov geocoding endpoint, and you can find the address points in many web mapping applications. This dataset is updated monthly and can also be found at: https://gis.utah.gov/data/location/address-data/.

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

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)

Area exposed to one or more hazards shown on the hazard map used for the RPP risk analysis. The hazard map is the result of the study of hazards whose objective is to assess the intensity of each hazard at any point in the study area. The assessment method is specific to each type of hazard. It leads to the delimitation of a set of zones on the study perimeter constituting a graduated zoning according to the level of the hazard. The assignment of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For PPRTs hazard levels are determined effect by effect on maps by type of effects and overall according to an aggregate level on a synthesis map.

All hazard areas shown on the hazard map are included. Areas protected by protective works must be represented (possibly in a specific way) because they are always considered subject to hazard (case of rupture or insufficiency of the structure). Hazard zones can be described as elaborated data to the extent that they result from a synthesis using several calculated, modelled or observed hazard data sources. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas of the study perimeter are considered “zero or insignificant hazard areas”. These are the areas where the hazard has been studied and is zero. These areas are not included in the object class and do not have to be represented as hazard zones.

This dataset contains files created, digitized, or georeferenced by Chris DeRolph for mapping the pre-urban renewal community within the boundaries of the Riverfront-Willow St. and Mountain View urban renewal projects in Knoxville TN. Detailed occupant information for properties within boundaries of these two urban renewal projects was extracted from the 1953 Knoxville City Directory. The year 1953 was chosen as a representative snapshot of the Black community before urban renewal projects were implemented. The first urban renewal project to be approved was the Riverfront-Willow Street project, which was approved in 1954 according to the University of Richmond Renewing Inequality project titled ‘Family Displacements through Urban Renewal, 1950-1966’ (link below in the 'Other shapefiles' section). For ArcGIS Online users, the shapefile and tiff layers are available in AGOL and can be found by clicking the ellipsis next to the layer name and selecting 'Show item details' for the layers in this webmap https://knoxatlas.maps.arcgis.com/apps/webappviewer/index.html?id=43a66c3cfcde4f5f8e7ab13af9bbcebecityDirectory1953 is a folder that contains:JPG images of 1953 City Directory for street segments within the urban renewal project boundaries; images collected at the McClung Historical CollectionTXT files of extracted text from each image that was used to join occupant information from directory to GIS address datashp is a folder that contains the following shapefiles:Residential:Black_owned_residential_1953.shp: residential entries in the 1953 City Directory identified as Black and property ownersBlack_rented_residential_1953.shp: residential entries in the 1953 City Directory identified as Black and non-owners of the propertyNon_Black_owned_residential_1953.shp: residential entries in the 1953 City Directory identified as property owners that were not listed as BlackNon_Black_rented_residential_1953.shp: residential entries in the 1953 City Directory not listed as Black or property ownersResidential shapefile attributes:cityDrctryString: full text string from 1953 City Directory entryfileName: name of TXT file that contains the information for the street segmentsOccupant: the name of the occupant listed in the City Directory, enclosed in square brackets []Number: the address number listed in the 1953 City DirectoryBlackOccpt: flag for whether the occupant was identified in the City Directory as Black, designated by the (c) or (e) character string in the cityDrctryString fieldOwnerOccpd: flag for whether the occupant was identified in the City Directory as the property owner, designated by the @ character in the cityDrctryString fieldUnit: unit if listed (e.g. Apt 1, 2d fl, b'ment, etc)streetName: street name in ~1953Lat: latitude coordinate in decimal degrees for the property locationLon: longitude coordinate in decimal degrees for the property locationrace_own: combines the BlackOccpt and OwnerOccpd fieldsmapLabel: combines the Number and Occupant fields for map labeling purposeslastName: occupant's last namelabelShort: combines the Number and lastName fields for map labeling purposesNon-residential:Black_nonResidential_1953.shp: non-residential entries in the 1953 City Directory listed as Black-occupiedNonBlack_nonResidential_1953.shp: non-residential entries in the 1953 City Directory not listed as Black-occupiedNon-residential shapefile attributes:cityDrctryString: full text string from 1953 City Directory entryfileName: name of TXT file that contains the information for the street segmentsOccupant: the name of the occupant listed in the City Directory, enclosed in square brackets []Number: the address number listed in the 1953 City DirectoryBlackOccpt: flag for whether the occupant was identified in the City Directory as Black, designated by the (c) or (e) character string in the cityDrctryString fieldOwnerOccpd: flag for whether the occupant was identified in the City Directory as the property owner, designated by the @ character in the cityDrctryString fieldUnit: unit if listed (e.g. Apt 1, 2d fl, b'ment, etc)streetName: street name in ~1953Lat: latitude coordinate in decimal degrees for the property locationLon: longitude coordinate in decimal degrees for the property locationNAICS6: 2022 North American Industry Classification System (NAICS) six-digit business code, designated by Chris DeRolph rapidly and without careful considerationNAICS6title: NAICS6 title/short descriptionNAICS3: 2022 North American Industry Classification System (NAICS) three-digit business code, designated by Chris DeRolph rapidly and without careful considerationNAICS3title: NAICS3 title/short descriptionflag: flags whether the occupant is part of the public sector or an NGO; a flag of '0' indicates the occupant is assumed to be a privately-owned businessrace_own: combines the BlackOccpt and OwnerOccpd fieldsmapLabel: combines the Number and Occupant fields for map labeling purposesOther shapefiles:razedArea_1972.shp: approximate area that appears to have been razed during urban renewal based on visual overlay of usgsImage_grayscale_1956.tif and usgsImage_colorinfrared_1972.tif; digitized by Chris DeRolphroadNetwork_preUrbanRenewal.shp: road network present in urban renewal area before razing occurred; removed attribute indicates whether road was removed or remains today; historically removed roads were digitized by Chris DeRolph; remaining roads sourced from TDOT GIS roads dataTheBottom.shp: the approximate extent of the razed neighborhood known as The Bottom; digitized by Chris DeRolphUrbanRenewalProjects.shp: boundaries of the East Knoxville urban renewal projects, as mapped by the University of Richmond's Digital Scholarship Lab https://dsl.richmond.edu/panorama/renewal/#view=0/0/1&viz=cartogram&city=knoxvilleTN&loc=15/35.9700/-83.9080tiff is a folder that contains the following images:streetMap_1952.tif: relevant section of 1952 map 'Knoxville Tennessee and Surrounding Area'; copyright by J.U.G. Rich and East Tenn Auto Club; drawn by R.G. Austin; full map accessed at McClung Historical Collection, 601 S Gay St, Knoxville, TN 37902; used as reference for street names in roadNetwork_preUrbanRenewal.shp; georeferenced by Chris DeRolphnewsSentinelRdMap_1958.tif: urban renewal area map from 1958 Knox News Sentinel article; used as reference for street names in roadNetwork_preUrbanRenewal.shp; georeferenced by Chris DeRolphusgsImage_grayscale_1956.tif: May 18, 1956 black-and-white USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/ARA550590030582/usgsImage_colorinfrared_1972.tif: April 18, 1972 color infrared USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/AR6197002600096/usgsImage_grayscale_1976.tif: November 8, 1976 black-and-white USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/AR1VDUT00390010/

