Initial Data Capture: Building were originally digitized using ESRI construction tools such as rectangle and polygon. Textron Feature Analyst was then used to digitize buildings using a semi-automated polygon capture tool as well as a fully automated supervised learning method. The method that proved to be most effective was the semi-automated polygon capture tool as the fully automated process produced polygons that required extensive cleanup. This tool increased the speed and accuracy of digitizing by 40%.Purpose of Data Created: To supplement our GIS viewers with a searchable feature class of structures within Ventura County that can aid in analysis for multiple agencies and the public at large.Types of Data Used: Aerial Imagery (Pictometry 2015, 9inch ortho/oblique, Pictometry 2018, 6inch ortho/oblique) Simi Valley Lidar Data (Q2 Harris Corp Lidar) Coverage of Data:Buildings have been collected from the aerial imageries extent. The 2015 imagery coverage the south county from the north in Ojai to the south in thousand oaks, to the east in Simi Valley, and to the West in the county line with Santa Barbara. Lockwood Valley was also captured in the 2015 imagery. To collect buildings for the wilderness areas we needed to use the imagery from 2007 when we last flew aerial imagery for the entire county. 2018 Imagery was used to capture buildings that were built after 2015.Schema: Fields: APN, Image Date, Image Source, Building Type, Building Description, Address, City, Zip, Data Source, Parcel Data (Year Built, Basement yes/no, Number of Floors) Zoning Data (Main Building, Out Building, Garage), First Floor Elevation, Rough Building Height, X/Y Coordinates, Dimensions. Confidence Levels/Methods:Address data: 90% All Buildings should have an address if they appear to be a building that would normally need an address (Main Residence). To create an address, we do a spatial join on the parcels from the centroid of a building polygon and extract the address data and APN. To collect the missing addresses, we can do a spatial join between the master address and the parcels and then the parcels back to the building polygons. Using a summarize to the APN field we will be able to identify the parcels that have multiple buildings and delete the address information for the buildings that are not a main residence.Building Type Data: 99% All buildings should have a building type according to the site use category code provided from the parcel table information. To further classify multiple buildings on parcels in residential areas, the shape area field was used to identify building polygons greater than 600 square feet as an occupied residence and all other buildings less than that size as outbuildings. All parcels, inparticular parcels with multiple buildings, are subject to classification error. Further defining could be possible with extensive quality control APN Data: 98% All buildings have received APN data from their associated parcel after a spatial join was performed. Building overlapping parcel lines had their centroid derived which allowed for an accurate spatial join.Troubleshooting Required: Buildings would sometimes overlap parcel lines making spatial joining inaccurate. To fix this you create a point from the centroid of the building polygon, join the parcel information to the point, then join the point with the parcel information back to the building polygon.

This dataset is one of several segments of a regional high detailed stream flowpath dataset. The data was separated using the TOPO 50 map series extents.The stream network was originally created for the purpose of high detailed work along rivers and streams in the Wellington region. It was started as a pilot study for the Mangatarere subcatchment of the Waiohine River for the Environmental Sciences department who was attempting to measure riparian vegetation. The data was sourced from a modelled stream network created using the 2013 LiDAR digital elevation model. Once the Mangatarere was complete the process was expanded to cover the entire region on an as needed basis for each whaitua. This dataset is one of several that shows the finished stream datasets for the Wairarapa region.The base stream network was created using a mixture of tools found in ArcGIS Spatial Analyst under Hydrology along with processes located in the Arc Hydro downloadable add-on for ArcGIS. The initial workflow for the data was based on the information derived from the help files provided at the Esri ArcGIS 10.1 online help files. The updated process uses the core Spatial Analyst tools to generate the streamlines while digital dams are corrected using the DEM Reconditioning tool provided by the Arc Hydro toolset. The whaitua were too large for processing separated into smaller units according to the subcatchments within it. In select cases like the Taueru subcatchment of the Ruamahanga these subcatchments need to be further defined to allow processing. The catchment boundaries available are not as precise as the LiDAR information which causes overland flows that are on edges of the catchments to become disjointed from each other and required manual correction.Attributes were added to the stream network using the River Environment Classification (REC) stream network from NIWA. The Spatial Join tool in Arcmap was used to add the Reach ID to each segment of the generated flow path. This ID was used to join a table which had been created by intersecting stream names (generated from a point feature class available from LINZ) with the REC subcatchment dataset. Both of the REC datasets are available from NIWA's website.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

This dataset is one of several segments of a regional high detailed stream flowpath dataset. The data was separated using the TOPO 50 map series extents.The stream network was originally created for the purpose of high detailed work along rivers and streams in the Wellington region. It was started as a pilot study for the Mangatarere subcatchment of the Waiohine River for the Environmental Sciences department who was attempting to measure riparian vegetation. The data was sourced from a modelled stream network created using the 2013 LiDAR digital elevation model. Once the Mangatarere was complete the process was expanded to cover the entire region on an as needed basis for each whaitua. This dataset is one of several that shows the finished stream datasets for the Wairarapa region.The base stream network was created using a mixture of tools found in ArcGIS Spatial Analyst under Hydrology along with processes located in the Arc Hydro downloadable add-on for ArcGIS. The initial workflow for the data was based on the information derived from the help files provided at the Esri ArcGIS 10.1 online help files. The updated process uses the core Spatial Analyst tools to generate the streamlines while digital dams are corrected using the DEM Reconditioning tool provided by the Arc Hydro toolset. The whaitua were too large for processing separated into smaller units according to the subcatchments within it. In select cases like the Taueru subcatchment of the Ruamahanga these subcatchments need to be further defined to allow processing. The catchment boundaries available are not as precise as the LiDAR information which causes overland flows that are on edges of the catchments to become disjointed from each other and required manual correction.Attributes were added to the stream network using the River Environment Classification (REC) stream network from NIWA. The Spatial Join tool in Arcmap was used to add the Reach ID to each segment of the generated flow path. This ID was used to join a table which had been created by intersecting stream names (generated from a point feature class available from LINZ) with the REC subcatchment dataset. Both of the REC datasets are available from NIWA's website.

