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.

Yearly citation counts for the publication titled "Learning Geospatial Analysis Skills with Consumer‐Grade GPS Receivers and Low Cost Spatial Analysis Software".

This map presents the full data available on the MLTSD GeoHub, and maps several of the key variables reflected by the Employment Services Program of ETD.Employment Services are a suite of services delivered to the public to help Ontarians find sustainable employment. The services are delivered by third-party service providers at service delivery sites (SDS) across Ontario on behalf of the Ministry of Labour, Training and Skills Development (MLTSD). The services are tailored to meet the individual needs of each client and can be provided one-on-one or in a group format. Employment Services fall into two broad categories: unassisted and assisted services.

Unassisted services include the following components:resources and information on all aspects of employment including detailed facts on the local labour marketresources on how to conduct a job search.assistance in registering for additional schoolinghelp with career planningreference to other Employment and government programs.

Unassisted services are available to all Ontarians without reference to eligibility criteria. These unassisted services can be delivered through structured orientation or information sessions (on or off site), e-learning sessions, or one-to-one sessions up to two days in duration. Employers can also use unassisted services to access information on post-employment opportunities and supports available for recruitment and workplace training.

The second category is assisted services, and it includes the following components:assistance with the job search (including individualized assistance in career goal setting, skills assessment, and interview preparation) job matching, placement and incentives (which match client skills and interested with employment opportunities, and include placement into employment, on-the-job training opportunities, and incentives to employers to hire ES clients), and job training/retention (which supports longer-term attachment to or advancement in the labour market or completion of training)For every assisted services client a service plan is maintained by the service provider, which gives details on the types of assisted services the client has accessed. To be eligible for assisted services, clients must be unemployed (defined as working less than twenty hours a week) and not participating in full-time education or training. Clients are also assessed on a number of suitability indicators covering economic, social and other barriers to employment, and service providers are to prioritize serving those clients with multiple suitability indicators.

About This Dataset

This dataset contains data on ES clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). This includes all assisted services clients whose service plan was closed in the 2015/16 fiscal year and all unassisted services clients who accessed unassisted services in the 2015/16 fiscal year. These clients have been distributed across Local Board areas based on the address of each client’s service delivery site, not the client’s home address. Note that clients who had multiple service plans close in the 2015/16 fiscal year (i.e. more than one distinct period during which the client was accessing assisted services) will be counted multiple times in this dataset (once for each closed service plan). Assisted services clients who also accessed unassisted services either before or after accessing assisted services would also be included in the count of unassisted clients (in addition to their assisted services data).

Demographic data on ES assisted services clients, including a client’s suitability indicators and barriers to employment, are collected by the service provider when a client registers for ES (i.e. at intake). Outcomes data on ES assisted services clients is collected through surveys at exit (i.e. when the client has completed accessing ES services and the client’s service plan is closed) and at three, six, and twelve months after exit. As demographic and outcomes data is only collected for assisted services clients, all fields in this dataset contain data only on assisted services clients except for the ‘Number of Clients – Unassisted R&I Clients’ field.

Note that ES is the gateway for other Employment Ontario programs and services; the majority of Second Career (SC) clients, some apprentices, and some Literacy and Basic Skills (LBS) clients have also accessed ES. It is standard procedure for SC, LBS and apprenticeship client and outcome data to be entered as ES data if the program is part of ES service plan. However, for this dataset, SC client and outcomes data has been separated from ES, which as a result lowers the client and outcome counts for ES.

About Local Boards

Local Boards are independent not-for-profit corporations sponsored by the Ministry of Labour, Training and Skills Development to improve the condition of the labour market in their specified region. These organizations are led by business and labour representatives, and include representation from constituencies including educators, trainers, women, Francophones, persons with disabilities, visible minorities, youth, Indigenous community members, and others. For the 2015/16 fiscal year there were twenty-six Local Boards, which collectively covered all of the province of Ontario.

The primary role of Local Boards is to help improve the conditions of their local labour market by:engaging communities in a locally-driven process to identify and respond to the key trends, opportunities and priorities that prevail in their local labour markets;facilitating a local planning process where community organizations and institutions agree to initiate and/or implement joint actions to address local labour market issues of common interest; creating opportunities for partnership development activities and projects that respond to more complex and/or pressing local labour market challenges; and organizing events and undertaking activities that promote the importance of education, training and skills upgrading to youth, parents, employers, employed and unemployed workers, and the public in general.