PLEASE NOTE: This dataset has been retired. It has been superseded by https://environment.data.gov.uk/dataset/04532375-a198-476e-985e-0579a0a11b47.The Flood Map for Planning (Rivers and Sea) includes several layers of information. This dataset covers Flood Zone 2 and should not be used without Flood Zone 3. It is our best estimate of the areas of land at risk of flooding, when the presence of flood defences are ignored and covers land between Zone 3 and the extent of the flooding from rivers or the sea with a 1 in 1000 (0.1%) chance of flooding each year. This dataset also includes those areas defined in Flood Zone 3.This dataset is designed to support flood risk assessments in line with Planning Practice Guidance ; and raise awareness of the likelihood of flooding to encourage people living and working in areas prone to flooding to find out more and take appropriate action.The information provided is largely based on modelled data and is therefore indicative rather than specific. Locations may also be at risk from other sources of flooding, such as high groundwater levels, overland run off from heavy rain, or failure of infrastructure such as sewers and storm drains.The information indicates the flood risk to areas of land and is not sufficiently detailed to show whether an individual property is at risk of flooding, therefore properties may not always face the same chance of flooding as the areas that surround them. This is because we do not hold details about properties and their floor levels. Information on flood depth, speed or volume of flow is not included.NOTE: We have paused quarterly updates of this dataset. Please visit the “Pause to Updates of Flood Risk Maps” announcement on our support pages for further information. We will provide notifications on the Flood Map for Planning website to indicate where we have new flood risk information. Other data related to the Flood Map for Planning will continue to be updated, including data relating to flood history, flood defences, and water storage areas.