For every address in the City of Kitchener, a GIS spatial join has been created to select the closest Park, Playground, Elementary School, etc

Visit this website for an explanation of the parcel's assessing info: https://www.mass.gov/info-details/massgis-data-property-tax-parcels#attributes-Through a series of joins, spatial joins and select by location with various datasets, the following key attribute fields were populated in the Municipal Properties dataset.Open Space/Conservation Land Attributes are: OS_ID OS_ID is a unique ID for polygons in the open space/conservation land database, [Fee_Owner], [Level_Protection], OLI_1_INT. For an explanation of the coded values used in these fields, visit: https://www.mass.gov/info-details/massgis-data-protected-and-recreational-openspace#attributes-Zoning info for the parcel is contained within [ZONECODE], [MinLot_ac], and [SubStd_Sz]. Zonecode assigned to a parcel is based on the location of the center point of the parcel. The minimum lot size is per the Town's zoning bylaws. Parcel's smaller than the bylaws minimum lot size were assigned a 'yes' value in the Substandard Size attribute column.The attribute [vacant] was assigned a 'yes' value if the assessor's Building Value > $0.00 for the parcel OR the parcel contained one or more structures per the MassGIS structures dataset.The attribute [conserved] was assigned a 'yes' value if the parcel's center point coincided with a parcel in the Dukes County Open Space & Conservation Land dataset.The attribute [AbutPot] Abutter Potential is assigned a 'yes' value if any of the following attributes contain a 'yes' value: [AbutMuni], [AbutOS], or [AbutVacPrv].The attribute [Notes] were manually added by the GIS staff based on local knowledge.Attributes dealing with Abutters: [AbutMuni] indicates if the municipal owned parcel abuts other municipally owned parcels. 'Abuts' are any parcels that thouch (share a boundary) or are within 40ft of each other. [AbutOS] indicates if the municipal owned parcel abuts a parcel which is open space/conservation land. [AbutVacPrv] indicates if the municipal owned parcel abuts a parcel which is vacant residential land. "Vacant Residential Land" was identified by the assessor's Use Code = 1300 or 1310 for the parcel.Identifying Neighbors: All municipal parcels were buffered 40ft and dissolved together. Then that resulting multi-part dataset was 'exploded' so each distinct polygon was represented by a distinct record in the attribute table. Each polygon was assigned an ID number. This output is the "Municipal Property Clusters".Via a Spatial Join, the respective Cluster (aka group ID) was assigned to the respective municipal parcel. Similarly, by finding the (a) Vacant Residential properties and (b) Conservation Land properties that intersected with the Municipal Property Clusters, the Cluster/Group ID was assigned to the respective vacant residential properties and conservation land properties. A & B each have a distinct dataset which is included in this bundle of data.By having the Group ID in the Municipal Properties dataset and the Vacant Residential and Conservation Land datasets ...let's say a parcel has a Group ID = 3 --> then you can find the abutters by finding the other Municipal Parcels with a Group ID = 3 AND look in the Vacant Residential attribute table for Group ID = 3 AND look in the Conservation Land attribute table for Group ID = 3 --AND then you have tons of info at your fingertips regarding that municipally owned parcel and its abutting vacant properties.

Several previously published reports and geographic information system (GIS) data layers were used to code information on site attributes for each assessment plot using the spatial join tool in ArcMap. This information was used for an analysis of dieback and non-dieback habitat characteristics. The results of this analysis are presented in this table which depicts the probability of heavy to severe canopy dieback occurring at some time at a particular 30 x 30 m pixel location within the study area.