In December 2015, the government of Ontario launched an eighteen-month Local Employment Planning Council pilot program, which established LEPCs in eight regions in the province formerly covered by Local Boards. LEPCs expand on the activities of existing Local Boards, leveraging additional resources and a stronger, more integrated approach to local planning and workforce development to fund community-based projects that support innovative approaches to local labour market issues, provide more accurate and detailed labour market information, and develop detailed knowledge of local service delivery beyond Employment Ontario (EO).

Eight existing Local Boards were awarded LEPC contracts that were effective as of January 1st, 2016. As such, from January 1st, 2016 to March 31st, 2016, these eight Local Boards were simultaneously Local Employment Planning Councils. The eight Local Boards awarded contracts were:Durham Workforce Authority Peel-Halton Workforce Development GroupWorkforce Development Board - Peterborough, Kawartha Lakes, Northumberland, HaliburtonOttawa Integrated Local Labour Market PlanningFar Northeast Training BoardNorth Superior Workforce Planning Board Elgin Middlesex Oxford Workforce Planning & Development BoardWorkforce Windsor-Essex

MLTSD has provided Local Boards and LEPCs with demographic and outcome data for clients of Employment Ontario (EO) programs delivered by service providers across the province on an annual basis since June 2013. This was done to assist Local Boards in understanding local labour market conditions. These datasets may be used to facilitate and inform evidence-based discussions about local service issues – gaps, overlaps and under-served populations - with EO service providers and other organizations as appropriate to the local context.

Data on the following EO programs for the 2015/16 fiscal year was made available to Local Boards and LEPCs in June 2016:Employment Services (ES)Literacy and Basic Skills (LBS) Second Career (SC) Apprenticeship

This dataset contains the 2015/16 ES data that was sent to Local Boards and LEPCs. Datasets covering past fiscal years will be released in the future.

Notes and Definitions

NAICS – The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, the United States, and Mexico against the backdrop of the North American Free Trade Agreement. It is a comprehensive system that encompasses all economic activities in a hierarchical structure. At the highest level, it divides economic activity into twenty sectors, each of which has a unique two-digit identifier. These sectors are further divided into subsectors (three-digit codes), industry groups (four-digit codes), and industries (five-digit codes). This dataset uses two-digit NAICS codes from the 2007 edition to identify the sector of the economy an Employment Services client is employed in prior to and after participation in ES.

NOC – The National Organizational Classification (NOC) is an occupational classification system developed by Statistics Canada and Human Resources and Skills Development Canada to provide a standard lexicon to describe and group occupations in Canada primarily on the basis of the work being performed in the occupation. It is a comprehensive system that encompasses all occupations in Canada in a hierarchical structure. At the highest level are ten broad occupational categories, each of which has a unique one-digit identifier. These broad occupational categories are further divided into forty major groups (two-digit codes), 140 minor groups

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Abstract: Community Engaged Learning (CEL) is a pedagogical approach that involves students, community partners, and instructors working together to analyze and address community-identified concerns through experiential learning. Implementing community-engagement in geography courses and, specifically, in GIS courses is not new. However, while students enrolled in CEL GIS courses critically reflect on social and spatial inequalities, GIS tools themselves are mostly applied in uncritical ways. Yet, CEL GIS courses can specifically help students understand GIS as a socially constructed technology which can not only empower but also disempower the community. This contribution presents the experiences from a community-engaged introductory GIS course, taught at a Predominantly White Institution (PWI) in Virginia (USA) in Spring ’24. It shows how the course helped students gain a conceptual understanding of what is GIS, how to use it, and valuable software skills, while also reflecting about their own privileges, how GIS can (dis)empower the community, and their own role as a GIS analyst. Ultimately, the paper shows how the course supported positive changes in the community, equity in education, reciprocity in university/community relationships, and student civic-mindedness.

