Parcel file includes information such as current values, ownership information, situs address and abbreviated legal.Instructions for Opening in Microsoft Excel: When opening this file in Microsoft Excel, the ParcelNumber should be treated as a text field because parcel numbers can have leading zeros. Use the following procedure:1. Do not open the file directly, start Excel with an empty workbook.2. Click the "Data" tab, then click the "From Text" button in the ribbon.3. Navigate to the saved CSV file and click the Import button.4. The "Delimited" radio button should be selected so click "Next".5. Check only the "Comma" check box and click "Next".6. Click the ParcelNumber field to select it in the "Data Preview" pane then select the "Text" radio button to define that column as a text column.7. Change any other columns as desired and click the "Finish" button to import.When the Text driver is used to open a file, the format of the text file is determined by using a schema information file (schema.ini) which is included with this download to correctly identify the column types.

Laatste update: 03 februari 2025Terug naar Esri Nederland Support HubNa een update naar ArcGIS Pro 3.x komt het voor dat het gebruik van Excel bestanden en daaraan gerelateerde tools tot foutmeldingen of onverwacht gedrag kunnen leiden.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

This online repository consists if the data used for the BSc thesis/project of Ivo van Middelkoop. It consists of a ArcGIS Project file (ArcGIS Pro BSc Project 2022 Ivo van Middelkoop.aprx) and an Excel worksheet file (Excel data BSc Project 2022 Ivo van Middelkoop.xlsx). The ArcGIS Project file was used to create shapefiles through a sea-level fluctuation model to make maps about paleo coastline reconstructions. The Excel worksheet file was used to analyse the output data coming from the ArcGIS Project file. The topic of this BSc project: How did the sea-level rise following the Late Pleistocene impact the connectivity over time between Sumatra and Borneo?

This repository is openly accessible to everyone. The copyright is owned by Ivo van Middelkoop and Dr. Kenneth F. Rijsdijk

For many of us, urban areas are the first thing that comes to mind when we think of spaces that have been altered by people. But, as it turns out, these mental images aren't very representative of our overall land use. In the second chapter of our Living in the Age of Humans series, the Esri Story Maps team takes a closer look at the ways Homo sapiens have modified Earth's limited land, and what implications this use has for our future.Data:NASA Blue Marble, July 2004Esri World ImageryESA CCI-LC Land Cover (2015)CIESIN Global Croplands, v1 (2000)CIESIN Global Pastures, v1 (2000)WheatMaizeRiceSoybeansForest Loss**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**Title: The Living LandItem Type: Web Mapping Application Storymap URLSummary: A look at how humans use the Earth's limited land space.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: Copy of this original map product: https://nmcdc.maps.arcgis.com/home/item.html?id=d29065c5443f4d008e7d7e181e54b05dFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=8a16839162554968ac6d2cf0513bcefaUID: 26Data Requested: Ag CensusMethod of Acquisition: Living AtlasDate Acquired: 5/2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDING

The ArcGIS Monitor Excel report task summarizes statistics for a specified time range (eg. last 7 days). The user can edit the config.json parameters to determine what timeframe, report path, and modules (tabs) to be included. Long-running reports over 7 days are best scheduled as a task and may avoid web time-out conditions in some environments.For more information on configuring the extension, see the PDF included in the download.

This zip file contains files used for the manuscript "The Aeolian Environment of the Landing Site for the ExoMars Rosalind Franklin Rover in Oxia Planum, Mars".1.The ArcGIS Pro files used to analyze the distribution, orientation, and morphologies of periodic bedrock ridges and transverse aeolian ridges.2.Excel datasets describing the dust devil work presented.Note: These .lyrx files are not backwards compatible with Arc 10.6. The files contained in this zip file are: 1. The HiRISE images used2. The 1-sigma ellipses3. The study area grid4. The ripple and PBRs analyzed5. Excel file for the density calculations of dust devils6. Excel file for the statistics associated with the dust devil tracks

Core forest in 2008. Core forest is defined as continuous patches of forest that are at least 250 acres, and at least 25 yards from a road.In order to maintain consistency between datasets and focus on changes to land use, 2021 US Census TIGER roads was used while calculating both 2008 and 2021 core forest locations.