Data Source: The primary data source used for this analysis are point-level business establishment data from InfoUSA. This commercial database produced by InfoGroup provides a comprehensive list of businesses in the SCAG region, including their industrial classification, number of employees, and several additional fields. Data have been post-processed for accuracy by SCAG staff and have an effective date of 2016. Locally-weighted regression: First, the SCAG region is overlaid with a grid, or fishnet, of 1km, 2km, and ½-km per cell. At the 1km cell size, there are 16,959 cells covering the SCAG region. Using the Spatial Join feature in ArcGIS, a sum total of business establishments and total employees (i.e., not separated by industrial classification) were joined to each grid cell. Note that since cells are of a standard size, the employment total in a cell is the equivalent of the employment density. A locally-weighted regression (LWR) procedure was developed using the R Statistical Software package in order to identify subcenters.The below procedure is described for 1km grid cells, but was repeated for 2km and 1/2km cells. Identify local maxima candidates.Using R’s lwr package, each cell’s 120 nearest neighbors, corresponding to roughly 5.5 km in each direction, was explored to identify high outliers or local maxima based on the total employment field. Cells with a z-score of above 2.58 were considered local maxima candidates.Identify local maxima. LWR can result in local maxima existing within close proximity. This step used a .dbf-format spatial weights matrix (knn=120 nearest neighbors) to identify only cells which are higher than all of their 120 nearest neighbors. At the 1km scale, 84 local maxima were found, which will form the “peak” of each individual subcenter. Search adjacent cells to include as part of each subcenter. In order to find which cells also are part of each local maximum’s subcenter, we use a queen (adjacency) contiguity matrix to search adjacent cells up to 120 nearest neighbors, adding cells if they are also greater than the average density in their neighborhood. A total of 695 cells comprise subcenters at the 1km scale. A video from Kane et al. (2018) demonstrates the above aspects of the methodology (please refer to 0:35 through 2:35 of https://youtu.be/ylTWnvCCO54), with several minor differences which result in a different final map of subcenters: different years and slightly different post-processing steps for InfoUSAdata, video study covers 5-county region (Imperial county not included), and limited to 1km scale subcenters.A challenge arises in that using 1km grid cells may fail to identify the correct local maximum for a particularly large employment center whose experience of high density occurs over a larger area. The process was repeated at a 2km scale, resulting in 54 “coarse scaled” subcenters. Similarly, some centers may exist with a particularly tightly-packed area of dense employment which is not detectable at the medium, 1km scale. The process was repeated again with ½-km grid cells, resulting in 95 “fine scaled” subcenters. In many instances, boundaries of fine, medium, and coarse scaled subcenters were similar, but differences existed. The next step was to qualitatively comparing results at each scale to create the final map of 72 job centers across the region. Most centers are medium scale, but some known areas of especially employment density were better captured at the 2km scale while . Giuliano and Small’s (1991) “ten jobs per acre” threshold was used as a rough guide to test for reasonableness when choosing a larger or smaller scale. For example, in some instances, a 1km scale included much additional land which reduced job density well below 10 jobs per acre. In this instance, an overlapping or nearby 1/2km scaled center provided a better reflection of the local employment peak. Ultimately, the goal was to identify areas where job density is distinct from nearby areas. Finally, in order to serve land use and travel demand modeling purposes for Connect SoCal, job centers were joined to their nearest TAZ boundaries. While the identification mechanism described above uses a combination of point and grid cell boundaries, the job centers boundaries expressed in this layer, and used for Connect SoCal purposes, are built from TAZ geographies. In Connect SoCal, job centers are associated with one of three strategies: focused growth, coworking space, or parking/AVR.Data Field/Value description:name: Name of job center based on name of local jurisdiction(s) or other discernable feature.Focused_Gr: Indicates whether job center was used for the 2020 RTP/SCS Focused Growth strategy, 1: center was used, 0: center was not used.Cowork: Indicates whether job center was used for the 2020 RTP/SCS Co-working space strategy, 1: center was used, 0: center was not used.Park_AVR: Indicates whether job center was used for the 2020 RTP/SCS parking and average vehicle ridership (AVR) strategies, 1: center was used, 0: center was not used. nTAZ: number of Transportation Analysis Zones (TAZs) which comprise this center.emp16: Estimated number of workers within job center boundaries based on 2016 InfoUSA point-based business establishment data. Values are rounded to the nearest 1000. acres: Land area within job center boundaries based on grid-based identification mechanism (i.e., not based on TAZ boundaries shown). Values are rounded to the nearest 100.

Shows the mean July max temperatures for each neighborhood in the city of Toledo.Workflow:- Spatial Join of July Temperatures (census tracts) and Neighborhoods- On the new layer, Summary Statistics of Max July Temp by name- With the new layer, Join Mean Max Temp to Neighborhoods by name- Data > Export features to shapefile

Summary of category 3 water pollution incidents reported to the Environment Agency are held on the National Incident Reporting System. Sum of incidents reported between 2001 and 2020 summarised by WFD Operational Catchment.Extracted from NIRS for Closed Category 3 and 4 Incidents classified as 3 and 4 in the Water Environmental Level code field from 01/01/2020 until date of extraction 20/05/2024. This data includes grid references for each incident. These Grid references were then used to map each Incident within ArcMap and analyse using the Spatial Join Tool how many incidents are located within each WFD Operational. Within the data tab shows a table of Counts of Category 3 and 4 Incidents within each WFD Operational Catchments from 01/01/2020 to data extraction date (20/05/2024).

Parcels and property data maintained and provided by Lee County Property Appraiser are converted to points. Property attribute data joined to parcel GIS layer by Lee County Government GIS. This dataset is generally used in spatial analysis.Process description: Parcel polygons, condominium points and property data provided by the Lee County Property Appraiser are processed by Lee County's GIS Department using the following steps:Join property data to parcel polygons Join property data to condo pointsConvert parcel polygons to points using ESRI's ArcGIS tool "Feature to Point" and designate the "Source" field "P".Load Condominium points into this layer and designate the "Source" field "C". Add X/Y coordinates in Florida State Plane West, NAD 83, feet using the "Add X/Y" tool.Projected coordinate system name: NAD_1983_StatePlane_Florida_West_FIPS_0902_FeetGeographic coordinate system name: GCS_North_American_1983

 Name
 Type
 Length
 Description


 STRAP
 String
 25
 17-digit Property ID (Section, Township, Range, Area, Block, Lot)


 BLOCK
 String
 10
 5-digit portion of STRAP (positions 9-13)