This dataset is a compilation of county parcel data from Minnesota counties that have opted-in for their parcel data to be included in this dataset.

It includes the following 55 counties that have opted-in as of the publication date of this dataset: Aitkin, Anoka, Becker, Benton, Big Stone, Carlton, Carver, Cass, Chippewa, Chisago, Clay, Clearwater, Cook, Crow Wing, Dakota, Douglas, Fillmore, Grant, Hennepin, Houston, Isanti, Itasca, Jackson, Koochiching, Lac qui Parle, Lake, Lyon, Marshall, McLeod, Mille Lacs, Morrison, Mower, Murray, Norman, Olmsted, Otter Tail, Pennington, Pipestone, Polk, Pope, Ramsey, Renville, Rice, Saint Louis, Scott, Sherburne, Stearns, Stevens, Traverse, Waseca, Washington, Wilkin, Winona, Wright, and Yellow Medicine.

If you represent a county not included in this dataset and would like to opt-in, please contact Heather Albrecht (Heather.Albrecht@hennepin.us), co-chair of the Minnesota Geospatial Advisory Council (GAC)’s Parcels and Land Records Committee's Open Data Subcommittee. County parcel data does not need to be in the GAC parcel data standard to be included. MnGeo will map the county fields to the GAC standard.

County parcel data records have been assembled into a single dataset with a common coordinate system (UTM Zone 15) and common attribute schema. The county parcel data attributes have been mapped to the GAC parcel data standard for Minnesota: https://www.mngeo.state.mn.us/committee/standards/parcel_attrib/parcel_attrib.html

This compiled parcel dataset was created using Python code developed by Minnesota state agency GIS professionals, and represents a best effort to map individual county source file attributes into the common attribute schema of the GAC parcel data standard. The attributes from counties are mapped to the most appropriate destination column. In some cases, the county source files included attributes that were not mapped to the GAC standard. Additionally, some county attribute fields were parsed and mapped to multiple GAC standard fields, such as a single line address. Each quarter, MnGeo provides a text file to counties that shows how county fields are mapped to the GAC standard. Additionally, this text file shows the fields that are not mapped to the standard and those that are parsed. If a county shares changes to how their data should be mapped, MnGeo updates the compilation. If you represent a county and would like to update how MnGeo is mapping your county attribute fields to this compiled dataset, please contact us.

This dataset is a snapshot of parcel data, and the source date of the county data may vary. Users should consult County websites to see the most up-to-date and complete parcel data.

There have been recent changes in date/time fields, and their processing, introduced by our software vendor. In some cases, this has resulted in date fields being empty. We are aware of the issue and are working to correct it for future parcel data releases.

The State of Minnesota makes no representation or warranties, express or implied, with respect to the use or reuse of data provided herewith, regardless of its format or the means of its transmission. THE DATA IS PROVIDED “AS IS” WITH NO GUARANTEE OR REPRESENTATION ABOUT THE ACCURACY, CURRENCY, SUITABILITY, PERFORMANCE, MECHANTABILITY, RELIABILITY OR FITINESS OF THIS DATA FOR ANY PARTICULAR PURPOSE. This dataset is NOT suitable for accurate boundary determination. Contact a licensed land surveyor if you have questions about boundary determinations.

DOWNLOAD NOTES: This dataset is only provided in Esri File Geodatabase and OGC GeoPackage formats. A shapefile is not available because the size of the dataset exceeds the limit for that format. The distribution version of the fgdb is compressed to help reduce the data footprint. QGIS users should consider using the Geopackage format for better results.

