A collection of geo-enabled career profiles produced by Strivven Media and managed by the Esri Schools team. For more information, email schools@eseri.com

PurposeThis job aid will lead the GIS analyst through the process of manually creating an incident map journal and how to create additional pages for the journal. This process should be used at the beginning of an incident and then the journal should be maintained to assure it remains viable. The incident map journal serves as a curated center to place maps, apps, and dashboards relevant to the incident.

This job aid assumes a working knowledge of how to create maps, apps, and dashboards on ArcGIS Online. For a tutorial, go to the Create apps from maps - ArcGIS Tutorial.Example workflow for the Geo-Enabled Plans Session at InSPIRE. Job Aid developed by FEMA GIS to enable GIS analysts to rapidly spin-up a standardized incident journal.

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.

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.

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.

This data collection contains Transit 2017 block group shapefiles and accessibility data dictionary.Accessibility Observatory data reflects the number of jobs that are reachable by various modes within different travel times from different Census-defined geographies in Massachusetts (block, block group, tract). The data comes from the Accessibility Observatory at the University of Minnesota, and the underlying jobs data is sourced from the U.S. Census Bureau’s Local Employer Household Dynamics (LEHD) dataset. More information about data methodology is available here: http://access.umn.edu/publications/· The data posted on GeoDOT is initially organized by mode: Auto, Transit, Pedestrian, and Bike. With respect to Auto, Transit, and Pedestrian data, data is then organized by geography (group and block group), and then travel time threshold: 30, 45, and 60 minutes. Please note that MassDOT has access to data that reflects travel time thresholds in five minute increments, email Derek Krevat at derek.krevat@dot.state.ma.us for more information. With respect to Bike data, data is organized by geography (group and block group) and then by Level of Traffic Stress; there are four different levels that correspond to the ratings given different roadway segments with respect to the level of 'traffic stress' imposed on cyclists LTS 1: Strong separation from all except low speed, low volume traffic. Simple crossings. Suitable for children. LTS 2: Except in low speed / low volume traffic situations, cyclists have their own place to ride that keeps them from having to interact with traffic except at formal crossings. Physical separation from higher speed and multilane traffic. Crossings that are easy for an adult to negotiate. Corresponds to design criteria for Dutch bicycle route facilities. A level of traffic stress that most adults can tolerate, particularly those sometimes classified as “interested but concerned.”LTS 3: Involves interaction with moderate speed or multilane traffic, or close proximity to higher speed traffic. A level of traffic stress acceptable to those classified as “enthused and confident.”LTS 4: Involves interaction with higher speed traffic or close proximity to high speed traffic. A level of stress acceptable only to those classified as “strong and fearless.” See http://www.northeastern.edu/peter.furth/research/level-of-traffic-stress/ for more information.· Data reflecting access to jobs via Auto is available for each hour of the day at the different travel time thresholds (30, 45 and 60 minute thresholds are posted; five minute thresholds are available by contacting Derek Krevat at derek.krevat@dot.state.ma.us).o For convenience, MassDOT has also created stand-alone summary files that reflect the total number of jobs available throughout the day within 30, 45, and 60 minutes of travel time. See the Data Dictionary, Auto All Jobs for more information.· Pedestrian and Transit data is only available for the morning peak travel period, 7:00 to 9:00 am.· Bicycle data is only available for the noontime hour.· Each of the data files contains data reflecting access to all jobs as well as discrete job opportunities as categorized by the U.S. Census bureau, such as jobs in specific industries, with specific types of workers, with specific wages, or in businesses of certain sizes or ages. See the Data Dictionary for more 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).

This dataset highlights localities that currently require the lower job creation threshold (25+ new jobs) to qualify for the Major Business Facility Job Tax Credit (MBFJTC).

MBFJTC-qualified companies locating or expanding anywhere in Virginia are eligible to receive a $1,000 income tax credit for each new full-time job created over a threshold number of jobs. Companies locating in an economically distressed locality or an Enterprise Zone are required to meet a 25-job threshold; all other locations have a 50-job threshold. For this tax credit, a locality qualifies as economically distressed if its unemployment rate for the preceding year is at least 0.5 percent higher than the average statewide unemployment rate.This data is updated in May/June of each year.Note: Unemployment rates for each county are determined by the Virginia Employment Commission. Additional Resources:Virginia's Guide to Business Incentives

ARC has developed a new series of population, household and employment forecasts for the 21-county region through the year 2050. The forecasts help inform the development of the Atlanta Region’s Plan, a long-range blueprint that details the investments that will be made in the next 30 years to improve the Atlanta region’s quality of life.For more information, see https://atlantaregional.org/atlanta-region/population-employment-forecasts

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.

