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

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for geographic information systems gis in the U.S.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for gis in the U.S.

The County of Suffolk Annual Salaries File for the Year 2018 is a yearly summary of Payroll Data for all Suffolk County employees. This file contains the Employee Names and Hired Date along with their most recent Job Title and Department. In addition, the file contains the Employee’s Regular Pay Rate (Hourly, Biweekly or Annual Salary), the Year to Date Regular Earnings, Longevity Pay, Overtime Pay, and Other Payments (comprised of Holiday Pay, Night Differential Pay, Cleaning and Clothing Allowances, Taxable Legal Benefit Amounts, etc.). If an employee has been terminated or has separated from County employment, the Separation Payment Amount (if applicable), and Termination Date is also included.

Additional information about the Dataset Attributes are listed below. Please feel free to contact us if you have any questions about this dataset.

Year: Year of employment Last Name: Employee Last Name First Name: Employee First Name Department: Department Name Job Title: Job Title Bargaining Unit Number: Bargaining Unit Bargaining Unit Name: Bargaining Unit Name Salary: Regular Salary Earnings for the Year Longevity Pay: Longevity Pay Overtime Pay: Overtime Pay Separation Pay: Separation Payment of Sick, Vacation and Personal Time Accruals Other Pay: Special Payments - Holiday Pay, Night Differential, Cleaning Clothing Tool Allowance, Legal Benefit Total Earnings: Total Earnings for the Year Separation Date: Date of Termination/Separation from Suffolk County

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

This project explores the integration of Geographic Information Systems (GIS) and Natural Language Processing (NLP) to improve job–candidate matching in recruitment. Traditional AI-based e-recruitment systems often ignore geographic constraints. Our hybrid model addresses this gap by incorporating both textual similarity and spatial relevance in matching candidates to job postings.Data UsedCandidate Data (CVs)Source: Scraped from emploi.maSize: 1000 CVs after cleaningContent: Candidate names (anonymized), skills, experiences, locations (coordinates), availability, etc.Job DescriptionsSource: Publicly available dataset from KaggleSize: we took 1000 job postings using category :MoroccoContent: Titles, descriptions, required skills, sector labels, and office locations...All datasets have been cleaned and anonymized for privacy and research ethics compliance.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for forest engineering (gis) in the U.S.

My name is Heather Bell, I am 30 years old and I am a GIS analyst and the Coastal Data Lead at The Rivers Trust.

The Long Island Sound Study developed these digital data from 1:100,000-scale National Oceanic & Atmospheric Administration (NOAA) and United States Geological Survey (USGS) maps as a general reference to the depth of water in Long Island Sound. In 1996, these data were digitized from paper maps by the Long Island Sound Study (http://www.longislandsoundstudy.net) and incorporated into a Long Island Sound GIS database. Not intended for maps printed at map scales greater or more detailed than 1:100,000 scale (1 inch = 1,578 feet.) Dataset credit: Applied Geographics, Inc. of Boston, Massachussets was contracted by the Long Island Sound Study to automate and digitize these bathymetry data for Long Island Sound. Linda Bischoff, GIS Analyst, digitized the data and created the orginal metadata.

The U.S. Census's 2010 LEHD Origin-Destination Employment Statistcs (LODES) Dataset was used to map job and worker density in throughout the Twin Cities Metropolitan Area, Minnesota. The LODES data is part of the U.S. Census's Longitudinal Employer-Household Dynamics program which records the number of jobs by workplace location and the number of workers by household location at the census block level. LEHD data is derived from data provided by the Minnesota Department of Employment and Economic Development's (MNDEED) Quarterly Census of Employment and Wages (QCEW) and the U.S. Social Security Administration.

The U.S. Cenus Bureau protects the confidentiality of the original data by using a system of multiplicative noise infusion, whereby all released data are "fuzzed." Although the positional accuracy of the data is not as good as the original MNDEED QCEW data, a more robust dataset is produced that allows allows users to not only map a general representation of job density (see LEHD Job Density), but also map jobs by income level (LEHD Low-Wage Job Density) and workers' residence (see LEHD Worker Household Density or LEHD Low-Wage Worker Household Density).

Jobs and workers are classified into three earning categories: <= $1,250/month, $1,251 to $3,333/month, and > $3,333/month. The census block level data was converted to a smoothly tapered surface of calculated census block value. The resulting data surface provides a general representation of density of low-wage jobs ($3,333/month or less: approx. $40,000 or less annually) in the Twin Cities Metropolitan Area, Minnesota.