 LOT
 String
 8
 Last 4-digits of STRAP


 FOLIOID
 Double
 8
 Unique Property ID


 MAINTDATE
 Date
 8
 Date LeePA staff updated record


 MAINTWHO
 String
 20
 LeePA staff who updated record


 UPDATED
 Date
 8
 Data compilation date


 HIDE_STRAP
 String
 1
 Confidential parcel ownership


 TRSPARCEL
 String
 17
 Parcel ID sorted by Township, Range & Section


 DORCODE
 String
 2
 Department of Revenue. See https://leepa.org/Docs/Codes/DOR_Code_List.pdf


 CONDOTYPE
 String
 1
 Type of condominium: C (commercial) or R (residential)


 UNITOFMEAS
 String
 2
 Type of Unit of Measure (ex: AC=acre, LT=lot, FF=frontage in feet)


 NUMUNITS
 Double
 8
 Number of Land Units (units defined in UNITOFMEAS)


 FRONTAGE
 Integer
 4
 Road Frontage in Feet


 DEPTH
 Integer
 4
 Property Depth in Feet


 GISACRES
 Double
 8
 Total Computed Acres from GIS


 TAXINGDIST
 String
 3
 Taxing District of Property


 TAXDISTDES
 String
 60
 Taxing District Description


 FIREDIST
 String
 3
 Fire District of Property


 FIREDISTDE
 String
 60
 Fire District Description


 ZONING
 String
 10
 Zoning of Property


 ZONINGAREA
 String
 3
 Governing Area for Zoning


 LANDUSECOD
 SmallInteger
 2
 Land Use Code


 LANDUSEDES
 String
 60
 Land Use Description


 LANDISON
 String
 5
 BAY,CANAL,CREEK,GULF,LAKE,RIVER & GOLF


 SITEADDR
 String
 55
 Lee County Addressing/E911


 SITENUMBER
 String
 10
 Property Location - Street Number


 SITESTREET
 String
 40
 Street Name


 SITEUNIT
 String
 5
 Unit Number


 SITECITY
 String
 20
 City


 SITEZIP
 String
 5
 Zip Code


 JUST
 Double
 8
 Market Value


 ASSESSED
 Double
 8
 Building Value + Land Value


 TAXABLE
 Double
 8
 Taxable Value


 LAND
 Double
 8
 Land Value


 BUILDING
 Double
 8
 Building Value


 LXFV
 Double
 8
 Land Extra Feature Value


 BXFV
 Double
 8
 Building Extra Feature value


 NEWBUILT
 Double
 8
 New Construction Value


 AGAMOUNT
 Double
 8
 Agriculture Exemption Value


 DISAMOUNT
 Double
 8
 Disability Exemption Value


 HISTAMOUNT
 Double
 8
 Historical Exemption Value


 HSTDAMOUNT
 Double
 8
 Homestead Exemption Value


 SNRAMOUNT
 Double
 8
 Senior Exemption Value


 WHLYAMOUNT
 Double
 8
 Wholly Exemption Value


 WIDAMOUNT
 Double
 8
 Widow Exemption Value


 WIDRAMOUNT
 Double
 8
 Widower Exemption Value


 BLDGCOUNT
 SmallInteger
 2
 Total Number of Buildings on Parcel


 MINBUILTY
 SmallInteger
 2
 Oldest Building Built


 MAXBUILTY
 SmallInteger
 2
 Newest Building Built


 TOTALAREA
 Double
 8
 Total Building Area


 HEATEDAREA
 Double
 8
 Total Heated Area


 MAXSTORIES
 Double
 8
 Tallest Building on Parcel


 BEDROOMS
 Integer
 4
 Total Number of Bedrooms


 BATHROOMS
 Double
 8
 Total Number of Bathrooms / Not For Comm


 GARAGE
 String
 1
 Garage on Property 'Y'


 CARPORT
 String
 1
 Carport on Property 'Y'


 POOL
 String
 1
 Pool on Property 'Y'


 BOATDOCK
 String
 1
 Boat Dock on Property 'Y'


 SEAWALL
 String
 1
 Sea Wall on Property 'Y'


 NBLDGCOUNT
 SmallInteger
 2
 Total Number of New Buildings on ParcelTotal Number of New Buildings on Parcel


 NMINBUILTY
 SmallInteger
 2
 Oldest New Building Built


 NMAXBUILTY
 SmallInteger
 2
 Newest New Building Built


 NTOTALAREA
 Double
 8
 Total New Building Area


 NHEATEDARE
 Double
 8
 Total New Heated Area


 NMAXSTORIE
 Double
 8
 Tallest New Building on Parcel


 NBEDROOMS
 Integer
 4
 Total Number of New Bedrooms


 NBATHROOMS
 Double
 8
 Total Number of New Bathrooms/Not For Comm


 NGARAGE
 String
 1
 New Garage on Property 'Y'


 NCARPORT
 String
 1
 New Carport on Property 'Y'


 NPOOL
 String
 1
 New Pool on Property 'Y'


 NBOATDOCK
 String
 1
 New Boat Dock on Property 'Y'


 NSEAWALL
 String
 1
 New Sea Wall on Property 'Y'


