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.

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

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 excel contains data for Chapter 2 “Precipitation” of the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org). It includes the raw data behind Figure 1, “Annual precipitation at Providence, RI,” (page 64); Figure 2, “Annual precipitation at Worcester, MA,” (page 64); Figure 3, “Annual Palmer Drought Severity Index (PDSI) for Rhode Island,” (page 65); Figure 4, "Annual Palmer Drought Severity Index (PDSI) for Massachusetts," (page 65); Figure 5, "Climate model projection of winter total precipitation in RI or MA to 2100," (page 67); and Figure 6, "Climate model projection of winter annual snowfall in RI or MA to 2100," (page 67). 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/.

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

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.

This dataset contains information about the employee travel expenses for the year 2023. 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.

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

Title SNAP Households by Household Types and Demographics 2016-2020 ACS - SNAP_HH_2020

Summary SNAP Households by type and demographics from 2016-2020 5-year period in NM Census tracts

Notes

Source US CENSUS TABLE FOOD STAMPS/SUPPLEMENTAL NUTRITION ASSISTANCE PROGRAM (SNAP) S2201 2020 ACS 5-YEAR ESTIMATE

Prepared by EMcRae_NMCDC

Feature Service https://nmcdc.maps.arcgis.com/home/item.html?id=8c3e62b5050f4bcc8853ecf0130f976d

Alias Definition

ID id

GeoName Geographic Area Name

ETH_1 Estimate Total Households

ETH_2 Estimate Total Households With one or more people in the household 60 years and over

ETH_3 Estimate Total Households No people in the household 60 years and over

ETH_4 Estimate Total Households Married-couple family

ETH_5 Estimate Total Households Other family:

ETH_6 Estimate Total Households Other family: Male householder, no spouse present

ETH_7 Estimate Total Households Other family: Female householder, no spouse present

ETH_8 Estimate Total Households Nonfamily households

ETH_9 Estimate Total Households With children under 18 years

ETH_10 Estimate Total Households With children under 18 years Married-couple family

ETH_11 Estimate Total Households With children under 18 years Other family:

ETH_12 Estimate Total Households With children under 18 years Other family: Male householder, no spouse present

ETH_13 Estimate Total Households With children under 18 years Other family: Female householder, no spouse present

ETH_14 Estimate Total Households With children under 18 years Nonfamily households

ETH_15 Estimate Total Households No children under 18 years

ETH_16 Estimate Total Households No children under 18 years Married-couple family

ETH_17 Estimate Total Households No children under 18 years Other family:

ETH_18 Estimate Total Households No children under 18 years Other family: Male householder, no spouse present

ETH_19 Estimate Total Households No children under 18 years Other family: Female householder, no spouse present

ETH_20 Estimate Total Households No children under 18 years Nonfamily households

ETH_POV_1 Estimate Total Households POVERTY STATUS IN THE PAST 12 MONTHS Below poverty level

ETH_POV_2 Estimate Total Households POVERTY STATUS IN THE PAST 12 MONTHS At or above poverty level

ETH_DIS_1 Estimate Total Households DISABILITY STATUS With one or more people with a disability

ETH_DIS_2 Estimate Total Households DISABILITY STATUS With no persons with a disability

ETH_RHO_1 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone

ETH_RHO_2 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Black or African American alone

ETH_RHO_3 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER American Indian and Alaska Native alone

ETH_RHO_4 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Asian alone

ETH_RHO_5 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Native Hawaiian and Other Pacific Islander alone

ETH_RHO_6 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Some other race alone

ETH_RHO_7 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Two or more races

ETH_RHO_8 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Hispanic or Latino origin (of any race)

ETH_RHO_9 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone, not Hispanic or Latino

ETH_INC_1 Estimate Total Households HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2020 INFLATION-ADJUSTED DOLLARS) Median income (dollars)

ETH_WS_1 Estimate Total WORK STATUS Families

ETH_WS_2 Estimate Total WORK STATUS Families No workers in past 12 months

ETH_WS_3 Estimate Total WORK STATUS Families 1 worker in past 12 months

ETH_WS_4 Estimate Total WORK STATUS Families 2 or more workers in past 12 months

EPH_2 Estimate Percent Households With one or more people in the household 60 years and over

EPH_3 Estimate Percent Households No people in the household 60 years and over

EPH_4 Estimate Percent Households Married-couple family

EPH_5 Estimate Percent Households Other family:

EPH_6 Estimate Percent Households Other family: Male householder, no spouse present

EPH_7 Estimate Percent Households Other family: Female householder, no spouse present

EPH_8 Estimate Percent Households Nonfamily households

EPH_9 Estimate Percent Households With children under 18 years

EPH_10 Estimate Percent Households With children under 18 years Married-couple family

EPH_11 Estimate Percent Households With children under 18 years Other family:

EPH_12 Estimate Percent Households With children under 18 years Other family: Male householder, no spouse present

EPH_13 Estimate Percent Households With children under 18 years Other family: Female householder, no spouse present

EPH_14 Estimate Percent Households With children under 18 years Nonfamily households

EPH_15 Estimate Percent Households No children under 18 years

EPH_16 Estimate Percent Households No children under 18 years Married-couple family

EPH_17 Estimate Percent Households No children under 18 years Other family:

EPH_18 Estimate Percent Households No children under 18 years Other family: Male householder, no spouse present

EPH_19 Estimate Percent Households No children under 18 years Other family: Female householder, no spouse present

EPH_20 Estimate Percent Households No children under 18 years Nonfamily households

EPH_POV_1 Estimate Percent Households POVERTY STATUS IN THE PAST 12 MONTHS Below poverty level

EPH_POV_2 Estimate Percent Households POVERTY STATUS IN THE PAST 12 MONTHS At or above poverty level

EPH_DIS_1 Estimate Percent Households DISABILITY STATUS With one or more people with a disability

EPH_DIS_2 Estimate Percent Households DISABILITY STATUS With no persons with a disability

EPH_RHO_1 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone

EPH_RHO_2 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Black or African American alone

EPH_RHO_3 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER American Indian and Alaska Native alone

EPH_RHO_4 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Asian alone

EPH_RHO_5 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Native Hawaiian and Other Pacific Islander alone

EPH_RHO_6 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Some other race alone

EPH_RHO_7 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Two or more races

EPH_RHO_8 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Hispanic or Latino origin (of any race)

EPH_RHO_9 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone, not Hispanic or Latino

EPH_WS_2 Estimate Percent WORK STATUS Families No workers in past 12 months

EPH_WS_3 Estimate Percent WORK STATUS Families 1 worker in past 12 months

EPH_WS_4 Estimate Percent WORK STATUS Families 2 or more workers in past 12 months

SNAP_1 Estimate Households receiving food stamps/SNAP Households

SNAP_2 Estimate Households receiving food stamps/SNAP Households With one or more people in the household 60 years and over

SNAP_3 Estimate Households receiving food stamps/SNAP Households No people in the household 60 years and over

SNAP_4 Estimate Households receiving food stamps/SNAP Households Married-couple family

SNAP_5 Estimate Households receiving food stamps/SNAP Households Other family:

SNAP_6 Estimate Households receiving food stamps/SNAP Households Other family: Male householder, no spouse present

SNAP_7 Estimate Households receiving food stamps/SNAP Households Other family: Female householder, no spouse present

SNAP_8 Estimate Households receiving food stamps/SNAP Households Nonfamily households

SNAP_9 Estimate Households receiving food stamps/SNAP Households With children under 18 years

SNAP_10 Estimate Households receiving food stamps/SNAP Households With children under 18 years Married-couple family

SNAP_11 Estimate Households receiving food stamps/SNAP Households With children under 18 years Other family:

SNAP_12 Estimate Households receiving food stamps/SNAP Households With children under 18 years Other family: Male householder, no spouse present

SNAP_13 Estimate Households receiving food stamps/SNAP Households With children under 18 years Other family: Female householder, no spouse present

SNAP_14 Estimate Households receiving food stamps/SNAP Households With children under 18 years Nonfamily households

SNAP_15 Estimate Households receiving food stamps/SNAP Households No children under 18 years

SNAP_16 Estimate Households receiving food stamps/SNAP Households No children under 18 years Married-couple family

SNAP_17 Estimate Households receiving food stamps/SNAP Households No children under 18 years Other family:

SNAP_18 Estimate Households

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

The purpose of the Indian Child Welfare Act (ICWA) is "...to protect the best interest of Indian Children and to promote the stability and security of Indian tribes and families by the establishment of minimum Federal standards for the removal of Indian children and placement of such children in homes which will reflect the unique values of Indian culture... "(25 U.S. C. 1902). ICWA provides guidance to States regarding the handling of child abuse and neglect and adoption cases involving Native children and sets minimum standards for the handling of these cases. The regulations implementing the Indian Child Welfare Act provide that Indian Tribes may designate an agent other than the Tribal chairman for service of notice of proceedings under the Act. This directory includes the current list of designated Tribal agents for service of notice: https://www.bia.gov/bia/ois/dhs/icwa.

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

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 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

This excel contains the 2020 update of the results from the 2017 State of Narragansett Bay and Its Watershed Technical Report (nbep.org), Chapter 13: "Benthic Habitat." Sites in Narragansett Bay were monitored across 1988, 2008, and 2018 for benthic habitat type and changes in benthic habitat quality via a sediment profile imaging survey. The results from this survey were published by Shumchenia and King (2019).