The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

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

BackgroundIn 1975, the Department of Forestry began a new management inventory designed to provide statistics of forest lands and timber volumes in a form that could be used to develop Forest Management Plans. This involved measuring over 500 Temporary Sample Plots (TSP) and measuring or remeasuring nearly 100 Stand Monitor Plots (SMP) each year. The SMPs were designed to provide information which would allow updating of inventory cover types between subsequent inventories.In 1985, nearing the completion of the second cycle of measuring TSPs, the focus began to change. It was felt that volume estimates acquired through TSPs were adequate for most strata and more emphasis be centered around collecting data on growth and yield. This led to the start of a Permanent Sample Plot (PSP) database. The program focused on establishing PSPs in regenerating and immature stand types. This focus continued between 1985 and 1991.In 1992, an evaluation of the existing PSP program and an understanding of the provinces need for growth and yield information led to the design of a 1,000 plot program focusing on growth and yield data collection. Since 1992, additional measurements have been added to the PSP program at the request of various data users. These include Damman Site Type (soils and vegetation), Hare Pellets, Woody Debris, and Song Birds.In 2007, the Newfoundland Forest Service began to use data loggers for collecting PSP data in the field. This speeds the data input process from the previous paper based system so that the data collected can be used shortly after the field season ends. The program also has controls to aid in avoiding errors during data entry; previously, errors were not detected until subsequent data analysis long after the plot measurements were completed.In 2024, the PSP database underwent a significant overhaul, involving a redesign of the 2007 Microsoft Access database and enabling data collection using more modern smartphone and tablet technology. This required the engineering of the database within the Oracle Forestry Enterprise geodatabase and management of data within the ArcGIS Enterprise environment. Data loggers have been replaced and now use iOS and Android technology to collect and measure PSPs within the ArcGIS Field Maps application.The data within this feature layer is updated daily at 24-hour intervals, beginning at 6:00PM NST. Information within this layer represents the most up-to-date and accurate information currently available.ObjectiveThe objectives of the Permanent Sample Plot Program are to provide stand growth data that can be used to calibrate and validate stand growth projection models and have a network of plots sufficient to sample the important stand conditions at an acceptable intensity. More specifically, the goal is to maintain a PSP program of at least 1,000 plots in natural and managed stands.The PSP Program incorporates measurement of other stand conditions and variables as deemed needed by the users of the data.Establishment and Allocation ProceduresThe allocation is based on proportional representation of stands by Strata (Working Group, Age Class and Site Class in a Management District). The actual plot locations are randomly located within the district and within the stand to avoid bias. As plots are lost to various disturbances, a new plot will be:Re-established at the same siteEstablished as a replacement in the same stratumEstablished in a strata type which is being under-represented, if the lost stratum is already well represented.Data CurrencyThe data within this feature layer is updated daily at 24-hour intervals, beginning at 6:00PM NST. Information within this layer represents the most up-to-date and accurate information currently available.