 O_NAME
 String
 30
 Owner Name


 O_OTHERS
 String
 120
 Other Owners


 O_CAREOF
 String
 30
 In Care Of Line


 O_ADDR1
 String
 30
 Owner Mailing Address Line 1


 O_ADDR2
 String
 30
 Owner Mailing Address Line 2


 O_CITY
 String
 30
 Owner Mailing City


 O_STATE
 String
 2
 Owner Mailing State


 O_ZIP
 String
 9
 Owner Mailing Zip


 O_COUNTRY
 String
 30
 Owner Mailing Country


 S_1DATE
 Date
 8
 Most Current Sale Date > $100.00


 S_1AMOUNT
 Double
 8
 Sale Amount


 S_1VI
 String
 1
 Sale Vacant or Improved


 S_1TC
 String
 2
 Sale Transaction Code


 S_1TOC
 String
 2
 Sale Transaction Override Code


 S_1OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_2DATE
 Date
 8
 Previous Sale Date > $100.00


 S_2AMOUNT
 Double
 8
 Sale Amount


 S_2VI
 String
 1
 Sale Vacant or Improved


 S_2TC
 String
 2
 Sale Transaction Code


 S_2TOC
 String
 2
 Sale Transaction Override Code


 S_2OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_3DATE
 Date
 8
 Next Previous Sale Date > $100.00


 S_3AMOUNT
 Double
 8
 Sale Amount


 S_3VI
 String
 1
 Sale Vacant or Improved


 S_3TC
 String
 2
 Sale Transaction Code


 S_3TOC
 String
 2
 Sale Transaction Override Code


 S_3OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_4DATE
 Date
 8
 Next Previous Sale Date > $100.00


 S_4AMOUNT
 Double
 8
 Sale Amount


 S_4VI
 String
 1
 Sale Vacant or Improved


 S_4TC
 String
 2
 Sale Transaction Code


 S_4TOC
 String
 2
 Sale Transaction Override Code


 S_4OR_NUM
 String
 13

This specialized geospatial dataset offers detailed insights into heliport locations across North America. Emergency services, aviation companies, and urban development agencies can leverage these precise center points and boundary information to enhance operational efficiency. The comprehensive data supports critical applications like emergency response routing, infrastructure planning, and aviation safety assessments. By providing exact geographical coordinates and spatial extents, Xtract.io empowers organizations to make data-driven decisions in helicopter transportation and emergency services.

How Do We Create Polygons? -All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct detailed indoor polygons. This meticulous process ensures higher accuracy and consistency. -We verify our polygons through multiple quality checks, focusing on accuracy, relevance, and completeness.

What's More? -Custom Polygon Creation: Our team can build polygons for any location or category based on your specific requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.

Unlock the Power of POI and Geospatial Data With our robust polygon datasets and point-of-interest data, you can: -Perform detailed market analyses to identify growth opportunities. -Pinpoint the ideal location for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute targeted, location-driven marketing campaigns for better ROI. -Gain an edge over competitors by leveraging geofencing and spatial intelligence.

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Access APIGeocoded Addressing Theme Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 …Show full description Access APIGeocoded Addressing Theme Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionally.The Geocoded Urban and Rural Addressing System (GURAS) is a ‘property’ based address database. Each property polygon captured within GURAS has a unique numeric identifier and contains at least one authoritative address which is sourced from local councils via the valuation of land database, also managed by LPI-Valnet. Properties may contain more than one address sourced from various other organisations. The GURAS database is commonly used by all levels of government for emergency services, computer aided dispatch systems, postal and delivery services, and to identify location. Address points are generally system generated points and do not always have a direct correlation to the dwelling location. In circumstances where there are multiple disparate lots for one property, particularly in rural addresses, the system generated address points may not reside within the correct property polygon. Owners names are not part of the GURAS database, nor does GURAS contain any personal information. The Geocoded Addressing Theme is a single source of truth for address information in NSW, GURAS eliminates the costly duplication of effort where all local councils, Australia Post, emergency service organisations and other agencies and businesses maintained individual address databases with different creation and distribution regimes.Geocoded Addressing Data Theme includes the following feature classes:Waypoint - A WayPoint is a point located on the RoadSegment feature class for an address where the road naming attributes from both the AddressString and the RoadSegment classes are identical. Indicates the approximate entry point of for an address.Address Point - A point feature class used to spatially locate an address / address stringThe Address Point Layer includes the below subtypes:· Building· Homestead· Monument· Property· Unit/Strata· OtherPro Way - A Proway is a line that spatially connects the AddressPoint and WayPoint.The Pro Way Layer includes the following subtypes:· Right· Left· Other Metadata Type Esri Feature Service Update Frequency As required Contact Details Contact us via the Spatial Services Customer Hub Relationship to Themes and Datasets NSW Geocoded Addressing Theme of the Foundation Spatial Data Framework (FSDF) Accuracy The dataset maintains a positional relationship to, and alignment with, the Lot and Property digital datasets. This dataset was captured by digitising the best available cadastral mapping at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program of positional upgrade (accuracy improvement) is currently underway. Spatial Reference System (dataset) Geocentric Datum of Australia 1994 (GDA94), Australian Height Datum (AHD) Spatial Reference System    (web service) EPSG 4326: WGS 84 Geographic 2D WGS 84 Equivalent To GDA94 Spatial Extent Full State Standards and Specifications Open Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement. Information about the “Feature Class” and “Domain Name” descriptions for the NSW Administrative Boundaries Theme can be found in the GURAS Delivery Model Data DictionarySome of Spatial Services Datasets are designed to work together for example “NSW Address Point” and “NSW Address String Table”, NSW Property (Polygon) and NSW Property Lot Table and NSW Lot (polygons). To do this you need to add a “Spatial Join”. A Spatial join is a GIS operation that affixes data from one feature layer’s attribute table to another from a spatial perspective. To see how Address, Property and Lot Geometry data and Tables can be joined together download the Data Model Document. This will show what attributes in the datasets can be linked. Distributors Service Delivery, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795 Dataset Producers and Contributors Administrative Spatial Programs, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795

