This repository contains two Microsoft Excel documents:A quiz with eight questions, assigned to students in a graduate-level GIS programming course as part of Homework Assignment 2. The quiz assesses students' understanding of basic Python programming principles (such as loops and conditional statements).An Excel document with three worksheets, each corresponding to one homework assignment from the same graduate GIS programming course. The document includes self-reported background information (e.g., students' prior programming experience), details about the use of various resources (e.g., websites) for completing assignments, the perceived helpfulness of these resources, and scores for the homework assignments and quizzes.

In this seminar, you will learn how to use Web AppBuilder to create powerful GIS apps that run on any device without writing a single line of code. You will also learn how to quickly build web apps with your data, selection of widgets, and the theme you choose, to make them available to your organization.This seminar was developed to support the following:ArcGIS OnlineWeb AppBuilder for ArcGISWeb AppBuilder for ArcGIS (Developer Edition) 1.0

800+ GIS Engineers with 25+ years of experience in geospatial, We provide following as Advance Geospatial Services:

Analytics (AI) Change detection Feature extraction Road assets inventory Utility assets inventory Map data production Geodatabase generation Map data Processing /Classifications
Contour Map Generation Analytics (AI) Change Detection Feature Extraction Imagery Data Processing Ortho mosaic Ortho rectification Digital Ortho Mapping Ortho photo Generation Analytics (Geo AI) Change Detection Map Production Web application development Software testing Data migration Platform development

AI-Assisted Data Mapping Pipeline AI models trained on millions of images are used to predict traffic signs, road markings , lanes for better and faster data processing

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Experience & Expertise -More than Two decade in Map making business with 800+ GIS expertise -Building world class products with our expertise service division & skilled project management -International Brand “Mappls” in California USA, focused on “Advance -Geospatial Services & Autonomous drive Solutions”

Value Added Services -Production environment with continuous improvement culture -Key metrics driven production processes to align customer’s goals and deliverables -Transparency & visibility to all stakeholder -Technology adaptation by culture

Flexibility -Customer driven resource management processes -Flexible resource management processes to ramp-up & ramp-down within short span of time -Robust training processes to address scope and specification changes -Priority driven project execution and management -Flexible IT environment inline with critical requirements of projects

Quality First -Delivering high quality & cost effective services -Business continuity process in place to address situation like Covid-19/ natural disasters -Secure & certified infrastructure with highly skilled resources and management -Dedicated SME team to ensure project quality, specification & deliverables

SONG, Carol X., Rosen Center for Advanced Computing, Purdue University, 155 South Grant Street, Young Hall, West Lafayette, IN 47907

Science gateways are becoming an integral component of modern collaborative research. They find widespread adoption by research groups to share data, code and tools both within a project and with the broader community. Sustainability beyond initial funding is a significant challenge for a science gateway to continue to operate, update and support the communities it serves. MyGeoHub.org is a geospatial science gateway powered by HUBzero. MyGeoHub employs a business model of hosting multiple research projects on a single HUBzero instance to manage the gateway operations more efficiently and sustainably while lowering the cost to individual projects. This model allows projects to share the gateway’s common capabilities and the underlying hardware and other connected computing resources, and continued maintenance of their sites even after the original funding has run out allowing time for acquiring new funding. MyGeoHub has hosted a number of projects, ranging from hydrologic modeling and data sharing, plant phenotyping, global and local sustainable development, climate variability impact on crops, and most recently, modeling of industry processes to improve reuse and recycling of materials. The shared need to manage, visualize and process geospatial data across the projects has motivated the Geospatial Data Building Blocks (GABBs) development funded by NSF DIBBs. GABBs provides a “File Explorer” type user interface for managing geospatial data (no coding is needed), a builder for visualizing and exploring geo-referenced data without coding, a Python map library and other toolkits for building geospatial analysis and computational tools without requiring GIS programming expertise. GABBs can be added to an existing or new HUBzero site, as is the case on MyGeoHub. Teams use MyGeoHub to coordinate project activities, share files and information, publish tools and datasets (with DOI) to provide not only easy access but also improved reuse and reproducibility of data and code as the interactive online tools and workflows can be used without downloading or installing software. Tools on MyGeoHub have also been used in courses, training workshops and summer camps. MyGeoHub is supporting more than 8000 users annually.