It is important to identify any barriers in recruitment, hiring, and employee retention practices that might discourage any segment of our population from applying for positions or continuing employment at the City of Tempe. This information will provide better awareness for outreach efforts and other strategies to attract, hire, and retain a diverse workforce.This page provides data for the Employee Vertical Diversity performance measure. The performance measure dashboard is available at 2.20 Employee Vertical Diversity. Additional InformationSource:PeopleSoft HCM, Maricopa County Labor Market Census DataContact: Lawrence LaVictoireContact E-Mail: lawrence_lavicotoire@tempe.govData Source Type: Excel, PDFPreparation Method: PeopleSoft query and PDF are moved to a pre-formatted Excel spreadsheet.Publish Frequency: Every six monthsPublish Method: ManualData Dictionary

ISO is an independent advisory organization that collects information on a community's building-code adoption and enforcement services in order to provide a ranking for insurance companies. ISO assigns a Building Code Effectiveness Classification from 1 to 10 based on the data collected. Class 1 represents exemplary commitment to building-code enforcement.Municipalities with better rankings are lower risk, and their residents' insurance rates can reflect that. The prospect of minimizing catastrophe-related damage and ultimately lowering insurance costs gives communities an incentive to enforce their building codes rigorously.This page provides data for the Insurance Services Organization (ISO) performance measure. This data includes residential and commercial building code enforcement ratings for the City of Tempe.The performance measure dashboard is available at 1.15 Insurance Services Organization (ISO) RatingAdditional InformationSource: Insurance Service Organization RatingContact: Chris ThompsonContact E-Mail: Christopher_Thompson@tempe.govData Source Type: ExcelPreparation Method: Information added to Excel spreadsheet from rating reportPublish Frequency: Every 5 YearsPublish Method: ManualData Dictionary

With this add in it is possible to create map templates from GIS files in KML format, and create choropleths with them. Providing you have access to KML format map boundary files, it is possible to create your own quick and easy choropleth maps in Excel. The KML format files can be converted from 'shape' files. Many shape files are available to download for free from the web, including from Ordnance Survey and the London Datastore. Standard mapping packages such as QGIS (free to download) and ArcGIS can convert the files to KML format. A sample of a KML file (London wards) can be downloaded from this page, so that users can easily test the tool out. Macros must be enabled for the tool to function. When creating the map using the Excel tool, the 'unique ID' should normally be the area code, the 'Name' should be the area name and then if required and there is additional data in the KML file, further 'data' fields can be added. These columns will appear below and to the right of the map. If not, data can be added later on next to the codes and names. In the add-in version of the tool the final control, 'Scale (% window)' should not normally be changed. With the default value 0.5, the height of the map is set to be half the total size of the user's Excel window. To run a choropleth, select the menu option 'Run Choropleth' to get this form. To specify the colour ramp for the choropleth, the user needs to enter the number of boxes into which the range is to be divided, and the colours for the high and low ends of the range, which is done by selecting coloured option boxes as appropriate. If wished, hit the 'Swap' button to change which colours are for the different ends of the range. Then hit the 'Choropleth' button. The default options for the colours of the ends of the choropleth colour range are saved in the add in, but different values can be selected but setting up a column range of up to twelve cells, anywhere in Excel, filled with the option colours wanted. Then use the 'Colour range' control to select this range, and hit apply, having selected high or low values as wished. The button 'Copy' sets up a sheet 'ColourRamp' in the active workbook with the default colours, which can just be extended or deleted with just a few cells, so saving the user time. The add-in was developed entirely within the Excel VBA IDE by Tim Lund. He is kindly distributing the tool for free on the Datastore but suggests that users who find the tool useful make a donation to the Shelter charity. It is not intended to keep the actively maintained, but if any users or developers would like to add more features, email the author. Acknowledgments Calculation of Excel freeform shapes from latitudes and longitudes is done using calculations from the Ordnance Survey.

Hospitals in Chicago. To view or use these files, compression software, like WinZip, and special GIS software, such as ESRI ArcGIS, is required. The .dbf file may also be opened in Excel, Access or other database programs.