COMPLETED 2010. The data was converted from the most recent (2010) versions of the adopted plans, which can be found at https://cms3.tucsonaz.gov/planning/plans/ Supplemental Information: In March 2010, Pima Association of Governments (PAG), in cooperation with the City of Tucson (City), initiated the Planned Land Use Data Conversion Project. This 9-month effort involved evaluating mapped land use designations and selected spatially explicit policies for nearly 50 of the City's adopted neighborhood, area, and subregional plans and converting the information into a Geographic Information System (GIS) format. Further documentation for this file can be obtained from the City of Tucson Planning and Development Services Department or Pima Association of Governments Technical Services. A brief summary report was provided, as requested, to the City of Tucson which highlights some of the key issues found during the conversion process (e.g., lack of mapping and terminology consistency among plans). The feature class "Plan_boundaries" represents the boundaries of the adopted plans. The feature class "Plan_mapped_land_use" represents the land use designations as they are mapped in the adopted plans. Some information was gathered that is implicit based on the land use designation or zones (see field descriptions below). Since this information is not explicitly stated in the plans, it should only be viewed by City staff for general planning purposes. The feature class "Plan_selected_policies" represents the spatially explicit policies that were fairly straightforward to map. Since these policies are not represented in adopted maps, this feature class should only be viewed by City staff for general planning purposes only. 2010 - created by Jamison Brown, working as an independent contractor for Pima Association of Governments, created this file in 2010 by digitizing boundaries as depicted (i.e. for the mapped land use) or described in the plans (i.e. for the narrative policies). In most cases, this involved tracing based on parcel (paregion) or street center line (stnetall) feature classes. Snapping was used to provide line coincidence. For some map conversions, freehand sketches were drawn to mimick the freehand sketches in the adopted plan. Field descriptions Field descriptions for the "Plan_boundaries" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number ADOPT_DATE: Date of Plan adoption IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator Field descriptions for the "Plan_mapped_land_use" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number LU_DES: Land use designation (e.g., Low density residential) LISTED_ALLOWABLE_ZONES: Allowable zones as listed in the Plan LISTED_RAC_MIN: Minimum residences per acre (if applicable), as listed in the Plan LISTED_RAC_TARGET: Target residences per acre (if applicable), as listed in the Plan LISTED_RAC_MAX: Maximum residences per acre (if applicable), as listed in the Plan LISTED_FAR_MIN: Minimum Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_TARGET: Target Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_MAX: Maximum Floor Area Ratio (if applicable), as listed in the Plan BUILDING_HEIGHT_MAX Building height maximum (ft.) if determined by Plan policy IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator IMPLIED_ALLOWABLE_ZONES: Implied (not listed in the Plan) allowable zones IMPLIED_RAC_MIN: Implied (not listed in the Plan) minimum residences per acre (if applicable) IMPLIED_RAC_TARGET: Implied (not listed in the Plan) target residences per acre (if applicable) IMPLIED_RAC_MAX: Implied (not listed in the Plan) maximum residences per acre (if applicable) IMPLIED_FAR_MIN: Implied (not listed in the Plan) minimum Floor Area Ratio (if applicable) IMPLIED_FAR_TARGET: Implied (not listed in the Plan) target Floor Area Ratio (if applicable) IMPLIED_FAR_MAX: Implied (not listed in the Plan) maximum Floor Area Ratio (if applicable) IMPLIED_LU_CATEGORY: Implied (not listed in the Plan) general land use category. General categories used include residential, office, commercial, industrial, and other.PurposeLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Dataset ClassificationLevel 0 - OpenKnown UsesThis layer is intended to be used in the City of Tucson's Open Data portal and not for regular use in ArcGIS Online, ArcGIS Enterprise or other web applications.Known ErrorsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Data ContactJohn BeallCity of Tucson Development Services520-791-5550John.Beall@tucsonaz.govUpdate FrequencyLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