This dataset is one of several segments of a regional high detailed stream flowpath dataset. The data was separated using the TOPO 50 map series extents.The stream network was originally created for the purpose of high detailed work along rivers and streams in the Wellington region. It was started as a pilot study for the Mangatarere subcatchment of the Waiohine River for the Environmental Sciences department who was attempting to measure riparian vegetation. The data was sourced from a modelled stream network created using the 2013 LiDAR digital elevation model. Once the Mangatarere was complete the process was expanded to cover the entire region on an as needed basis for each whaitua. This dataset is one of several that shows the finished stream datasets for the Wairarapa region.The base stream network was created using a mixture of tools found in ArcGIS Spatial Analyst under Hydrology along with processes located in the Arc Hydro downloadable add-on for ArcGIS. The initial workflow for the data was based on the information derived from the help files provided at the Esri ArcGIS 10.1 online help files. The updated process uses the core Spatial Analyst tools to generate the streamlines while digital dams are corrected using the DEM Reconditioning tool provided by the Arc Hydro toolset. The whaitua were too large for processing separated into smaller units according to the subcatchments within it. In select cases like the Taueru subcatchment of the Ruamahanga these subcatchments need to be further defined to allow processing. The catchment boundaries available are not as precise as the LiDAR information which causes overland flows that are on edges of the catchments to become disjointed from each other and required manual correction.Attributes were added to the stream network using the River Environment Classification (REC) stream network from NIWA. The Spatial Join tool in Arcmap was used to add the Reach ID to each segment of the generated flow path. This ID was used to join a table which had been created by intersecting stream names (generated from a point feature class available from LINZ) with the REC subcatchment dataset. Both of the REC datasets are available from NIWA's website.

This intersection points feature class represents current intersections in the City of Los Angeles. Few intersection points, named pseudo nodes, are used to split the street centerline at a point that is not a true intersection at the ground level. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way. The right of way information is available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Intersection layer was created in geographical information systems (GIS) software to display intersection points. Intersection points are placed where street line features join or cross each other and where freeway off- and on-ramp line features join street line features. The intersection points layer is a feature class in the LACityCenterlineData.gdb Geodatabase dataset. The layer consists of spatial data as a point feature class and attribute data for the features. The intersection points relates to the intersection attribute table, which contains data describing the limits of the street segment, by the CL_NODE_ID field. The layer shows the location of the intersection points on map products and web mapping applications, and the Department of Transportation, LADOT, uses the intersection points in their GIS system. The intersection attributes are used in the Intersection search function on BOE's web mapping application NavigateLA. The intersection spatial data and related attribute data are maintained in the Intersection layer using Street Centerline Editing application. The City of Los Angeles Municipal code states, all public right-of-ways (roads, alleys, etc) are streets, thus all of them have intersections. List of Fields:Y: This field captures the georeferenced location along the vertical plane of the point in the data layer that is projected in Stateplane Coordinate System NAD83. For example, Y = in the record of a point, while the X = .CL_NODE_ID: This field value is entered as new point features are added to the edit layer, during Street Centerline application editing process. The values are assigned automatically and consecutively by the ArcGIS software first to the street centerline spatial data layer, then the intersections point spatial data layer, and then the intersections point attribute data during the creation of new intersection points. Each intersection identification number is a unique value. The value relates to the street centerline layer attributes, to the INT_ID_FROM and INT_ID_TO fields. One or more street centerline features intersect the intersection point feature. For example, if a street centerline segment ends at a cul-de-sac, then the point feature intersects only one street centerline segment.X: This field captures the georeferenced location along the horizontal plane of the point in the data layer that is projected in Stateplane Coordinate System NAD83. For example, X = in the record of a point, while the Y = .ASSETID: User-defined feature autonumber.USER_ID: The name of the user carrying out the edits.SHAPE: Feature geometry.LST_MODF_DT: Last modification date of the polygon feature.LAT: This field captures the Latitude in deciaml degrees units of the point in the data layer that is projected in Geographic Coordinate System GCS_North_American_1983.OBJECTID: Internal feature number.CRTN_DT: Creation date of the polygon feature.TYPE: This field captures a value for intersection point features that are psuedo nodes or outside of the City. A pseudo node, or point, does not signify a true intersection of two or more different street centerline features. The point is there to split the line feature into two segments. A pseudo node may be needed if for example, the Bureau of Street Services (BSS) has assigned different SECT_ID values for those segments. Values: • S - Feature is a pseudo node and not a true intersection. • null - Feature is an intersection point. • O - Intersection point is outside of the City of LA boundary.LON: This field captures the Longitude in deciaml degrees units of the point in the data layer that is projected in Geographic Coordinate System GCS_North_American_1983.