This excel contains data for Chapter 1 “Temperature” of the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org). It includes the raw data behind Figure 1, “Annual average water temperature at Woods Hole, MA 1880-2015,” (page 51); Figure 2, “Annual mean air temperatures at Worcester, MA 1949-2015,” (page 54); Figure 3, “Annual mean air temperature at Warwick, RI 1895-2015,” (page 54); Figure 4, "Annual mean surface water temperatures in Narragansett Bay 1960-2010," (page 55); Figure 5, "Annual mean river/stream water temperatures in Scituate Reservoir," (page 55); Figure 6, "Annual mean river/stream water temperatures at Millville, MA," (page 56); Figure 7, "Annual mean river/stream water temperatures from 2007-2014," (page 56); and Figure 8 "Seasonal air temperature projections for RI from 1950-2100," (page 58). For more information, please reference the Technical Report or contact info@nbep.org. Original figures are available at http://nbep.org/the-state-of-our-watershed/figures/.

This dataset lists the employee name and mileage paid as travel expenses for the year 2022. Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Data for other years is available in separate datasets. Updated quarterly when expenses are prepared.

Screencast on how to export field observations with gps coordinates in Excel to a .csv file.

Summary This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Records from FMIS (Fire Management Information System) were reviewed and compared to refuge records. Polygon data in FMIS only occurs from 2012 to current and many acreage estimates did not match. This dataset includes ALL fires no matter the size. This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Data origins include: Data origins include: 1) GPS Polygon-data (Best), 2) GPS Lat/Long or UTM, 3)TRS QS, 4)TRS Point, 6)Hand digitized from topo map, 7) Circle buffer, 8)Screen digitized, 9) FMIS Lat/Long. Started compiling fire history of CMR in 2007. This has been a 10 year process.FMIS doesn't include fires polygons that are less than 10 acres. This dataset has been sent to FMIS for FMIS records to be updated with correct information. The spreadsheet contains 10-15 records without spatial information and weren't included in either feature class. Fire information from 1964 - 1980 came from records Larry Eichhorn, BLM, provided to CMR staff. Mike Granger, CMR Fire Management Officer, tracked fires on an 11x17 legal pad and all this information was brought into Excel and ArcGIS. Frequently, other information about the fires were missing which made it difficult to back track and fill in missing data. Time was spent verifiying locations that were occasionally recorded incorrectly (DMS vs DD) and converting TRS into Lat/Long and/or UTM. CMR is divided into 2 different UTM zones, zone 12 and zone 13. This occasionally caused errors in projecting. Naming conventions caused confusion. Fires are frequently names by location and there are several "Soda Creek", "Rock Creek", etc fires. Fire numbers were occasionally missing or incorrect. Fires on BLM were included if they were "Assists". Also, fires on satellite refuges and the district were also included. Acreages from GIS were compared to FMIS acres. Please see documentation in ServCat (URL) to see how these were handled.

Mapping of deicing material storage facilities in the Lake Champlain Basin was conducted during the late fall and winter of 2022-23. 126 towns were initially selected for mapping (some divisions within the GIS towns data are unincorporated “gores”). Using the list of towns, town clerk contact information was obtained from the Vermont Secretary of State’s website, which maintains a database of contact information for each town.Each town was contacted to request information about their deicing material storage locations and methods. Email and telephone scripts were developed to briefly introduce the project and ask questions about the address of any deicing material storage locations in the town, type of materials stored at each site, duration of time each site has been used, whether materials on site are covered, and the type of surface the materials are stored on, if any. Data were entered into a geospatial database application (Fulcrum). Information was gathered there and exported as ArcGIS file geodatabases and Comma Separated Values (CSV) files for use in Microsoft Excel. Data were collected for 118 towns out of the original 126 on the list (92%). Forty-three (43) towns reported that they are storing multiple materials types at their facilities. Four (4) towns have multiple sites where they store material (Dorset, Pawlet, Morristown, and Castleton). Of these, three (3) store multiple materials at one or both of their sites (Pawlet, Morristown, and Castleton). Where towns have multiple materials or locations, the record information from the overall town identifier is linked to the material stored using a unique ‘one-to-many’ identifier. Locations of deicing material facilities, as shown in the database, were based on the addresses or _location descriptions provided by town staff members and was verified only using the most recent aerial imagery (typically later than 2018 for all towns). Locations have not been field verified, nor have site conditions and infrastructure or other information provided by town staff.Dataset instructions:The dataset for Deicing Material Storage Facilities contains two layers – the ‘parent’ records titled ‘salt_storage’ and the ‘child’ records titled ‘salt_storage_record’ with attributes for each salt storage site. This represents a ‘one-to-many’ data structure. To see the attributes for each salt storage site, the user needs to Relate the data. The relationship can be accomplished in GIS software. The Relate needs to be built on the following fields:‘salt_storage’: ‘fulcrum_id’‘salt_storage_record: ‘fulcrum_parent_id’This will create a one-to-many relationship between the geographic locations and the attributes for each salt storage site.

