Loudoun County Parcel X,Y coordinates table. Available in Latitude and Longitude decimal degrees and Virginia State Plane North.

New Parking Citations dataset here: https://data.lacity.org/Transportation/Parking-Citations/4f5p-udkv/about_data ---Archived as of September 2023--- Parking citations with latitude / longitude (XY) in US Feet coordinates according to the California State Plane Coordinate System - Zone 5 (https://www.conservation.ca.gov/cgs/rgm/state-plane-coordinate-system). For more information on Geographic vs Projected coordinate systems, read here: https://www.esri.com/arcgis-blog/products/arcgis-pro/mapping/gcs_vs_pcs/ For information on how to change map projections, read here: https://learn.arcgis.com/en/projects/make-a-web-map-without-web-mercator/

Abstract The dataset was derived by the Bioregional Assessment Programme from Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013. The source dataset ia identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Displays the original Hydstra measurement (HYDMEAS) tabular data records (as stored in the Hydstra software platform) in a GIS format for …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013. The source dataset ia identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Displays the original Hydstra measurement (HYDMEAS) tabular data records (as stored in the Hydstra software platform) in a GIS format for interpretation and analysis. Analysis completed on this dataset includes extracts to determine location and status of current monitoring bores: HYDMEAS - original tabular database file (dbf) showing groundwater levels HYDMEAS_XY_all - displays all original tabular data in GIS shapefile format HYDMEAS_unique_bores - shows one record for each unique bore station ID HYDMEAS_2008 - All HYDMEAS data from 2008 or later HYDMEAS_2008to2013_mulitple_reading - All HYDMEAS data from 2008 or later which has been monitored twice or more (in that period), produced to estimate groundwater level monitoring bores National Groundwater Information System (NGIS) data supplied as a comparison of HYDMEAS monitoring estimates. Hydstra is a water resources time series data management system developed by KISTERS Pty Ltd. Purpose Provide spatial distribution of groundwater level monitoring status and reading for New South Wales. Dataset History HYDMEAS - original tabular data HYDMEAS_XY_all - displays all original tabular data in GIS format - Displayed as XY in ArcGIS based on Lat and Long attributes and exported as a point shapefile HYDMEAS_unique_bores - shows one record for each unique bore ID - Dissolved HYDMEAS_XY_all based on STATION field HYDMEAS_2008 - All HYDMEAS data from 2008 or later - Selected based on DATE field HYDMEAS_2008to2013_mulitple_reading - All HYDMEAS data from 2008 or later which has been monitored twice or more (in that period), produced to estimate groundwater level monitoring bores - HYDMEAS_2008 dataset dissolved based on STATION and a count field added. Only bores with count of 2 or more were retained Dataset Citation Bioregional Assessment Programme (2014) GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013. Bioregional Assessment Derived Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/d414c703-aabd-43af-81e0-30aab4d9dfb1. Dataset Ancestors Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

This is the data repository for the PLOS ONE Manuscript: "Meeting radiation dosimetry capacity requirements of population-scale exposures by geostatistical sampling". This repository contains the following data:

1. "State-and-Subdivision-Boundary-Files.Edited-for-ArcMap-10.4.KML-Format.zip":

This file contains modified U.S. state and sub-division boundary files [in KML format], which can be imported into ArcMap using its KMLtoLayer function. These files have been modified to prevent sub-division naming issues that we encountered when importing boundary data into ArcMap: A) State sub-divisions with identical names are considered a single sub-division by ArcMap (corrected by adding a letter after each sub-division of the same name, i.e. CenterA, CenterB, etc), and; B) ArcMap would only identify the sub-division by its first word if sub-division name contained spaces (corrected by converting all spaces into dashes).

2. "HPAC-Plumes.Processed.zip" and "HPAC-Plumes.Unprocessed.zip":

These files contain HPAC plume coordinate (WGS1984) and dose (in cGy) values for all scenarios discussed in the manuscript. We provide "processed" and "unprocessed" HPAC plume data files. The "unprocessed" HPAC plume data is provided in its original XML format, which cannot be imported into ArcMap directly. The "processed" HPAC plumes are provided in tab-delimited X,Y,Z format (Latitude, Longitude, and Dose). We have also added a "0 cGy" contour in the "processed" plumes (surrounding the HPAC plume), as the presence of unirradiated data points adjacent to the plume was found to be crucial for accurate kriging, since these points served as boundaries for kriging.