This dataset is OBSOLETE as of 12/3/2024 and will be removed from ArcGIS Online on 12/3/2025.An updated version of this dataset is available at Land Use FY2024.This data set derives from several sources, and is updated annually with data current through July 1 of the reported year. The primary source is a data dump from the VISION assessing data system, which provided data up to date as of January 1, 2012, and is supplemented by information from subsequent building permits and Development Logs. (Use codes provided by this system combine aspects of land use, tax status, and condominium status. In an effort to clarify land use type the data has been cleaned and subdivided to break the original use code into several different fields.) The data set has further been supplemented and updated with development information provided by building permits issued by the Inspectional Services Department and from data found in the Development Log publication. Information from these sources is added to the data set periodically. Land use status is up to date as of the Last Modified date.Differences From “Official” Parcel LayerThe Cambridge GIS system maintains a separate layer of land parcels reflecting up to date subdivision and ownership. The parcel data associated with the Land Use Data set differs from the “official” parcel layer in a number of cases. For that reason this separate parcel layer is provided to work with land use data in a GIS environment. See the Assessing Department’s Parcel layer for the most up-to-date land parcel boundaries.Table of Land Use CodesThe following table lists all land use code found in the data layer:Land Use CodeLand Use DescriptionCategory0101MXD SNGL-FAM-REMixed Use Residential0104MXD TWO-FAM-RESMixed Use Residential0105MXD THREE-FM-REMixed Use Residential0111MXD 4-8-UNIT-APMixed Use Residential0112MXD >8-UNIT-APTMixed Use Residential0121MXD BOARDING-HSMixed Use Residential013MULTIUSE-RESMixed Use Residential031MULTIUSE-COMMixed Use Commercial0340MXD GEN-OFFICEMixed Use Commercial041MULTIUSE-INDMixed Use Industrial0942Higher Ed and Comm MixedMixed Use Education101SNGL-FAM-RESResidential1014SINGLE FAM W/AUResidential104TWO-FAM-RESResidential105THREE-FM-RESResidential106RES-LAND-IMPTransportation1067RES-COV-PKGTransportation1114-8-UNIT-APTResidential112>8-UNIT-APTResidential113ASSISTED-LIVAssisted Living/Boarding House121BOARDING-HSEAssisted Living/Boarding House130RES-DEV-LANDVacant Residential131RES-PDV-LANDVacant Residential132RES-UDV-LANDVacant Residential1322RES-UDV-PARK (OS) LNVacant Residential140CHILD-CARECommercial300HOTELCommercial302INN-RESORTCommercial304NURSING-HOMEHealth316WAREHOUSECommercial323SH-CNTR/MALLCommercial324SUPERMARKETCommercial325RETAIL-STORECommercial326EATING-ESTBLCommercial327RETAIL-CONDOCommercial330AUTO-SALESCommercial331AUTO-SUPPLYCommercial332AUTO-REPAIRCommercial334GAS-STATIONCommercialLand Use CodeLand Use DescriptionCategory335CAR-WASHCommercial336PARKING-GARTransportation337PARKING-LOTTransportation340GEN-OFFICEOffice341BANKCommercial342MEDICAL-OFFCHealth343OFFICE-CONDOOffice345RETAIL-OFFICOffice346INV-OFFICEOffice353FRAT-ORGANIZCommercial362THEATRECommercial370BOWLING-ALLYCommercial375TENNIS-CLUBCommercial390COM-DEV-LANDVacant Commercial391COM-PDV-LANDVacant Commercial392COM-UDV-LANDVacant Commercial3922CRMCL REC LNDVacant Commercial400MANUFACTURNGIndustrial401WAREHOUSEIndustrial404RES-&-DEV-FCOffice/R&D406HIGH-TECHOffice/R&D407CLEAN-MANUFIndustrial409INDUST-CONDOIndustrial413RESRCH IND CNDIndustrial422ELEC GEN PLANTUtility424PUB UTIL REGUtility428GAS-CONTROLUtility430TELE-EXCH-STAUtility440IND-DEV-LANDVacant Industrial442IND-UDV-LANDVacant Industrial920ParklandsPublic Open Space930Government OperationsGovernment Operations934Public SchoolsEducation940Private Pre & Elem SchoolEducation941Private Secondary SchoolEducation942Private CollegeHigher Education9421Private College Res UnitsEducation Residential943Other Educ & Research OrgHigher EducationLand Use CodeLand Use DescriptionCategory953CemeteriesCemetery955Hospitals & Medical OfficHealth956MuseumsHigher Education957Charitable ServicesCharitable/Religious960ReligiousCharitable/Religious971Water UtilityUtility972Road Right of WayTransportation975MBTA/RailroadTransportation9751MBTA/RailroadTransportation995Private Open SpacePrivately-Owned Open SpaceExplore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription ML type: Stringwidth: 16precision: 0 Map-Lot: This a unique parcel identifier found in the deed and used by the Assessing data system. In a few cases, where parcels have been subdivided subsequent to January 1, 2012, a placeholder Map-Lot number is assigned that differs from that used elsewhere.