Status: COMPLETED 2010. The data was converted from the most recent (2010) versions of the adopted plans, which can be found at http://cms3.tucsonaz.gov/planning/plans/Contact: John Beall, City of Tucson Development Services, 520-791-5550, John.Beall@tucsonaz.gov. Jamie Brown, City of Tucson, 520-837-6934. Jamie.Brown@tucsonaz.govIntended Use: For mappingSupplemental Information: In March 2010, Pima Association of Governments (PAG), in cooperation with the City of Tucson (City), initiated the Planned Land Use Data Conversion Project. This 9-month effort involved evaluating mapped land use designations and selected spatially explicit policies for nearly 50 of the City's adopted neighborhood, area, and subregional plans and converting the information into a Geographic Information System (GIS) format. Further documentation for this file can be obtained from the City of Tucson Planning and Development Services Department or Pima Association of Governments Technical Services. A brief summary report was provided, as requested, to the City of Tucson which highlights some of the key issues found during the conversion process (e.g., lack of mapping and terminology consistency among plans). The feature class "Plan_boundaries" represents the boundaries of the adopted plans. The feature class "Plan_mapped_land_use" represents the land use designations as they are mapped in the adopted plans. Some information was gathered that is implicit based on the land use designation or zones (see field descriptions below). Since this information is not explicitly stated in the plans, it should only be viewed by City staff for general planning purposes. The feature class "Plan_selected_policies" represents the spatially explicit policies that were fairly straightforward to map. Since these policies are not represented in adopted maps, this feature class should only be viewed by City staff for general planning purposes only. 2010 - created by Jamison Brown, working as an independent contractor for Pima Association of Governments, created this file in 2010 by digitizing boundaries as depicted (i.e. for the mapped land use) or described in the plans (i.e. for the narrative policies). In most cases, this involved tracing based on parcel (paregion) or street center line (stnetall) feature classes. Snapping was used to provide line coincidence. For some map conversions, freehand sketches were drawn to mimick the freehand sketches in the adopted plan. Field descriptions Field descriptions for the "Plan_boundaries" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number ADOPT_DATE: Date of Plan adoption IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator Field descriptions for the "Plan_mapped_land_use" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number LU_DES: Land use designation (e.g., Low density residential) LISTED_ALLOWABLE_ZONES: Allowable zones as listed in the Plan LISTED_RAC_MIN: Minimum residences per acre (if applicable), as listed in the Plan LISTED_RAC_TARGET: Target residences per acre (if applicable), as listed in the Plan LISTED_RAC_MAX: Maximum residences per acre (if applicable), as listed in the Plan LISTED_FAR_MIN: Minimum Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_TARGET: Target Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_MAX: Maximum Floor Area Ratio (if applicable), as listed in the Plan BUILDING_HEIGHT_MAX Building height maximum (ft.) if determined by Plan policy IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator IMPLIED_ALLOWABLE_ZONES: Implied (not listed in the Plan) allowable zones IMPLIED_RAC_MIN: Implied (not listed in the Plan) minimum residences per acre (if applicable) IMPLIED_RAC_TARGET: Implied (not listed in the Plan) target residences per acre (if applicable) IMPLIED_RAC_MAX: Implied (not listed in the Plan) maximum residences per acre (if applicable) IMPLIED_FAR_MIN: Implied (not listed in the Plan) minimum Floor Area Ratio (if applicable) IMPLIED_FAR_TARGET: Implied (not listed in the Plan) target Floor Area Ratio (if applicable) IMPLIED_FAR_MAX: Implied (not listed in the Plan) maximum Floor Area Ratio (if applicable) IMPLIED_LU_CATEGORY: Implied (not listed in the Plan) general land use category. General categories used include residential, office, commercial, industrial, and other.Usage: This layer is intended to be used in the City of Tucson's Open Data portal and not for regular use in ArcGIS Online, ArcGIS Enterprise or other web applications.Link to Open Data item: https://gisdata.tucsonaz.gov/datasets/redevelopment-plans-open-data

General Plan Land Use is a Polygon FeatureClass representing It is primarily used to indicate the General Plan Land Use designations within the City Limits. Updates to the layer are requested to the GIS Division by Community Development as directed by the City Council. The latest amendment is from September 2018 per resolution 18-055. General Plan Land Use has the following fields:

OBJECTID: Unique identifier automatically generated by Esri type: OID, length: 4, domain: none

LAND_USE: The land use code associated with the feature type: String, length: 16, domain: none

LABEL: The label associated with the feature type: String, length: 80, domain: none

ResolutionNumber: The resolution number type: String, length: 50, domain: none

GlobalID: Unique identifier automatically generated for features in enterprise database type: GlobalID, length: 38, domain: none

Shape: Field that stores geographic coordinates associated with feature type: Geometry, length: 4, domain: noneShape.STArea()The area of the geometric featuretype: Double, length: noneShape.STLength() The length of the geometric featuretype: Double, length: none

Analysis of ‘Addresses’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ed1c4987-f6ba-4ed3-8560-6e7314426948 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

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.

<p>Data Source Type: ESRI ArcGIS Enterprise Geodatabase</p>
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<p>Preparation Method: N/A</p>
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<p>Publish Frequency: Weekly</p>
<br />
<p>Publish Method: Automatic</p>
<br />
<p><a href='https://gis.tempe.gov/address-dictionary/' target='_blank'>Data Dictionary</a>
  <br />
</p>