3. "Final-Derived-Plumes.Data-Points.zip":

This file contains geostatistically-derived plume coordinate (WGS1984) and dose (in cGy) values for all scenarios discussed in the manuscript. Data is in comma-delimited format (Latitude, Longitude, and Dose). Data points consist of a set of initial coordinates generated at random locations within each Census sub-division using the ArcMap tool, ‘CreateRandomPoints_management’, and subsequent points generated by densification (the geostatistical procedure that targets and localizes an additional small cohort of irradiated individuals to mitigate uncertainty in environmental measurements). These data points were assigned radiation level values corresponding to the adjacent outer HPAC contour by a script comparing each sample with its location within the HPAC plume of the same scenario.

4. “Intermediate-Derived-Plumes.Data-Points.zip”

This archive contains coordinate data (WGS1984) and dose values (in cGy) for all intermediary steps of plume development (using our geostatistical method) for all scenarios. Like (3), the data is comma-delimited (Latitude, Longitude, and Dose), and were assigned radiation level values by a script comparing sampling locations with the location of the HPAC plume of the same scenario. Scenario replicate folders contains text files for each iteration step of the plume derivation process, including a file containing just the initial random sampling (“Iteration-1”), a file containing initial sampling and sampling locations selected by the first densification step (“Iteration-2”), a file containing initial sampling and sampling locations selected by the first and second densification steps (“Iteration-3”), and so on.

This archive also contains a Table (“Progression-of-New-Densification-Selected-Sampling-Locations-For-All-Scenarios.xslx”) which provides a categorical breakdown of how many unique densification-selected sampling locations occur within the irradiated region (i.e. overlap the HPAC plume) for each iteration of all scenario replicates. The fraction of irradiated to unirradiated sampling locations varies among each scenario and individual replicates for the same scenario. Our analysis shows that these results depend on the population densities and exact topography of the HPAC plume which is different among each scenario.

5. "Geostatistical-Sampling-Project.All-Scripts.zip"

This archive contains all programs required for this project. This includes Python scripts meant to be run within the ArcMap software environment (for random point generation and data extraction), and Perl scripts used to process HPAC and U.S. State and Sub-division boundary files, and to assign radiation values to sample locations based on a modified HPAC plume. A java program, “CompareReplicates.jar”, compares the overlapping areas between a pair of polygons that overlap one other using the ArcMap software environment, and requires access to the ArcGIS Runtime SDK (https://developers.arcgis.com/arcgis-runtime/).

Summary Rail Crossings is a spatial file maintained by the Federal Railroad Administration (FRA) for use by States and railroads. Description FRA Grade Crossings is a spatial file that originates from the National Highway-Rail Crossing, Inventory Program. The program is to provide information to Federal, State, and local governments, as well as the railroad industry for the improvements of safety at highway-rail crossing. Credits Federal Railroad Administration (FRA) Use limitations There are no access and use limitations for this item. Extent West -79.491008 East -75.178954 North 39.733500 South 38.051719 Scale Range Maximum (zoomed in) 1:5,000 Minimum (zoomed out) 1:150,000,000 ArcGIS Metadata ▼►Topics and Keywords ▼►Themes or categories of the resource  transportation * Content type  Downloadable Data Export to FGDC CSDGM XML format as Resource Description No Temporal keywords  2013 Theme keywords  Rail Theme keywords  Grade Crossing Theme keywords  Rail Crossings Citation ▼►Title rr_crossings Creation date 2013-03-15 00:00:00 Presentation formats  * digital map Citation Contacts ▼►Responsible party  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian Responsible party  Organization's name Research and Innovative Technology Administration/Bureau of Transportation Statistics Individual's name National Transportation Atlas Database (NTAD) 2013 Contact's position Geospatial Information Systems Contact's role  distributor Contact information  ▼►Phone  Voice 202-366-DATA Address  Type  Delivery point 1200 New Jersey Ave. SE City Washington Administrative area DC Postal code 20590 e-mail address answers@BTS.gov Resource Details ▼►Dataset languages  * English (UNITED STATES) Dataset character set  utf8 - 8 bit UCS Transfer Format Spatial representation type  * vector * Processing environment Microsoft Windows 7 Version 6.1 (Build 7600) ; Esri ArcGIS 10.2.0.3348 Credits Federal Railroad Administration (FRA) ArcGIS item properties  * Name USDOT_RRCROSSINGS_MD * Size 0.047 Location withheld * Access protocol Local Area Network Extents ▼►Extent  Geographic extent  Bounding rectangle  Extent type  Extent used for searching * West longitude -79.491008 * East longitude -75.178954 * North latitude 39.733500 * South latitude 38.051719 * Extent contains the resource Yes Extent in the item's coordinate system  * West longitude 611522.170675 * East longitude 1824600.445629 * South latitude 149575.449134 * North latitude 752756.624659 * Extent contains the resource Yes Resource Points of Contact ▼►Point of contact  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian Resource Maintenance ▼►Resource maintenance  Update frequency  annually Resource Constraints ▼►Constraints  Limitations of use There are no access and use limitations for this item. Spatial Reference ▼►ArcGIS coordinate system  * Type Projected * Geographic coordinate reference GCS_North_American_1983_HARN * Projection NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet * Coordinate reference details  Projected coordinate system  Well-known identifier 2893 X origin -120561100 Y origin -95444400 XY scale 36953082.294548117 Z origin -100000 Z scale 10000 M origin -100000 M scale 10000 XY tolerance 0.0032808333333333331 Z tolerance 0.001 M tolerance 0.001 High precision true Latest well-known identifier 2893 Well-known text PROJCS["NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet",GEOGCS["GCS_North_American_1983_HARN",DATUM["D_North_American_1983_HARN",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree"

