About this itemBack in 2017, I made a Cascade story map to compile GIS career resources for my current and future interns. Fast forward seven years, and I finally rebuilt it as an ArcGIS StoryMap. From job title descriptions to certifications and to salaries, it covers the main areas I find emerging professionals asking about when they're looking at a career in GIS. There are multiple shout outs to the Consortium in it too, of course.😎Author/ContributorJohn NergeOrganizationPersonal workOrg Websitehttps://bit.ly/JohnNerge

Communication and Image - To increase internal and external engagement by implementing best practices in communications, marketing, and public relations by 5%.

View the diversity of challenges and opportunities across America's counties within different types of rural regions and communities. Get statistics on people, jobs, and agriculture.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Data file GIS API Services Interactive map Zip of CSV files For complete information, please visit https://data.gov.

These data contain selected census tract level demographic indicators (estimates) from the 2015-2019 American Community Survey representing the percent of the civilian labor force population (Age 16+) that is unemployed.

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 files Contact: Madalaine McConville Contact Phone: 480-350-2927 Data Source Type: Excel files Preparation Method: Extracted from GPEC monthly and annual reports and proprietary Excel files Publish Frequency: Annually Publish Method: Manual Data Dictionary

Videos and additional details assembled by Strivven, supported by Esri.

I’d love to begin by saying that I have not “arrived” as I believe I am still on a journey of self-discovery. I have heard people say that they find my journey quite interesting and I hope my story inspires someone out there.I had my first encounter with Geographic Information System (GIS) in the third year of my undergraduate study in Geography at the University of Ibadan, Oyo State Nigeria. I was opportune to be introduced to the essentials of GIS by one of the prominent Environmental and Urban Geographers in person of Dr O.J Taiwo. Even though the whole syllabus and teaching sounded abstract to me due to the little exposure to a practical hands-on approach to GIS software, I developed a keen interest in the theoretical learning and I ended up scoring 70% in my final course exam.

The organization is seeking a Geographic Information System (GIS) expert for a one-year renewable contract. The position involves providing specialized expertise in GIS technologies and applications. This recruitment aims to enhance spatial data management and analysis capabilities.

Targeted Employment Areas. The dataset contains locations and attributes of Targeted Employment Area, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies.

Dataset featuring the full-time, part-time and seasonal jobs, as well as internships posted on the City's job portal @ https://www.raleighnc.gov/jobs This dataset is updated weekdays by 9am and does not contain past (non-active) postings.

This map shows areas where population and jobs growth will be concentrated in the District through the year 2045.

This dataset represents all future planned employment areas within the region.This dataset was compiled for the Edmonton Metropolitan Region Growth Plan which came into effect on October 26, 2017.

Last Updated: N/A

This layer shows employment data in Tucson by neighborhood, aggregated from block level data for 2019. For questions, contact GIS_IT@tucsonaz.gov. The data shown is from Esri's 2019 Updated Demographic estimates.Esri's U.S. Updated Demographic (2019/2024) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2019/2024 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Addy PopeHigher Education Manager - Esri UKStill think I am a glaciologistGIS consultant GIS EducationDidn't actually do any GIS as an undergrad.

This dataset includes average commute time from the 2010-2014 American Community Survey. It also includes totals and densities for both Primary Jobs and Resident Workers from 2012 - 2014, It identifies totals and percentages of jobs that are held by residents of Detroit, Highland Park, or Hamtramck (DHPH), and totals and percentages of resident workers who work in DHPH. All data have been assembled at the census tract level.Metadata available for download here.

Dataset description: This dataset contains the information needed to replicate the results presented in the article “Optimizing recruitment in an online environmental PPGIS—is it worth the time and costs?”. The data were collected as part of a study investigating recruitment strategies for a large-scale online public participation GIS (PPGIS) platform in coastal areas of northern Norway. To investigate different recruitment strategies, we reviewed previous environmental PPGIS studies using random sampling and methods to increase response rates. We compared the attained results with our large-scale PPGIS in northern Norway, where we used both random and volunteer (traditional and social media) sampling. The dataset includes response rates for the 5% of the population (13 regions in northern Norway) recruited by mail to participate in an online PPGIS survey, response rates from volunteers recruited through traditional and social media, synthetic demographic data, and the code necessary for processing demographic data to obtain the results presented in the article. Original demographic data is not shared due to privacy legislation. We furthermore calculated time spent and costs used for recruiting both randomly sampled persons and volunteers. Article abstract: Public participation GIS surveys use both random and volunteer sampling to recruit people to participate in a self-administered mapping exercise online. In random sampling designs, the participation rate is known to be relatively low and biased to specific segments (e.g., middle-aged, educated men). Volunteer sampling provides the opportunity to reach a large crowd at reasonable costs but generally suffers from unknown sampling biases and lower data quality. The low participation rates and the quality of mapping question the validity and generalizability of the results, limiting their use as a democratic tool for enhancing participation in spatial planning. We therefore asked: How can we increase participation in online environmental PPGIS surveys? Is it worth the time and costs? We reviewed environmentally related online PPGIS surveys (n=26) and analyzed the sampling biases and recruitment strategies utilized in a large-scale online PPGIS platform in coastal areas of northern Norway via both random (16978 invited participants) and volunteer sampling. We found that the time, effort, and costs required to increase participation rates yielded meager results. We discuss the time and cost efficiency of different recruitment methods and the implications of participation levels despite the recruitment methods used.

Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas.

These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2023-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2019 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process.

Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.

As these projections may be a valuable input to other analyses, this dataset is made available here as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes.

Wasatch Front Real Estate Market Model (REMM) Projections

WFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:

Demographic data from the decennial census
County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature
Current employment locational patterns derived from the Utah Department of Workforce Services
Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff
Current land use and valuation GIS-based parcel data stewarded by County Assessors
Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations
Calibration of model variables to balance the fit of current conditions and dynamics at the county and regional level

‘Traffic Analysis Zone’ Projections

The annual projections are forecasted for each of the Wasatch Front’s 3,546 Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres).

‘City Area’ Projections

The TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.

Summary Variables in the Datasets

Annual projection counts are available for the following variables (please read Key Exclusions note below):

Demographics

Household Population Count (excludes persons living in group quarters) 
Household Count (excludes group quarters) 

Employment

Typical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)
Retail Job Count (retail, food service, hotels, etc)
Office Job Count (office, health care, government, education, etc)
Industrial Job Count (manufacturing, wholesale, transport, etc)
Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count 
All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).

• These variables includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.

Key Exclusions from TAZ and ‘City Area’ Projections

As the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

Statewide Projections

Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.

Employment and wages data for census designated places (CDPs) & cities, census areas & boroughs, and economic regions in Alaska. Includes historic data from 2001 to present.This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Local and Regional Information

This depository contains two data sets:1. Collected and analysed field data related to herbivore browsing, and2. The 50 x 50 km fishnet (GIS data) as applied in:Per Angelstam P., Manton M., Pedersen S. and M. Elbakidze 2017. Disrupted trophic interactions affect recruitment of boreal deciduous and coniferous trees in northern Europe. Ecological Applications xxPlease note, other data used in this publication can be sourced from the original data sources (see cited literature for more information).

Source: Snapshot visualization of the estimated average number of jobs at the census block level, disaggregated from LODES data.

Purpose: Tile layer utilized for visualization.

Contact Information: Charles Rudder (crudder@citiesthatwork.com)/ Alex Bell (abell@citiesthatwork.com)