Access APIAdministrative Boundaries Theme - Federal Electoral Division Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new …Show full description Access APIAdministrative Boundaries Theme - Federal Electoral Division Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionally.NSW Federal Electoral Division is a feature class which represents a gazetted area of a federal electoral division that has been defined by redistribution. NSW Federal Electoral Division is a feature class within the Administrative boundaries theme. It represents a gazetted area of a federal electoral division that has been defined by redistribution. Australian Electoral Commission is responsible for this dataset. Any changes that occur to the dataset should have a reference in the authority of reference feature class in the Administrative boundaries. Features are typically positioned in alignment within the extents of the cadastral polygons and NSW Lot and Property data changes impact this dataset. This dataset is current as per last redistribution. Metadata Type Esri Feature Service Update Frequency As required Contact Details Contact us via the Spatial Services Customer Hub Relationship to Themes and Datasets Administrative Boundaries Theme of the Foundation Spatial Data Framework (FSDF) Accuracy The dataset maintains a positional relationship to, and alignment with, the Lot and Property digital datasets. This dataset was captured by digitising the best available cadastral mapping at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program of positional upgrade (accuracy improvement) is currently underway. Spatial Reference System (dataset) Geocentric Datum of Australia 1994 (GDA94), Australian Height Datum (AHD) Spatial Reference System    (web service) EPSG 4326: WGS 84 Geographic 2D WGS 84 Equivalent To GDA94 Spatial Extent Full State Standards and Specifications Open Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement. Information about the Feature Class and Domain Name descriptions for the NSW Administrative Boundaries Theme can be found in the NSW Cadastral Delivery Model Data Dictionary Some of Spatial Services Datasets are designed to work together for example NSW Address Point and NSW Address String (table), NSW Property (Polygon) and NSW Property Lot (table) and NSW Lot (polygons). To do this you need to add a Spatial Join. A Spatial Join is a GIS operation that affixes data from one feature layer’s attribute table to another from a spatial perspective. To see how NSW Address, Property, Lot Geometry data and tables can be spatially joined, download the Data Model Document. Distributors Service Delivery, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795 Dataset Producers and Contributors Administrative Spatial Programs, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795

This dataset is one of several segments of a regional high detailed stream flowpath dataset. The data was separated using the TOPO 50 map series extents.The stream network was originally created for the purpose of high detailed work along rivers and streams in the Wellington region. It was started as a pilot study for the Mangatarere subcatchment of the Waiohine River for the Environmental Sciences department who was attempting to measure riparian vegetation. The data was sourced from a modelled stream network created using the 2013 LiDAR digital elevation model. Once the Mangatarere was complete the process was expanded to cover the entire region on an as needed basis for each whaitua. This dataset is one of several that shows the finished stream datasets for the Wairarapa region.The base stream network was created using a mixture of tools found in ArcGIS Spatial Analyst under Hydrology along with processes located in the Arc Hydro downloadable add-on for ArcGIS. The initial workflow for the data was based on the information derived from the help files provided at the Esri ArcGIS 10.1 online help files. The updated process uses the core Spatial Analyst tools to generate the streamlines while digital dams are corrected using the DEM Reconditioning tool provided by the Arc Hydro toolset. The whaitua were too large for processing separated into smaller units according to the subcatchments within it. In select cases like the Taueru subcatchment of the Ruamahanga these subcatchments need to be further defined to allow processing. The catchment boundaries available are not as precise as the LiDAR information which causes overland flows that are on edges of the catchments to become disjointed from each other and required manual correction.Attributes were added to the stream network using the River Environment Classification (REC) stream network from NIWA. The Spatial Join tool in Arcmap was used to add the Reach ID to each segment of the generated flow path. This ID was used to join a table which had been created by intersecting stream names (generated from a point feature class available from LINZ) with the REC subcatchment dataset. Both of the REC datasets are available from NIWA's website.

This map shows the range of urbanized area related to the naturally occurring resources of Chester County. Using this data, we can view which area within the county have remained non-urbanized, and where the natural resources are still located, in order to more generally understand what and where the resources have been used. The Create Buffers and Spatial Join features were used to create this map.

This is a collaboration between City of Los Angeles Mayor's Office, StreetsLA, and USC. To consolidate / aggregate many datasets for Street Sweeping. Task 2: to perform spatial join between Centerlines and Biweekly Posted Routes.