This layer provides aspect values calculated dynamically using the server-side aspect function applied to a Terrain layer. The values are float, and represent the orientation of the downward sloping terrain in degrees (0 to 359.9), clockwise from north. Cells in the input raster that are flat with zero slope are assigned an aspect of -1.WARNING: Aspect is computed in the projection specified by the client software. The server resamples the data to the required projection and then computes aspect. The default projection for web applications is Mercator in which scale increases equally in x and y by latitude, so aspect computations are not affected. Using geographic coordinates will give distorted results. It is advised to check the client application projection prior to obtaining aspect values. What can you do with this layer?Use for Visualization: No. This layer provides numeric values indicating terrain characteristics, and is not generally appropriate for visual interpretation, unless the client application applies an additional color map. For visualization use the Terrain: Aspect Map.Use for Analysis: Yes. This layer provides numeric values indicating the orientation of the terrain within a raster cell, calculated based on the defined cell size.For more details such as Data Sources, Mosaic method used in this layer, please see the Terrain layer.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

PLEASE NOTE: If choosing the Download option of "Spreadsheet" the field PIN is reformatted to a number - you will need to format it as a 10 character text string with leading zeros to join this data with data from King County.King County Assessor data has been summarized to the tax parcel identification number (PIN) and City of Seattle spatial overlay data has been assigned through geographic overlay processes. This data is updated periodically and is used to support the analytical and reporting functions of the City of Seattle long-range and policy planning office.The table includes attribute data from the King County Assessor as well as spatial overlay data for various City of Seattle reporting geographies. These geographic attributes are assigned as "majority rules" by land area in cases where multiple geographies span a single tax parcel.KCA tax parcels are created by King County for property tax assessment and collection and may not match development sites as defined by the City of Seattle (single buildings may span multiple tax parcels), may be stacked on top of each other to represent undivided interest and vertical parcels, or may be made up of several sites that are not contiguous. Every effort is made to accurately summarize key tax parcel attributes to a single PIN. Attributes include parcel centroid locations in latitude/longitude and Washington State Plane X,Y. To get polygon representation of the data please see King County's open data page for parcels and join this table through the PIN field. Please be aware that the King County Assessor site address is not a postal address and may not match other address sources for the same property such as postal, utility billing, and permitting.See the detailed data dictionary for more information.

Tags

   survey, environmental behaviors, lifestyle, status, PRIZM, Baltimore Ecosystem Study, LTER, BES




   Summary


   BES Research, Applications, and Education


   Description


   XY Positions for BES telephone survey. The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM� classification, census block group, and latitude-longitude. PRIZM� classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM� classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 



   The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 


   Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey.


   This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used

This layer provides slope values calculated dynamically from the elevation data (within the current extents) using the server-side slope function applied to a Terrain layer. The values are integer and represent the angle of the downward sloping terrain (0 to 90 degrees). Note slope is a function of the pixel size of the request, so at smaller scales the slope values are smaller as pixel sizes increase. WARNING: Slope is computed in the projection specified by the client software. The server resamples the elevation data to the requested projection and pixel size and then computes slope. Slope should be requested in a projection that maintains correct scale in x and y directions for the area of interest. Using geographic coordinates will give incorrect results. For the WGS84 Mercator and WGS Web Mercator (auxiliary sphere) projections used by many web applications, a correction factor has been included to correct for latitude-dependent scale changes.What can you do with this layer?Use for Visualization: No. This image service provides numeric values indicating terrain characteristics. Due to the limited range of values, this service is not generally appropriate for visual interpretation, unless the client application applies an additional color map. For use in visualization, use the Terrain: Slope Map. Use for Analysis: Yes. This layer provides numeric values indicating the average slope angle within a raster cell, calculated based on the defined cell size. Cell size has an effect on the slope values.For more details such as Data Sources, Mosaic method used in this layer, please see the Terrain layer. This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single export image request.