MAP type: Stringwidth: 5precision: 0 This Map portion of the unique parcel identifier found in the deed and used by the Assessing data system. In a few cases, where parcels have been subdivided subsequent to January 1, 2012, a placeholder Map-Lot number is assigned that differs from that used elsewhere.

LOT type: Stringwidth: 5precision: 0 This is the Lot portion of the unique parcel identifier found in the deed and used by the Assessing data system. In a few cases, where parcels have been subdivided subsequent to January 1, 2012, a placeholder Map-Lot number is assigned that differs from that used elsewhere.

Location type: Stringwidth: 254precision: 0 In the great majority of cases this is the street address of the parcel as it is recorded in the Registry of Deed record. In instances where edits were made to the base parcel layer the best address available at the time is employed.

LandArea type: Doublewidth: 8precision: 15

LUCode type: Stringwidth: 254precision: 0 The four digit text string in this field indicates the primary usage of a parcel. While the codes are based on the standard Massachusetts assessing land use classification system, they differ in a number of cases; the coding system used here is unique to this data set. Note that other minor uses may occur on a property and, in some cases, tenants may introduce additional uses not reflected here (eg, office space used as a medical office, home based businesses).

LUDesc type: Stringwidth: 254precision: 0 The short description gives more detail about the specific use indicated by the Land Use Code. Most descriptions are taken from the standard Massachusetts assessing land use classification system.

Category type: Stringwidth: 254precision: 0 This broader grouping of land uses can be used to map land use data. You can find the land use data mapped at: https://www.cambridgema.gov/CDD/factsandmaps/mapgalleries/othermaps

ExistUnits type: Doublewidth: 8precision: 15 This value indicates the number of existing residential units as of July 1 of the reported year. A residential unit may be a house, an apartment, a mobile home, a group of rooms or a single room that is occupied (or, if vacant, intended for occupancy) as separate living quarters. This includes units found in apartment style graduate student housing residences and rooms in assisted living facilities and boarding houses are treated as also housing units. The unit count does not include college or graduate student dormitories, nursing home rooms, group homes, or other group quarters living arrangements.

MixedUseTy type: Stringwidth: 254precision: 0 Two flags are used for this field. “Groundfloor” indicates that a commercial use is found on the ground floor of the primary building, and upper floors are used for residential purposes. “Mixed” indicates that two or more uses are found throughout the structure or multiple structures on the parcel, one of which is residential.

GQLodgingH type: Stringwidth: 254precision: 0 A value of “Yes” indicates that the primary use of the property is as a group quarters living arrangement. Group quarters are a place where people live or stay, in a group living arrangement, that is owned or managed by an entity or organization providing housing and/or services for the residents. Group quarters include such places as college residence halls, residential treatment centers, skilled nursing facilities, group homes, military barracks, correctional facilities, and workers’ dormitories.

Most university dormitories are included under the broader higher education land use code, as most dormitories are included in the larger parcels comprising the bulk of higher education campuses.

GradStuden type: Stringwidth: 254precision: 0 A value of “Yes” indicates the parcel is used to house graduate students in apartment style units. Graduate student dormitories are treated as a higher education land use.

CondoFlag type: Stringwidth: 254precision: 0 “Yes” indicates that the parcel is owned as a condominium. Condo properties can include one or more uses, including residential, commercial, and parking. The great majority of such properties in Cambridge are residential only.

TaxStatus type: Stringwidth: 254precision: 0 A value indicates that the parcel is not subject to local property taxes. The following general rules are employed to assign properties to subcategories, though special situations exist in a number of cases.

o Authority: Properties owned the Cambridge Redevelopment Authority and Cambridge Housing Authority. o City: Properties owned by the City of Cambridge or cemetery land owned by the Town of Belmont. o Educ: Includes properties used for education purposes, ranging from pre-schools to university research facilities. (More detail about the level of education can be found using the Land Use Code.) o Federal: Properties owned by the federal government, including the Post Office. Certain properties with assessing data indicating Cambridge Redevelopment Authority ownership are in fact owned by the federal government as part of the Volpe Transportation Research Center and are so treated here. o Other: Nontaxable properties owned by a nonprofit organization and not