--- Original source retains full ownership of the source dataset ---

Esri ArcGIS Online (AGOL) Hosted Feature Layer for accessing the MDOT SHA Park and Rides data product.MDOT SHA Park and Rides data consists of point & polygon geometric features which represent the geographic locations & areas of MDOT SHA Park and Ride Facilities along roadways throughout the State of Maryland. This data product includes components related to counts of select space types and the usage of those types, averaged over the previous two inspection cycles (fall and spring of every year).MDOT SHA Park and Rides data is dynamically compiled & maintained by the MDOT SHA OIT Enterprise Information Services - GIS Team according to these data product requirements. OIT GIS staff members intimately familiar with this data maintenance process are Mr. John Shiu and Mr. Elliott Plack. MDOT SHA Park and Rides data is owned by the MDOT SHA Office of Planning & Preliminary Engineering (OPPE), under the MDOT SHA OPPE Regional Intermodal Planning Division (RIPD).MDOT SHA Park and Rides data symbology is defined by the regular vehicle occupancy percentile over the last two inspection cycles. The symbols are derived from the MUTCD D4-2 Park & Ride sign specification, and are colorized by the percentile. The colors are selected from the MUTCD color set:Green: 50% full or lessYellow: 50% to 75% full75% full or greaterFor additional information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

Esri ArcGIS Online (AGOL) Hosted, View Feature Layer for accessing the MDOT SHA Facilities data product.MDOT SHA Facilities data consists of point geometric features which represent the geographic locations of MDOT SHA Facilities throughout the State of Maryland. Facility types included are Headquarters, Complexes, District Offices, Maintenance Shops & Landscape Depots.MDOT SHA Facilities data is maintained by the MDOT SHA OIT Enterprise Information Services. MDOT SHA Facilities data is updated on an Irregular / As-Needed basis. This data was last updated in October 2020.For more information, contact MDOT SHA OIT Enterprise Information Services: Email: GIS@mdot.maryland.gov

The Public Works Department operates a robust graffiti abatement program. Its strategic approach includes proactive abatement through directed patrols across the City. This “enterprise” approach strengthens the City’s commitment to prevention by aggressively removing graffiti in order to communicate that a clean city is valued. This metric discourages repeat behavior. “Hot spot” areas frequently targeted by graffiti vandals are patrolled regularly with the overall goal of removing graffiti as soon as possible. In addition, the approach to data gathering and measuring has improved exponentially through the use of Tempe’s emerging Enterprise GIS system.Contact: Sue TaaffeContact E-Mail: sue_taaffe@tempe.govContact Phone: 480-350-8663Link: https://www.tempe.gov/city-hall/public-works/transportation/streets-traffic-opsData Source Type: Geospatial

This file contains names and codes for the Local Enterprise Partnerships (LEP) (non-overlapping parts) in England as at 1 April 2020. (File Size - 16 KB). Field Names – LEPNOP20CD, LEPNOP20NM, FID

Field Types – Text, Text

Field Lengths – 9, 51

FID = The FID, or Feature ID is created by the publication process when the names and codes / lookup products are published to the Open Geography portal. REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_non_overlapping_parts_April_2020_Names_and_Codes_in_England_2022/FeatureServer