In response to the growing concern in geographic information science, which pertains to utilizing contemporary internet technology to communicate past information or knowledge for establishing foundations in geography. Recent studies have investigated geomatics solutions for historical city, and enhancing GIS skills through collaborative approach. In this study, we build upon prior research by exploring how the implementation of current technology can promote a cooperative learning environment, particularly within the realm of forestry education. Minetest, the 3D voxel game engine has high capability of modification, for visualizing natural environments and urban structures. The goal of this study was to investigate the potential of using the game engine for forestry education purposes. To meet this objective, we developed precise and detailed models of building structures and their surrounding environment. We also explored the visualization beyond 3D geospatial data, by generating analytical results of solar radiation on building roofs using GIS software. The visualization process was facilitated by the use of 3D light detection and ranging (LiDAR) data, provided by the UBC Campus + Community Planning department. The proposed approach proved to be effective in producing compatible geospatial data for visualization in the game engine. We also conducted exploratory statistical analysis to examine the relationship between building energy usage and solar radiation. The exploratory regression result of the solar radiation analysis has an R2adj of 0.19, which indicates its insignificant impact on building energy usage. Finally, the findings of this research provide a foundation for future studies that can continue to explore the potential of using 3D game engines. Keywords: 3D Geo-Visualization, Forestry Education, Remote Sensing, Light Detection and Ranging (LiDAR), Building Energy Usage, Solar Radiation Analysis

Cristy Parsons · Geospatial Portfolio is a dynamic online platform that highlights my expertise and passion for geospatial technologies. This portfolio features a variety of GIS projects I've worked on, showcasing my skills in spatial analysis, mapping, and data visualization. Each project demonstrates the use of GIS tools to address real-world problems, from community art mapping to land use analysis. The site includes interactive maps, embedded StoryMaps, web mapping applications, and other geospatial content, offering visitors an in-depth look at my professional capabilities and projects. It's also a space where I can continue to grow, share new work, and connect with the geospatial community.

Coordinator, Geospatial Technical Services Job #: 1358 Jurisdiction: CMM Division: City Planning & Community Development Department: Sustainable Infrastructure

This study seeks to aid understanding of the difficulties that organizations encounter when their units are geographically distributed and they seek to effectively distribute resources in accordance with geographical and demographic conditions. This study specifically looks at those segments of the population that require health services. The case study in question is representative of the decisions regarding municipal healthcare policies concerning the distribution of resources in terms of staff, material and equipment. It serves as a reference for the inherent difficulties of these decisions. The base of Geographic Information System (GIS) enables spatial and demographic analyses and their relationship with the data regarding the management of the required resources and skills. Analysis using an adapted Resource-Based View (RBV) allows evaluation of the internal decisions within the system in question. The results show the limits of the shared or isolated evaluation of the spatial distribution of resources, which are compromised by the decisions involved in these two approaches. In this sense, the concomitant evaluation of the distributed resources linked to the GIS results in an important analysis element, as it enables the identification of strategic resources that adequately satisfy the purposes of Curitiba's Municipal Health Units, in the state of Paraná.

The Python language offers an efficient way to automate and extend geoprocessing and mapping functionality. In ArcGIS 10, Python was fully integrated into ArcGIS Desktop with the addition of the Python window and the ArcPy site package. This course introduces Python scripting within ArcGIS Desktop to automate geoprocessing workflows. These skills are needed by GIS analysts to work efficiently and productively with ArcGIS for Desktop.After completing this course, you will be able to:Create geoprocessing scripts using the ArcPy site package.Identify common scripting workflows.Write Python scripts that create and update data.Create a script tool using built-in validation.

Coconuts and coconut products are an important commodity in the Tongan economy. Plantations, such as the one in the town of Kolovai, have thousands of trees. Inventorying each of these trees by hand would require lots of time and manpower. Alternatively, tree health and location can be surveyed using remote sensing and deep learning. In this lesson, you'll use the Deep Learning tools in ArcGIS Pro to create training samples and run a deep learning model to identify the trees on the plantation. Then, you'll estimate tree health using a Visible Atmospherically Resistant Index (VARI) calculation to determine which trees may need inspection or maintenance.

To detect palm trees and calculate vegetation health, you only need ArcGIS Pro with the Image Analyst extension. To publish the palm tree health data as a feature service, you need ArcGIS Online and the Spatial Analyst extension.