This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

A collection of red-light camera violations per month.Accuracy: The City of Ottawa provides this information in good faith but provides no warranty, nor accepts any liability arising from any incorrect, incomplete or misleading information or its improper use. N/A represents a non-active camera.Update Frequency: AnnuallyAttributes:• Intersection: Street location of the Red-Light Camera• Camera Install Year• Latitude / Longitude• X / Y• Camera-Facing: Traffic direction the camera is pointing• Months: The number of violations in each month• Total Violations: The total number of violations for the year• Highest Monthly Total: The highest violation number in a monthContact: Transportation Services Department, Traffic Operations BranchStuart Edison (for questions relating to the content)Zac Brydges (for questions relating to the data)

Analysis of ‘King County Tax Parcel Centroids with select City of Seattle geographic overlays’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/360b2b98-85f4-4a30-ae63-1b047824ef61 on 13 February 2022.

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

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

The Automated Traffic Enforcement (ATE) is a division of the District Department of Transportation (DDOT) that uses photo enforcement cameras as a traffic calming measure to enforce traffic laws, and to reduce violations on DC’s streets and most intersections. ATE is currently enforcing Posted Speed Limit (Speed), Stop Sign (Failure to come to a complete STOP), Red-Light (Running Red-Light), Bus Lane, Bus Zone, School Bus Stop-Arm, and Truck Restriction routes.This version of ATE data includes devices with no coordinates and because of that, the data is in table format. Data can be mapped using Lat/Lon or X/Y MD State Plane (Meters) but there will be records with null coordinate values.

This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

This is a Locator for finding British National Grid references. It provides lookups on the British National Grid, which can be applied to all Ordnance Survey maps of Great Britain. You can use it to query by absolute coordinates or by tile. Both types of query return the centre point of the corresponding 10k grid square BNG tile. Enter grid coordinates as absolute XY: 123456, 654321 Enter tile queries as Grid squares: TL44; as sub tile: TQ1234 or; as quadrant SN1234SE

A database (NDP-068) was generated from estimates of geographically referenced carbon densities of forest vegetation in tropical Southeast Asia for 1980. A geographic information system (GIS) was used to incorporate spatial databases of climatic, edaphic, and geomorphological indices and vegetation to estimate potential (i.e., in the absence of human intervention and natural disturbance) carbon densities of forests. The resulting map was then modified to estimate actual 1980 carbon density as a function of population density and climatic zone. The database covers the following 13 countries: Bangladesh, Brunei, Cambodia (Campuchea), India, Indonesia, Laos, Malaysia, Myanmar (Burma), Nepal, the Philippines, Sri Lanka, Thailand, and Vietnam.

The data sets within this database are provided in three file formats: ARC/INFOTM exported integer grids; ASCII (American Standard Code for Information Interchange) files formatted for raster-based GIS software packages; and generic ASCII files with x, y coordinates for use with non-GIS software packages.

The database includes ten ARC/INFO exported integer grid files (five with the pixel size 3.75 km x 3.75 km and five with the pixel size 0.25 degree longitude x 0.25 degree latitude) and 27 ASCII files. The first ASCII file contains the documentation associated with this database. Twenty-four of the ASCII files were generated by means of the ARC/INFO GRIDASCII command and can be used by most raster-based GIS software packages. The 24 files can be subdivided into two groups of 12 files each.

The files contain real data values representing actual carbon and potential carbon density in Mg C/ha (1 megagram = 10^6 grams) and integer-coded values for country name, Weck's Climatic Index, ecofloristic zone, elevation, forest or non- forest designation, population density, mean annual precipitation, slope, soil texture, and vegetation classification. One set of 12 files contains these data at a spatial resolution of 3.75 km, whereas the other set of 12 files has a spatial resolution of 0.25 degree. The remaining two ASCII data files combine all of the data from the 24 ASCII data files into 2 single generic data files. The first file has a spatial resolution of 3.75 km, and the second has a resolution of 0.25 degree. Both files also provide a grid-cell identification number and the longitude and latitude of the centerpoint of each grid cell.

The 3.75-km data in this numeric data package yield an actual total carbon estimate of 42.1 Pg (1 petagram = 10^15 grams) and a potential carbon estimate of 73.6 Pg; whereas the 0.25-degree data produced an actual total carbon estimate of 41.8 Pg and a total potential carbon estimate of 73.9 Pg.

