This video demonstrates how school board administrators map and analyze student achievement using ArcGIS Maps for Office and ArcGIS Online. Specifically, it covers how to prepare and map student data from Microsoft Excel, how to enrich that data with the geoenrichment service in ArcGIS Online and how to share, communicate and present your work in Microsoft PowerPoint and in Story Map applications.

Spreadsheets and graphs are powerful tools that make data come alive and tell a story. Now, use maps to see the story from another perspective. ArcGIS Maps for Office enables Microsoft Excel and PowerPoint users worldwide to ask location-related questions of data, get powerful insights, and make the best decisions. You can:Map your spreadsheet data – whether you want to see customer locations, ZIP code aggregations, custom sales territories and more – you can see it all.Add geographic context to your spreadsheet data and communicate these insights via interactive maps in PowerPoint.Gain insight into demographic, spending, behavior, and landscape information, among many more.Use the authoritative content on the ArcGIS platform to supplement your location data and add context to the locations in your spreadsheet.Securely share your maps with colleagues and stakeholders.Bring the power of the ArcGIS platform into your spreadsheets and presentations. To use ArcGIS Maps for Office you need an ArcGIS Online paid or trial subscription or a Portal for ArcGIS Named User License and Microsoft Office 2010, 2013, or 2016. Visit the online documentation for information on how to use this app.

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

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.

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 dataset lists the employee name and taxable benefit for personal use of City of Greater Sudbury Vehicle as travel expenses for the year 2023. 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.

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

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

Download Employee Travel Excel SheetThis dataset contains information about the employee travel expenses for the year 2021. Details are provided on the employee (name, title, department), the travel (dates, location, purpose) and the cost (expenses, recoveries). Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Updated quarterly when expenses are prepared. Expenses for other years are available in separate datasets.

This dataset contains information about the employee travel expenses for the year 2018. Details are provided on the employee (name, title, department), the travel (dates, location, purpose) and the cost (expenses, recoveries). Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Updated quarterly when expenses are prepared. Expenses for other years are available in separate datasets.

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.

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/.

Abstract

This dataset was derived by the Bioregional Assessment Programme. The parent datasets are identified in the Lineage statement in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset comprises of interpreted elevation surfaces and contours for the major Triassic and Upper Permian units of the Galilee Geological Basin.

Purpose

This dataset was created to provide formation extents for aquifers in the Galilee geological basin

Dataset History

A Quality Assurance (QA) and validation process was conducted on the original well and bore data to choose wells/bores that are within 25 kilometres of the BA Galilee Region extent.

The QA/Validation process is as follows:

1. Well data

   a. Obtained excel file "QPED_July_2013_galilee.xlsx" from GA

   b. Based on stratigraphic information in "BH_costrat" tab formation names were regularised and simplified based on current naming conventions.

   c. Simplified names added to QPED_July_2013_galileet.xlsx as "Steve_geo" and "Steve_group"

   d. Produced new file "GSQ_Geology.xlsx" contained decimal latitude and longitude, KB elevation, top of unit in metres from KB, top of unit in metres AHD, bottom of unit in metres from KB, bottom of unit in metres AHD, original geology, simplified geology, simplified Group geology.

    i.     KB obtained from "BH_wellhist"
   
    ii.    Where no KB information was available ie KB=0, sample the 1S DEM at the well's location to obtain height. KB=DEM+10. Marked well as having lower reliability.
   
    iii.    Calculated Top_m_AHD = KB - Top_m_KB
   
    iv.    Calculated Bottom_m_AHD = KB - Bottom_m_KB
   

   e. Brought GSQ_Geology.xlsx into ArcGIS

   f. Selected wells based on "Steve_geo" field for each model layer to produce a geodatabase for each layer.

    i.     GSQ_basement_wells
   
    ii.    GSQ_top_joe_joe_group
   
    iii.    GSQ_top_bandanna_merge
   
    iv.    GSQ_rewan_group
   
    v.     GSQ_clematis
   
    vi.    GSQ_moolyember
   

   g. Additional wells and reinterpreted tops added to appropriate geodatabase based on well completion reports

   h. Additional wells added to coverages to help model building process

    i.     Well_name listed as Fake
   
    ii.    Exception being GSQ_top_basement_fake which was created as a separate geodatabase
   