In this lesson you will build skills in these areas:

• Creating training schema
• Digitizing training samples
• Using deep learning tools in ArcGIS Pro
• Calculating VARI
• Extracting data to points

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

Manager, Geospatial Solutions Job #: 1636 Jurisdiction: Out-of-Scope Department: Sustainable Infrastructure / Geospatial Solutions

HCID is a global grid identification system offering users to refer the location and boundary of a grid cell, available at multiple spatial resolutions, using a single integer number. Instead of using the coordinates (latitude and longitude) of two corners of the grid cell bounding box (i.e., upper-left and lower-right), we assign each grid cell with a sequential integer number, or a grid cell ID, unique to each spatial resolution. This system was developed by HarvestChoice (http://harvestchoice.org) and is being widely used to facilitate analysis of spatial data layers, including the visualization, domain analysis, spatial aggregation/dis-aggregation, and general exchange of spatially-explicit data across disciplines - without needing to use a GIS software and spatial analysis skills. For the five arc-minute resolution of grids, we call the ID system as "CELL5M", whereas ones for 30 arc-second, 30-minute and 1 degree are called CELL30S, CELL30M and CELL1D, respectively. Assigning 0 starting at the upper-left corner (longitude: -180.0, latitude: 90.0) with a geographic projection, for example, CELL5M ranges up to 9,331,199 at the lower-right corner (longitude: 180.0, latitude: -90.0). The grid cell ID at a specific location can be easily computed mathematically, and this can be also easily converted to different resolutions.

To address the global challenge of reducing greenhouse gas emissions contributing to climate change, it is essential to explore innovative, renewable, and sustainable energy solutions. Bioenergy, derived from biological sources, plays a vital role by providing renewable options for heat, electricity, and vehicle fuel. Biofuels from food crops like sugarcane and cassava demonstrate the potential of agricultural products for energy generation, while jatropha is cultivated primarily for oil. This learning activity focuses on land suitability mapping for these selected crops in Florida, incorporating criteria such as temperature, rainfall, soil type, soil pH, and topography. The analysis evaluates the land requirements of food and energy crops within the Food-Energy-Water (FEW) nexus framework, addressing potential land-use conflicts. Geographic Information Systems (GIS) are employed to identify optimal regions for energy crop cultivation, promoting sustainable practices that balance food security, water conservation, and renewable energy production. The modules are developed and designed for undergraduate students, particularly those enrolled in any of courses such as environmental science, GIS, natural resource management, agricultural science and remote sensing. Students will apply GIS and remote sensing techniques to analyze interactions among food, energy, and water resources, focusing on resilient crops. The activity incorporates the 4DEE framework – Core Ecological Concepts, Ecological Practices, Human-Environment Interactions, and Cross-Cutting Themes to enhance understanding of the FEW nexus. Through hands-on projects addressing real-world ecological challenges, students will develop critical skills in geospatial data analysis, data interpretation, and ethical considerations, preparing them for sustainable resource management. Likewise on part of the instructors, the activity is designed for those with intermediate to advanced GIS expertise, particularly in ArcGIS Pro and Google Earth Engine for spatial analysis and a basic understanding and application of the Food-Energy-Water (FEW) Nexus to guide students in making informed land-use decisions that support sustainable development goals.

Many people assume that poor credit scores translate to higher interest rates. But is this assumption true? Follow Jonathan Blum, New York author and journalist, as he attempts to answer this question using GIS. In this lesson, you'll map variations in online loan interest rates. Then, you'll use regression analysis to build a predictive model, quantifying the relationship between interest rates and loan grade rankings.

This workflow can be used to map and measure the correlation between any two variables. It's perfect for anyone interested in regression analysis in ArcGIS Pro.

In this lesson you will build skills in these areas:

• Mapping interest rate hotspots
• Performing regression analysis
• Interpreting regression results
• Finding minimum neighbor distance
• Building the spatial regression model

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

This layer contains the Employment Ontario regional boundaries. There are four regional boundaries in total: Central Eastern Northern Western Employment Ontario (EO) is a service of the Government of Ontario that offers Ontarians a single point of access to Ontario’s employment and training programs and services. EO is an ‘umbrella’ term used to describe a combination of various programs, including Apprenticeship; Literacy and Basic Skills; Youth Job Connection; Canada-Ontario Job Grant; Employment Services, and more. EO services aims to:ensure the highest quality of services and support to help individuals meet their career goals;provide opportunities to make it easier for individuals to improve their skills through education and training;work with employers and communities to build a competitive, skilled, and educated workforce; provide information about careers, occupations, and other community services and supports available to the general public; and deliver services tailored to the needs of each individual, employer, or communityThe service components offered by Employment Ontario is available to all Ontarians as there are no eligibility or access requirements. EO helps connect workers to the right people so they can get the training they need, build their skills, and find a job.