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Cross-border commuters from Wallonia to Luxembourg at place of residence (Arrondissements): 2013-2023 Territorial entities: Arrondissements Commuting data sources: INAMI. Calculations: OIE/IBA 2024 Geodata sources: NGI-Belgium. Harmonization: SIG-GR / GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2409&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/a852c7c4-8c13-4b77-8708-3ff0e4946e05 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Commuter_flows_to_Luxembourg_WMS/guest with layer name(s): -Commuters_WAL_LUX_2013_2023_change -Commuters_WAL_LUX_2013_2023_share

Data updated quarterly.Data Attributes and Definitions -- Department: The department the employee works in.- Department ID: The numeric identifier for the department (typically 4 digits).- Job: The name for the job assigned to the employee.- Category: Grouping of employees in similar jobs/leadership roles.- Sub Category: Secondary grouping of employees within a category.- Race/Ethnicity: The race/ethnicity category which the employee identifies with (self-identified).- Gender: Designates the employee's gender (self-identified).- Age: The chronological number (age) assigned to the employee based on date of birth.- Age Group: Grouping of employees having approximately the same age or age range.- Original Hire Date: Date upon which the employee was originally hired.- Last Hire Date: Date upon which an employee was hired; may be a rehire date.- Pay Class: Defines how the employee gets paid for hours worked based on defined rules (full-time, part-time, hourly, etc.)- Data As of: The date to which the given data applies to.

Cross-border commuters from France to Saarland at place of work (Kreise): 2013-2023 Territorial entities: Landkreise - Commuting data sources: Bundesagentur für Arbeit. Calculations: OIE/IBA 2024 Geodata sources: GeoBasis-DE / BKG. Harmonization: SIG-GR / GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2413&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/5a66182a-bb83-4e10-8fa9-834d533de02f This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Commuter_flows_France_to_Germany_WMS/guest with layer name(s): -Commuters_FR_SL_2013-2023_change -Commuters_FR_SL_2023_share

Cross-border commuters from Lorraine to Luxembourg at place of residence (arrondissements): 2013-2023 Territorial entities: arrondissements Commuting data sources: IGSS 2024. Calculations: OIE/IBA 2024 Geodata sources: IGN France. Harmonization: SIG-GR / GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2406&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/868705a6-72ac-43fa-af6d-1fe7e5f136ac This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Commuter_flows_to_Luxembourg_WMS/guest with layer name(s): -Commuters LOR LUX 2013 2023 change -Commuters_LOR_LUX_2013_2023_share

Source: Snapshot visualization of the total number of jobs accessible within a 30 minute bike ride at the MAZ level.

Purpose: Tile layer utilized for visualization.

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

Employment Protection Districts are economically viable industrial and employment-rich areas, having policies that prevent the conversion of industrial land to non-industrial uses. These are for areas in UNINCORPORATED Los Angeles County only.Please see Figure 14.1 and the the Economic Development Element of the Los Angeles County General Plan 2035 for more information. https://planning.lacounty.gov/generalplan/Source: L.A. County Dept. of Regional Planning (DRP) GIS Section; created November 5, 2015.NEED MORE FUNCTIONALITY? If you are looking for more layers or advanced tools and functionality, then try our suite of GIS Web Mapping Applications.

Annual employment and wage data from 2001 to present for boroughs and census areas from Alaska Department of Labor and Workforce Development. FieldsResidentsAge16AndOverResidentsEmployedWagesLessThan5k: Number of residents making between $0 and $4,999 per yearWages5k_10k: Number of residents making between $5,000 and $9,999 per yearWages10k_20k: Number of residents making between $10,000 and $19,999 per yearWages20k_50k: Number of residents making between $20,000 and $49,999 per yearWagesGreaterThan50k: Number of residents making more than $50,000 per yearEmployedInPrivateSector: Number of residents who are employed in the private sectorPercentInPrivateSector: Percent of residents employed in the private sector (of residents employed)EmployedInStateGovt: Number of residents who are employed in state governmentPercentInStateGovt: Percent of residents employed in state government (of residents employed)EmployedFemales: Number of female residents who are employedEmployedMales: Number of male residents who are employed

This is an ArcMap shapefile that includes the exact spatial boundaries of all 90,000 agrarian settlements in Bihar and West Bengal, India. The file includes a unique settlement code that corresponds to the settlement codes that are at the basis of the Indian census of 2011. This makes it possible to analyse a wide range of socio-economic conditions across all settlements. Date Submitted: 2022-03-09

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

The PERM Sponsorship Trends linear chart visualizes the number of PERM cases filed by Gis Surveyors from 2020 to 2023, highlighting the company’s long-term sponsorship patterns. The horizontal bar chart titled Distribution of Job Fields Receiving PERM Sponsorship further categorizes sponsored roles by job type.