This data tracks four-year graduation rates from high schools located within the City of Tempe, with data publicly available through the Arizona Department of Education.Values of “8888” are used when there are too few to count, and values of “9999” are used where there is no data available. This page provides data for the High School Graduation Rate performance measure. The performance measure dashboard is available at 3.08 High School Graduation Rates. Additional Information Source: Contact: Marie RaymondContact E-Mail: Marie_Raymond@tempe.govContact Phone: 480-585-7818Data Source: Tempe High School DistrictData Source Type: Excel Preparation Method: Arizona Department of Education (ADE) generated Excel Spreadsheets- available at https://www.azed.gov/accountability-research/data/Publish Frequency: AnnuallyPublish Method: ManualData Dictionary

City of Austin Open Data Terms of Use https://data.austintexas.gov/stories/s/ranj-cccq The City of Austin’s Community Tree Priority Map (formerly Planting Prioritization) serves as a decision support tool to determine where to focus forestry activities in Austin, Texas. This map shows U.S. Census tracts (2010) containing tabular data related to community forestry priorities determined by the Community Tree Preservation Division’s Urban Forest Program. Prioritization is determined through the priority score. This score combines nine measures normalized and summarized into four broad categories. The score is aggregated at the neighborhood (U.S. Census tract) level. Scores can range from 0 to 100 with higher scores meaning a higher need for community forestry activities to achieve more equitable canopy distribution. Finally, the priority level provides a categorical representation of the data for a simplified view. Priority Score = ( Σ Natural Environment + Σ Social Vulnerability + Σ Community Investment + Σ Health & Well-Being ) / 4 This map was updated in 2020. Minor updates are made as-needed with a review and data update scheduled for 2025 (every 5 years). Ultimately, this map is used to aggregate Urban Forest Grant/Portal projects and tree planting/distribution data to assess program performance. This dataset is intended to be downloaded as a GIS Shapefile but may also be viewed in Excel. It's also available in ArcGIS Online at https://austin.maps.arcgis.com/home/item.html?id=7d7c5260e60c4f8ab811d2c5fda6c40f

Tempe is among Arizona's most educated cities, lending to a creative, smart atmosphere. With more than a dozen colleges, trade schools, and universities, about 40 percent of our residents over the age of 25 have Bachelor's degrees or higher. Having such an educated and accessible workforce is a driving factor in attracting and growing jobs for residents in the region.The City of Tempe is a member of the Greater Phoenix Economic Council (GPEC), and with the membership, staff tracks collaborative efforts to recruit business prospects and locations. The Greater Phoenix Economic Council (GPEC) is a performance-driven, public-private partnership. GPEC partners with the City of Tempe, Maricopa County, 22 other communities, and more than 170 private-sector investors to promote the region’s competitive position and attract quality jobs that enable strategic economic growth and provide increased tax revenue for Tempe. This dataset provides the target and actual job creation numbers for the City of Tempe and the Greater Phoenix Economic Council (GPEC). The job creation target for Tempe is calculated by multiplying GPEC's target by twice Tempe's proportion of the population. This page provides data for the New Jobs Created performance measure.The performance measure dashboard is available at 5.02 New Jobs Created. Additional Information Source: Extracted from GPEC monthly and annual reports and proprietary excel filesContact: Madalaine McConvilleContact Phone: 480-350-2927Data Source Type: Excel filesPreparation Method: Extracted from GPEC monthly and annual reports and proprietary Excel filesPublish Frequency: AnnuallyPublish Method: ManualData Dictionary