This layer (hosted feature layer) depicts the street sign support locations in the City of Canton, GA. This data set is maintained by the City of Canton's GIS division.For specific questions about this data or to provide feedback, please contact the City's GIS division: Alaina Ellis GIS Analyst alaina.ellis@cantonga.gov (770) 546-6780 Canton City Hall 110 Academy Street, Canton, GA 30114

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for applied geospatial science in the U.S.

Off-road ATV usage is widespread in many regions of the Izembek Refuge, especially adjacent to official roads north and west of Cold Bay. GIS analysts manually digitized ATV tracks in this region using aerial imagery collected in May 2022 and processed by Mark Laker (Alaska Refuge Remote Sensing Coordinator) during that summer. This aerial imagery has a ground-resolution of 10-20 cm. GIS Analyst Tristan Amaral (Alaska Refuge Natural Resources Program) created this layer and GIS Analysts Dan Quinn (Alaska Refuge Natural Resources, daniel_quinn@fws.gov) completed the project. The layer exists as a Hosted Feature Layer on ArcGIS Online.

GIS analyst Kathryn Brautigam (kathryn.brautigam@fws.gov) manually digitized ATV tracks in the layer ATV_tracks_2004_IZM using aerial imagery collected in October 2004 by R. Michael Anthony. Image resolution was predicted to be approximately 12.5 centimeters per pixel. Any remaining distortions or geospatial inaccuracies of the 2004 aerial images were dealt with individually and locations, sizes, and directions of land features were confirmed using the 2022 aerial imagery and satellite imagery so that digitized ATV tracks were drawn in the correct locations.

In Geographic Information Systems (GIS), geoprocessing workflows allow analysts to organize their methods on spatial data in complex chains. We propose a method for expressing workflows as linked data, and for semi-automatically enriching them with semantics on the level of their operations and datasets. Linked workflows can be easily published on the Web and queried for types of inputs, results, or tools. Thus, GIS analysts can reuse their workflows in a modular way, selecting, adapting, and recommending resources based on compatible semantic types. Our typing approach starts from minimal annotations of workflow operations with classes of GIS tools, and then propagates data types and implicit semantic structures through the workflow using an OWL typing scheme and SPARQL rules by backtracking over GIS operations. The method is implemented in Python and is evaluated on two real-world geoprocessing workflows, generated with Esri's ArcGIS. To illustrate the potential applications of our typing method, we formulate and execute competency questions over these workflows.

The dataset depicts land use/cover in the Cayuga Lake Watershed. This geographic dataset was constructed using a multi-resource approach. To map small scale land uses, high resolution aerial imagery available from the New York State Digital OrthoimageryProgram (NYSDOP) was interpreted by a trained GIS analyst according to a multi-tiered classification scheme similar to that used by Tompkins County, New York to map land use in 1995. To map large scale land uses and land cover types, two sets of Landsat Thematic Mapper images were analyzed. One set was analyzed by the USDA National Agricultural Statistics Service Research and Development Division, and published as the Cropland Data Layer for the state of New York. To distinguish between different forest cover types, Landsat-5 TM images were radiometrically and geographically corrected, and analyzed using a supervised classification procedure. These separate layers were overlayed to produce the final vector layer.

This layer (hosted feature layer) depicts the citizens that are water customers in the City of Canton, GA. This data set is maintained by the City of Canton's GIS division, and is updated on a regular basis to depict the current customers. For specific questions about this data or to provide feedback, please contact the City's GIS division: Alaina Ellis GIS Analyst alaina.ellis@cantonga.gov (770) 546-6780 Canton City Hall 110 Academy Street, Canton, GA 30114

This layer (hosted feature layer) depicts the water mains in the City of Canton, GA. This data set is maintained by the City of Canton's GIS division.For specific questions about this data or to provide feedback, please contact the City's GIS division: Alaina Ellis GIS Analyst alaina.ellis@cantonga.gov (770) 546-6780 Canton City Hall 110 Academy Street, Canton, GA 30114

EbolaGeonode was a partnership platform for sharing geospatial data, analysis and maps related to the Ebola emergency response. The platform was intended to minimize the time that GIS analysts spend locating up-to-date data. Users were able to make maps on the fly, view metadata, and access the reports behind GIS layers. Curators worked to ensure that the layers were recent, clean, useful, and legally and technically open.

This layer (hosted feature layer) depicts water meters in the City of Canton, GA. This data set is maintained by the City of Canton's GIS division.For specific questions about this data or to provide feedback, please contact the City's GIS division: Alaina Ellis GIS Analyst alaina.ellis@cantonga.gov (770) 546-6780 Canton City Hall 110 Academy Street, Canton, GA 30114