Fortran and SASTM access codes are provided to read the ASCII data files, and ARC/INFO and ARCVIEW command syntax are provided to import the ARC/INFO exported integer grid files. The data files and this documentation are available without charge on a variety of media and via the Internet from the Carbon Dioxide Information Analysis Center (CDIAC).

This layer provides slope percent rise values calculated dynamically from the elevation data (within the current extents) using the server-side slope function applied to the Terrain layer. Percent of slope is determined by dividing the amount of elevation change by the amount of horizontal distance covered (sometimes referred to as "the rise divided by the run"), and then multiplying the result by 100. The values range from 0 to essentially infinity. When the slope angle equals 45 degrees, the rise is equal to the run. Expressed as a percentage, the slope of this angle is 100 percent. As the slope approaches vertical (90 degrees), the percentage slope approaches infinity.

WARNING: Slope is computed in the projection specified by the client software. The server resamples the elevation data to the requested projection and pixel size and then computes slope. Slope should be requested in a projection that maintains correct scale in x and y directions for the area of interest. Using geographic coordinates will give incorrect results. For the WGS84 Mercator and WGS Web Mercator (auxiliary sphere) projections used by many web applications, a correction factor has been included to correct for latitude-dependent scale changes.What can you do with this layer?Use for Visualization: No. This image service provides numeric values indicating terrain characteristics. Due to the limited range of values, this service is not generally appropriate for visual interpretation, unless the client application applies an additional color map. Use for Analysis: Yes. This layer provides numeric values indicating slope percent, calculated based on the defined cell size. Cell size has an effect on the slope values.For more details such as Data Sources, Mosaic method used in this layer, please see the Terrain layer. This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single export image request.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

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

A subset of parent FIREARM DATA.csv, this file contains only intersections of seized firearms, with limited success in geocoding from Lojic ArcGIS geocoder. This file contains X and Y coordinates in Kentucky State Plane North coordinate system, in addition to converted Latitude and Longitude coordinates.Data Dictionary:INCIDENT_NUMBER - the number associated with either the incident or used as reference to store the items in our evidence rooms and can be used to connect the dataset to the other datasets UCR_CATEGORY - the UCR based highest offense associated with the incident. For more information on UCR standards please visit https://ucr.fbi.gov/ucrTYPE_OF_FIREARM - based on the Firearm type, eg “pistol, revolver” or “shotgun, pump action” this field is for general categorization of the Firearm.FIREARMS_MANUFACTURE - the group, or company who manufactured the FirearmFIREARMS_MODEL - secondary information used to identify the Firearm.FIREARMS_CALIBER - the caliber associated with the Firearm, we use federally supplied caliber codes.RECOVERY_DATE - the date the item was identified or taken into custody.RECOVERY_BLOCK_ADRESS - the location the items was identified or taken into custody.RECOVERY_ZIPCODE - the zip code associated to the recovery block location.PERSON_RECOVERED_FROM RACE - the race associated with person who identified the item or was taken into custody from. The person listed may be the person who found the item, not the person associated with the firearm or offense.PERSON_RECOVERED_FROM _SEX - the sex associated with person who identified the item or was taken into custody from. The person listed may be the person who found the item, not the person associated with the firearm or offense.PERSON_RECOVERED_FROM AGE - the age associated with person who identified the item or was taken into custody from. The person listed may be the person who found the item, not the person associated with the firearm or offense.YEAR - the year the incident happened, useful for times the data is masked.

Locational listing of collisions by year with total of pedestrian and cycling collisions listed and geographically represented on the centroid of the line segment or intersection.

Accuracy: The City of Ottawa provides this information in good faith but provides no warranty, nor accepts any liability arising from any incorrect, incomplete or misleading information or its improper use.

Update Frequency: Annually

Attributes: • X and Y coordinate format is projected in MTM Zone 9, NAD83 (CSRS)

• Year

• Location Description (RD1 @ RD2 or RD from RD 1 to RD 2)

• Count of all collisions

• Count of pedestrian collisions (included in count of all collisions)

• Count of Bicycle collisions (included in count of all collisions)

• Latitude and longitude

Contact: Transportation Data Collection & Analytics

This data set provides a means of identifying an x-y coordinate for the approximate center (centroid) of landnet units based on the corresponding standardized PLSS description (e.g., for PLSS Section this is DTRS -- Direction, Township, Range, and Section codes). This process is sometimes referred to as "protraction". The Landnet centroid shapefile includes coordinates in WTM83/91 and latitude/longitude expressed as decimal degrees or degrees, minutes and seconds.