2. Bore data

   a. Obtained QLD_DNRM_GroundwaterDatabaseExtract_20131111 from GA

   b. Used files REGISTRATIONS.txt, ELEVATIONS.txt and AQUIFER.txt to build GW_stratigraphy.xlsx

    i.     Based on RN
   
    ii.    Latitude from GIS_LAT (REGISTRATIONS.txt)
   
    iii.    Longitude from GIS_LNG (REGISTRATIONS.txt)
   
    iv.    Elevation from (ELEVATIONS.txt)
   
    v.     FORM_DESC from (AQUIFER.txt)
   
    vi.    Top from (AQUIFER.txt)
   
    vii.    Bottom from (AQUIFER.txt)
   

   c. Brought GW_stratigraphy.xlsx into ArcGIS

   d. Created gw_bores_galilee_dem

    i.     Sampled 1S DEM to obtain ground level elevation column RASTERVALU
   
    ii.    Created column top_m_AHD by RASTERVALU - Top
   

   e. Selected bores based on "FORM_DESC" field for each model layer to produce a geodatabase for each layer.

    i.     Gw_basement
   
    ii.    GW_bores_joe_joe_group
   
    iii.    GW_bores_bandanna
   
    iv.    Gw_bores_rewan
   
    v.     Gw_bores_clematis
   
    vi.    Gw_bores_moolyember
   

3. Georectified seismic surfaces

   a. Extracted interpreted seismic surfaces for base Permian (interpreted as basement) and top Bandanna (in time) from the following seismic surveys

    i.     Y80A, W81A, Carmichael, Pendine, T81A, Quilpie, Ward and Powell Creek seismic survey downloaded https://qdexguest.deedi.qld.gov.au/portal/site/qdex/search?searchType=general 
   
    ii.    Brought TIF images into ArcGIS and georectified
   
    iii.    Digitised shape of contours and faults into geodatabase
   
        1.   Basement_contours and basement_faults
   
        2.   bandanna_contours_new_data and bandanna_faults
   
    iv.    Added field "contour" to geodatabase
   
    v.     Converted contours to depth in "contour" field based on well and bore data (top_m_AHD) and contour progression
   
    vi.    Use the shape and depth derived from OZ SEEBASE to help to add additional contours and faults to basement and bandanna datasets
   

4. Additional contour and fault surfaces were built derived from underlying surfaces and wells/bore data

   a. Joejoe_contours and joejoe)faults

   b. Rewan_contour_clip (used bandanna_faults as fault coverage)

   c. Clematis_contour and clematis_faults

   d. Moolyember_contour (used clematis_faults as fault coverage)

5. Surface geology

   a. Extracted surface geology from QUEENSLAND GEOLOGY_AUGUST_2012 using Galilee BA region boundary with 25 kilometre boundary to form geodatabase QLD_geology_galilee

   b. Selected relevant surface geology from QLD_geology_galilee based on field "Name" for each model layer and created new geodatabase layers

    i.     Basement_geology: Argentine Metamorphics,Running River Metamorphics,Charters Towers Metamorphics; Bimurra Volcanics, Foyle Volcanics, Mount Wyatt Formation, Saint Anns Formation, Silver Hills Volcanics, Stones Creek Volcanics; Bulliwallah Formation, Ducabrook Formation, Mount Rankin Formation, Natal Formation, Star of Hope Formation; Cape River Metamorphics; Einasleigh Metamorphics; Gem Park Granite; Macrossan Province Cambrian-Ordovician intrusives; Macrossan Province Ordovician-Silurian intrusives; Macrossan Province Ordovician intrusives; Mount Formartine, unnamed plutonic units; Pama Province Silurian-Devonian intrusives; Seventy Mile Range Group; and Kirk River beds, Les Jumelles beds.
   
    ii.    Joe_joe_geology: Joe Joe Group
   
    iii.    Galilee_permian_geology: Back Creek Group, Betts Creek Group, Blackwater Group
   
    iv.    Rewan_geology: Rewan Group
   
       1.    Later also made dunda_beds_geology to be included in Rewan model: Dunda beds
   
    v.     Clematis_geology: Clematis Group
   
       1.    Later also made warang_sandstone_geology to be included in Clematis model: Warang Sandstone
   
    vi.    Moolyember_surface_geology: Moolyember Formation
   

6. DEM for each model layer

   a. Using surface geology geodatabase extent extract grid from dem_s_1s to represent the top of the model layer at the surface

    i.     Basement_dem
   
    ii.    Joejoe_dem
   
    iii.    Bandanna_dem
   
    iv.    Rewan_dem and dunda_dem
   
    v.     Clematis_dem and warang_dem
   
    vi.    Moolyember_surface_dem
   

   b. Used Contour tool in ArcGIS to obtain a 25 metre contour geodatabase from the relevant model DEM

    i.     Basement_dem_contours
   
    ii.    Joejoe_dem_contours
   
    iii.    Bandanna_dem_contours
   
    iv.    Rewan_dem_contours and dunda_dem_contours
   
    v.     Clematis_dem_contours and warang_dem_contours
   
    vi.    Moolyember_dem_contours
   

   c. For the purpose of guiding the model building process additional fields were added to each DEM contour geodatabase was added based on average thickness derived from groundwater bores and petroleum wells.