This service displays polygons for current land use records and features used to inventory land use patterns. Data is updated, maintained and published from the enterprise GIS database to reflect the most recent information for the City of Las Cruces. Data is organized by activity, structure, or function. Layer Type: PolygonData Owner: Community DevelopmentAuthoritative: YesDownloadable: N/AInitial Dataset Creation: UnknownLast update: 2018 Update Frequency: As necessary Status: CurrentReason for Updates: Classify and inventory land use patternsSource data: N/AReference Source: Land Based Classification Standards (LBCS)Projected Coordinate System: N/AReference information: The classification is a snapshot at one particular time. Uses, businesses, and new construction occur on a daily basis. Also, there may be several parcels that make up a particular site. The classification used was the predominant use of that parcel (e.g., a residential condo plat has a parcel for common area that is mostly parking and parcels for each residential unit, the common area parcel was classed as parking and the parcels for the units as residential). It is important to note that parcel information will change if not updated. The Land-Based Classification System (LBCS) is the industry standard for classification developed by the American Planning Association. It is not an ideal system in that classification codes for certain dimensions do not exist, multiple classes may fit for any one dimension, and a level of subjectivity occurs during classification. LBCS consists of five major categories called “dimensions”: Web site can be found at: https://www.planning.org/lbcs/Five Dimensions for Classifying Land-Use DataActivity1000: Residential activities2000: Shopping, business, or trade activities3000: Industrial, manufacturing, and waste-related activities4000: Social, institutional, or infrastructure-related activities5000: Travel or movement activities6000: Mass assembly of people7000: Leisure activities8000: Natural resources-related activities9000: No human activity or unclassifiable activityActivity refers to the actual use of land based on its observable characteristics. It describes what actually takes place in physical or observable terms (e.g., farming, shopping, manufacturing, vehicular movement, etc.). An office activity, for example, refers only to the physical activity on the premises, which could apply equally to a law firm, a nonprofit institution, a court house, a corporate office, or any other office use. Similarly, residential uses in single-family dwellings, multi-family structures, manufactured houses, or any other type of building, would all be classified as residential activity.Activity Note:The five fields of Activ_20, Activ_40, Activ_60, Activ_80, and Activ_100 were used to identify different actual uses noted on a parcel. The intent was when multiple activity classes exist to determine visually the area taken up by each use (e.g., a parcel has a restaurant and an office, the office takes up 60% of the building to class the restaurant under Activ_40 and the office under Activ_60). This worked for some parcels, but many parcels had more than five possible classes or determination of square footage was difficult to determine because floor plan-site plan information was not readily available. Some pointers on using the activity field include:>On parcels having multiple activity classes an overall activity class was put under Activ_100 in order to extract data more readily. The ‘12’ class represents mixed use. Mixed use for this inventory meant a residential use existed on the same parcel with a non-residential use. It does not assess non-residential mixed use or the type of mixed use (e.g., vertical in same building or different uses in different locations on same parcel). The Activ_100 class for multiple activities used was the highest percentage class by area, except for undeveloped (9990) where the highest percentage class by area was used if 9990 area appeared to be less than 50% of the parcel area. >For contractor yards the 2013 Inventory used either 3000, Industrial-Manufacturing, as a catch-all if the activity was not very clear. It used 3300, Construction Activities, for activities related to construction contractors which is different than the APA Classification. 3300 in the APA Classification is actually describing the stage the parcel would be in physical construction.Function1000: Residence or accommodation functions2000: General sales or services3000: Manufacturing and wholesale trade4000: Transportation, communication, information, and utilities5000: Arts, entertainment, and recreation6000: Education, public admin., health care, and other inst.7000: Construction-related businesses8000: Mining and extraction establishments9000: Agriculture, forestry, fishing and huntingFunction refers to the economic function or type of enterprise using the land. Every land use can be characterized by the type of enterprise it serves. Land-use terms, such as agricultural, commercial, industrial, relate to enterprises. The type of economic function served by the land use gets classified in this dimension; it is independent of actual activity on the land. Enterprises can have a variety of activities on their premises, yet serve a single function. For example, two parcels are said to be in the same functional category if they belong to the same enterprise, even if one is an office building and the other is a factory.Function Note:The five fields of Function, Funct_40, Funct_60, Funct_80, and Funct_100 were used to identify different economic types noted on a parcel. The intent was to indicate the percentage of the building on the parcel related to that function. The function field chosen mimics the activity field in most cases. Unlike Active_100, an overall function class was not put under Funct_100 on parcels with multiple functions. Structural Character1000: Residential buildings2000: Commercial buildings and other specialized structures3000: Public assembly structures4000: Institutional or community facilities5000: Transportation-related facilities6000: Utility and other non-building structures7000: Specialized military structures8000: Sheds, farm buildings, or agricultural facilities9000: No structureStructural character refers to the type of structure or building on the land. Land-use terms embody a structural or building characteristic, which suggests the utility of the space (in a building) or land (when there is no building). Land-use terms, such as single-family house, office building, warehouse, hospital building, or highway, also describe structural characteristic. Although many activities and functions are closely associated with certain structures, it is not always so. Many buildings are often adapted for uses other than its original use. For instance, a single-family residential structure may be used as an office.Structural Note:The predominant structural type class was selected when multiple structures existed on a parcel. Some pointers on using the structural field include:>1130, Accessory Units, in the APA Classification is for secondary units. The 2013 Inventory used this class to identify accessory structures like sheds, etc. Secondary units on the same parcel are noted in the Units field.>1140, townhouse, and 1121, duplex, were sometimes used interchangeably. Townhouse for the APA classification is three or more attached dwelling units. Efforts were made to correct errors, but several likely were not caught. >1150, manufactured home, should be fairly accurate. NM does allow a double-wide manufactured home set on a foundation in a single-family zone. Several instances in the 2008 inventory classed this as 1100 or 1110, single-family site built unit. Efforts were made to class these as 1150 in the 2013 inventory. >1350, Temporary Structures, was used for RV Parks that appear to be more transitory. Otherwise, 1150, Manufactured Home, was used. Site Development Character1000: Site in natural state2000: Developing site3000: Developed site -- crops, grazing, forestry, etc.4000: Developed site -- no buildings and no structures5000: Developed site -- non-building structures6000: Developed site -- with buildings7000: Developed site -- with parks8000: Not applicable to this dimension9000: Unclassifiable site development characterSite development character refers to the overall physical development character of the land. It describes "what is on the land" in general physical terms. For most land uses, it is simply expressed in terms of whether the site is developed or not. But not all sites without observable development can be treated as undeveloped. Land uses, such as parks and open spaces, which often have a complex mix of activities, functions, and structures on them, need categories independent of other dimensions. This dimension uses categories that describe the overall site development characteristics.Site Note:All efforts were made to follow the site classification. Some pointers on using the site field include:>2000, Developing Site, was used if the site was under construction. The entire Metro Verde South Phase 1C plat was used for this class. A lot of home building activity was occurring in this area, but many lots were not under construction at time of site check. Ownership1000: No constraints--private ownership2000: Some constraints--easements or other use restrictions3000: Limited restrictions--leased and other tenancy restrictions4000: Public restrictions--local, state, and federal ownership5000: Other public use restrictions--regional, special districts, etc.6000: Nonprofit ownership restrictions7000: Joint ownership character--public entities8000: Joint ownership character--public, private, nonprofit, etc.9000: Not applicable to this dimensionOwnership refers to the relationship between the use and its land rights. Since the function of most land uses is either public or