To assess site resilience, we divided the coast into 1,232 individual sites centered around each tidal marsh or complex of tidal habitats. For each site, we estimated the amount of migration space available under four sea-level rise scenarios and we identified the amount of buffer area surrounding the whole tidal complex. We then examined the physical properties and condition characteristics of the site and its features using newly developed analyses as well as previously published and peer-reviewed datasets.Sites vary widely in the amount and suitability of migration space they provide. This is determined by the physical structure of the site and the intactness of processes that facilitate migration. A marsh hemmed in by rocky cliffs will eventually convert to open water, whereas a marsh bordered by low lying wetlands with ample migration space and a sufficient sediment supply will have the option of moving inland. As existing tidal marshes degrade or disappear, the amount of available high-quality migration space becomes an indicator of a site’s potential to support estuarine habitats in the future. The size and shape of a site’s migration space is dependent on the elevation, slope, and substrate of the adjacent land. The condition of the migration space also varies substantially among sites. For some tidal complexes, the migration space contains roads, houses, and other forms of hardened structures that resist conversion to tidal habitats, while the migration space of other complexes consists of intact and connected freshwater wetlands that could convert to tidal habitats.Our aim was to characterize each site’s migration space but not predict its future composition. Towards this end, we measured characteristics of the migration space related to its size, shape, volume, and condition, and we evaluated the options available to the tidal complex to rearrange and adjust to sea level rise. In the future, the area will likely support some combination of salt marsh, brackish marsh and tidal flat, but predictions concerning the abundance and spatial arrangement of the migration space’s future habitats are notoriously difficult to make because nature’s transitions are often non-linear and facilitated by pulses of disturbance and internal competition. For instance, in response to a 1.4 mm increase in the rate of SLR, the landward migration of low marsh cordgrass in some New York marshes appears to be displacing high marsh (Donnelly & Bertness 2001). Thus, our assumption was simply that a tidal complex with a large amount of high quality and heterogeneous migration space will have more options for adaptation, and will be more resilient, than a tidal complex with a small amount of degraded and homogenous migration space.To delineate migration space for the full project area, we requested the latest SLR Viewer (Marcy et al. 2011) marsh migration data, with no accretion rate, for all the NOAA geographic units within the project area, from NOAA (N. Herold, pers. comm., 2018). Specifically, we obtained data for the following states in the project area: Virginia, North Carolina, South Carolina, Georgia, and Florida. As accretion is very location-dependent, we chose not to use one of the three SLR Viewer accretion rates because they were flat rates applied across each geographic unit. For each geography, we combined four SLR scenarios (1.5’, 3’, 4’, and 6.5’) with the baseline scenario to identify pixels that changed from baseline. We only selected cells that transitioned to tidal habitats (unconsolidated shoreline, salt marsh, and transitional / brackish marsh) and not to open water or upland habitat. We combined the results from each of the geographies and projected to NAD83 Albers. The resultant migration space was then resampled to a 30-m grid and snapped to the NOAA 2010 C-CAP land cover grid (NOAA, 2017). The tidal complex grid and the migration space grid were combined to ensure that there were no overlapping pixels. While developed areas were not allowed to be future marsh in NOAA’s SLR Viewer marsh migration model, we still removed all roads and development, as represented in the original 30-m NOAA 2010 C-CAP land cover grid, from the migration space. We took this step as differences in spatial resolution between the underlying elevation and land cover datasets could occasionally result in small amounts of development in our resampled migration space. The remaining migration space was then spatially grouped into contiguous regions using an eight-neighbor rule that defined connected cells as those immediately to the right, left, above, or diagonal to each other. The region-grouped grid was converted to a polygon, and the SLR scenario represented by each migration space footprint was assigned to each polygon. Finally, the migration space scenario polygons that intersected any of the tidal complexes were selected. Because a single migration space polygon could be adjacent to and accessible to more than one tidal complex unit, each migration space polygon was linked to their respective tidal complex units with a unique ID by restructuring and aggregating the output from a one-to-many spatial join in ArcGIS. This linkage enabled the calculation of attributes for each tidal complex such as total migration space acreage, total number of migration space units, and the percent of the tidal complex perimeter that was immediately adjacent to migration space. Similar attributes were calculated for each migration space unit including total tidal complex acreage and number of tidal complex units.REFERENCESChaffee, C, Coastal policy analyst for the R.I. Coastal Resources Management Council. personal communication. April 4, 2017.Donnelly, J.P, & Bertness, M.D. 2001. Rapid shoreward encroachment of salt marsh cordgrass in response to accelerated sea-level rise. PNAS 98(25) www.pnas.org/cgi/doi/10.1073/pnas.251209298Herold, N. 2018. NOAA Sea Level Rise (SLR) Viewer marsh migration data (10-m), with no accretion rate, for all SLR scenarios from 0.5-ft. to 10.0-ft. for VA, NC, SC, GA, and FL. Personal communication Jan. 24, 2018. Lerner, J.A., Curson, D.R., Whitbeck, M., & Meyers, E.J., Blackwater 2100: A strategy for salt marsh persistence in an era of climate change. 2013. The Conservation Fund (Arlington, VA) and Audubon MD-DC (Baltimore, MD).Lucey, K. NH Coastal Program. Personal Communication. April 4, 2017.Maine Natural Areas Program. 2016. Coastal Resiliency Datasets, Schlawin, J and Puryear, K., project leads. http://www.maine.gov/dacf/mnap/assistance/coastal_resiliency.htmlMarcy, D., Herold, N., Waters, K., Brooks, W., Hadley, B., Pendleton, M., Schmid, K., Sutherland, M., Dragonov, K., McCombs, J., Ryan, S. 2011. New Mapping Tool and Techniques For Visualizing Sea Level Rise And Coastal Flooding Impacts. National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center. Originally published in the Proceedings of the 2011 Solutions to Coastal Disasters Conference, American Society of Civil Engineers (ASCE), and reprinted with permission of ASCE(https://coast.noaa.gov/slr/).National Oceanic and Atmospheric Administration (NOAA), Office for Coastal Management. “VA_2010_CCAP_LAND_COVER,” “NC_2010_CCAP_LAND_COVER,” “SC_2010_CCAP_LAND_COVER,” “GA_2010_CCAP_LAND_COVER,” “FL_2010_CCAP_LAND_COVER”. Coastal Change Analysis Program (C-CAP) Regional Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed September 2017 at www.coast.noaa.gov/ccapftp.Schuerch, M.; Spencer, T.; Temmerman, S.; Kirwan, M L.; Wolff, C.; Linck, D.; McOwen, C.J.; Pickering, M.D.; Reef, R.; Vafeidis, A.T.; Hinkel J.; Nicholls, R.J.; and Sally Brown. 2018. Future response of global coastal wetlands to sea-level rise. Nature 561: 231-234.