    i.     Basement_dem_contours: Joejoe, bandanna, rewan, clematis, moolyember
   
    ii.    Joejoe_dem_contours: basement, bandanna
   
    iii.    Bandanna_dem_contours: joejoe, rewan
   
    iv.    Rewan_dem_contours and dunda_dem_contours: clematis, rewan
   
    v.     Clematis_dem_contours and warang_dem_contours: moolyember, rewan
   
    vi.  Moolyember_dem_contours: clematis
   

The model building process is as follows:

1. Used the tope to raster tool to create surface based on the following rules

   a. Environment

       i.  Extent
   
          1. Top: -19.7012030024424
   
          2. Right: 148.891511819054
   
          3. Bottom: -27.5812030024424
   
          4. Left: 139.141511819054
   
       ii. Output cell size: 0.01 degrees
   
       iii. Drainage enforcement: No_enforce
   

   b. Input

       i.  Basement
   
          1. Basement_dem_contour; field - contour; type - contour
   
          2. Joejoe_dem_contour; field - basement; type - contour
   
          3. Basement_contour; field - contour; type - contour
   
          4. GSQ_basement_wells; field - top_m_AHD; type - point elevation
   
          5. GW_basement; field - top_m_AHDl type - point elevation
   
          6. GSQ_top_basement_fake; field - top_m_AHDl type - point elevation
   
          7. Basement_faults; type - cliff
   
      ii.  Joe Joe Group
   
          1. Joejoe_dem_contour; field - basement; type - contour
   
          2. Basement_dem_contour; field - joejoe; type - contour
   
          3. permian_dem_contour; field - joejoe, type - contour
   
          4. joejoe_contour; field - joejoe; type - contour
   
          5. GSQ_top_joejoe_group; field - top_m_AHD; type - point elevation
   
          6. GW_bores_joe_joe_group; field - top_m_AHDl type - point elevation
   
          7. joejoe_faults; type - cliff
   
      iii.  Bandanna Group
   
          1. Permian_dem_contour; field - contour; type - contour
   
          2. Joejoe_dem_contour; field - bandanna; type - contour
   
          3. Rewan_dem_contour: field - bandanna; type - contour
   
          4. Dunda_dem_contour; field - bandanna; type - contour
   

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

Census block groups for Rhode Island, Massachusetts, and Connecticut as defined by the 2020 U.S. Census. Census data has been supplemented with town names (RIGIS 2001; MassGIS 2021; CTDEEP 2005), watersheds (USGS WBD 2023), and NBEP study areas (NBEP 2017). This dataset is intended for general planning, graphic display, and GIS analysis.

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 locates. 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 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 InformationSource:Contact: 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

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

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 page provides data for the Facilities Conditions Index performance measure. Regular assessments of the condition of city facilities is important. An outcome of the assessments is the Facilities Condition Index (FCI). This index rates facilities based on current condition. The FCI indicates the ratio of assets repair costs to the replacement value of the entire building. The lower the FCI ratio, the better the condition of the building.This dataset provides the current FCI value for each city owned facility. The FCI is generated quarterly for inpidual facilities and then calculated for the City overall.The performance measure dashboard is available at 4.14 Facilities Conditions Index.Additional InformationSource:Contact: Dana JanofskyContact E-Mail: dana_janofsky@tempe.govData Source Type: FacilitizePreparation Method: Reports are generated from Facilitize and exported as Excel spreadsheetsPublish Frequency: AnnualPublish Method: ManualData Dictionary

This dataset comes from the Biennial City of Tempe Employee Survey questions related to employee engagement. Survey respondents are asked to rate their level of agreement on a scale of 5 to 1, where 5 means "Strongly Agree" and 1 means "Strongly Disagree".This dataset includes responses to the following statements:Overall, I am satisfied with the level of employee engagement in my Department I have been mentored at work. Overall, how satisfied are you with your current job? Participation in the survey is voluntary and confidential.This page provides data for the Employee Engagement performance measure. The performance measure dashboard is available at 2.13 Employee Engagement.Additional Information Source: Community Attitude SurveyContact:  Wydale Holmes Contact E-Mail:  wydale_holmes@tempe.govData Source Type:  ExcelPreparation Method:  Data received from vendor (Community Survey)Publish Frequency:  AnnualPublish Method:  ManualData Dictionary