In 2014 and 2015, The LA County Enterprise GIS team under the Geographic Information Officer worked with the Unincorporated Area Deputies and Field Deputies of each Board Office to establish names that reflect the desires of residents. CSAs differ from the more informal Community geographies because:They are focused on broad statistics and reporting, not mapping of communities.They represent board approved names assigned to Census block groups and city boundaries.They cover the entire unincorporated County (no gaps).There are not overlapping areas. Additionally, CSAs use the following naming conventions:All names are assumed to begin with Unincorporated (e.g. Unincorporated El Camino Village) which will not be part of the CSA Name (so the name of the Statistical Area would be El Camino Village).Names will not contain “Island.” Beginning each name with Unincorporated will distinguish an area from any surrounding cities. There may be one or more exceptions for certain small areas (e.g. Bandini Islands)A forward slash implies an undetermined boundary between two areas within a statistical geography (e.g. Westfield/Academy Hills or View Park/Windsor Hills)Certain established names may include hyphens (e.g. Florence-Firestone)Aliases may be defined in parentheses (e.g. Unincorporated Long Beach (Bonner/Carson Park))The original set of names were derived from community names used in the 2011 Redistricting process, chosen with the assistance of the Board of Supervisors.Updates: 2023 December: CSA data updated to include "Unincorporated Charter Oak" (south of 10 Freeway) into "Unincorporated Covina".2023 June: CSA data was updated to include "Kinneloa Mesa" community, which was a part of Unincorporated East Pasadena.2023 January: Updated layer schema to include feature type (“FEAT_TYPE”) field, which can be one of land, water, breakwater, or pier (consistent with the City Boundaries layer).2022 December: CSA data was updated to incorporate the “Tesoro Del Valle” annexation to the city of Santa Clarita. Unincorporated Valencia is now completely annexed to the City of Santa Clarita. In addition to land area, this data also includes other feature types such as piers, breakwater and water area. 2022 September: CSA data was updated to match with city boundaries along shoreline/coastal area and minor boundary adjusted in some other areas.