Summary: Week 2: QuizStorymap metadata page: URL forthcoming Possible K-12 Next Generation Science standards addressed:Grade level(s) K: Standard K-ESS3-2 - Earth and Human Activity - Ask questions to obtain information about the purpose of weather forecasting to prepare for, and respond to, severe weatherGrade level(s) 1: Standard 1-LS1-1 - From Molecules to Organisms: Structures and Processes - Use materials to design a solution to a human problem by mimicking how plants and/or animals use their external parts to help them survive, grow, and meet their needsGrade level(s) K-2: Standard K-2-ETS1-1 - Engineering Design - Ask questions, make observations, and gather information about a situation people want to change to define a simple problem that can be solved through the development of a new or improved object or tool.Grade level(s) 3: Standard 3-PS2-3 - Motion and Stability: Forces and Interactions - Ask questions to determine cause and effect relationships of electric or magnetic interactions between two objects not in contact with each otherGrade level(s) 4: Standard 4-PS3-3 - Energy - Ask questions and predict outcomes about the changes in energy that occur when objects collideGrade level(s) 6-8: Standard MS-PS2-3 - Motion and Stability: Forces and Interactions - Ask questions about data to determine the factors that affect the strength of electric and magnetic forcesGrade level(s) 6-8: Standard MS-ESS1-3 - Earth’s Place in the Universe - Analyze and interpret data to determine scale properties of objects in the solar systemGrade level(s) 6-8: Standard MS-ESS1-4 - Earth’s Place in the Universe - Construct a scientific explanation based on evidence from rock strata for how the geologic time scale is used to organize Earth’s 4.6-billion-year-old historyGrade level(s) 6-8: Standard MS-ESS2-2 - Earth’s Systems - Construct an explanation based on evidence for how geoscience processes have changed Earth’s surface at varying time and spatial scalesGrade level(s) 6-8: Standard MS-ESS3-5 - Earth and Human Activity - Ask questions to clarify evidence of the factors that have caused the rise in global temperatures over the past centuryGrade level(s) 9-12: Standard HS-PS1-3 - Matter and Its Interactions - Plan and conduct an investigation to gather evidence to compare the structure of substances at the bulk scale to infer the strength of electrical forces between particlesGrade level(s) 9-12: Standard HS-PS1-7 - Matter and Its Interactions - Use mathematical representations to support the claim that atoms, and therefore mass, are conserved during a chemical reactionGrade level(s) 9-12: Standard HS-PS1-8 - Matter and Its Interactions - Develop models to illustrate the changes in the composition of the nucleus of the atom and the energy released during the processes of fission, fusion, and radioactive decay.Grade level(s) 9-12: Standard HS-PS3-2 - Energy - Develop and use models to illustrate that energy at the macroscopic scale can be accounted for as a combination of energy associated with the motion of particles (objects) and energy associated with the relative position of particles (objects).Grade level(s) 9-12: Standard HS-PS4-2 - Waves and Their Applications in Technologies for Information Transfer - Evaluate questions about the advantages of using digital transmission and storage of informationGrade level(s) 9-12: Standard HS-LS2-1 - Ecosystems: Interactions, Energy, and Dynamics - Use mathematical and/or computational representations to support explanations of factors that affect carrying capacity of ecosystems at different scalesGrade level(s) 9-12: Standard HS-LS2-2 - Ecosystems: Interactions, Energy, and Dynamics - Use mathematical representations to support and revise explanations based on evidence about factors affecting biodiversity and populations in ecosystems of different scalesGrade level(s) 9-12: Standard HS-LS3-1 - Heredity: Inheritance and Variation of Traits - Ask questions to clarify relationships about the role of DNA and chromosomes in coding the instructions for characteristic traits passed from parents to offspringGrade level(s) 9-12: Standard HS-ESS2-1 - Earth’s Systems - Develop a model to illustrate how Earth’s internal and surface processes operate at different spatial and temporal scales to form continental and ocean-floor features.Most frequently used words:questionscaleApproximate Flesch-Kincaid reading grade level: 9.8. The FK reading grade level should be considered carefully against the grade level(s) in the NGSS content standards above.

description: Quivira National Wildlife Refuge was established in 1955, and a detailed vegetation map was not available for management purposes. With the present development of a biological program and Comprehensive Conservation Plan (CCP), a baseline vegetation map of the refuge was identified as a necessity. Development of the vegetation map and associated report was a multi-step process. Aerial photography (NAIP, 2008) was used with eCognition to create polygons of different plant communities based on the likeness of surrounding pixels in the area. Prior to ground-truthing, the following activities were accomplished: training on vegetation mapping using GIS (previous experience and National Conservation Training Center course), creation of an vegetation association and alliance dichotomous key, development of a refuge plant key and identification skills, and preparation of maps for ground truthing. Once out in the field dominant plants were identified for appropriate vegetation alliance and association classification, plant specimens were collected for the refuge herbarium as necessary and additional observations and photos were gathered for the report. Over the course of the project, classification data was entered into a GIS and polygons were appropriately modified to create the final map. At Quivira, results found a total of 42 alliances and 43 associations.The most dominant plants throughout the refuge in 2008 based on canopy cover were saltgrass, plum, little bluestem and cottonwood. The number of alliances and associations found on the refuge show high species diversity.; abstract: Quivira National Wildlife Refuge was established in 1955, and a detailed vegetation map was not available for management purposes. With the present development of a biological program and Comprehensive Conservation Plan (CCP), a baseline vegetation map of the refuge was identified as a necessity. Development of the vegetation map and associated report was a multi-step process. Aerial photography (NAIP, 2008) was used with eCognition to create polygons of different plant communities based on the likeness of surrounding pixels in the area. Prior to ground-truthing, the following activities were accomplished: training on vegetation mapping using GIS (previous experience and National Conservation Training Center course), creation of an vegetation association and alliance dichotomous key, development of a refuge plant key and identification skills, and preparation of maps for ground truthing. Once out in the field dominant plants were identified for appropriate vegetation alliance and association classification, plant specimens were collected for the refuge herbarium as necessary and additional observations and photos were gathered for the report. Over the course of the project, classification data was entered into a GIS and polygons were appropriately modified to create the final map. At Quivira, results found a total of 42 alliances and 43 associations.The most dominant plants throughout the refuge in 2008 based on canopy cover were saltgrass, plum, little bluestem and cottonwood. The number of alliances and associations found on the refuge show high species diversity.

Maps exist to convey information to people, whether that information is how to get from one point to another or how many oil fields are located in a given region. Effective cartography can convey that information efficiently to map users.In this course, you will be introduced to a five-step workflow for designing and creating maps. This workflow can be applied to any map or output medium (print or digital). This course will cover all steps of the workflow in general terms, emphasizing the first two steps: the cartographic planning process and data evaluation.After completing this course, you will be able to perform the following tasks:Identify and describe the cartographic workflow steps.Explain cartographic design controls and how they drive map creation.Apply the planning step of the cartographic workflow.Evaluate data sources to determine applicability.Discuss why basemap and operational layers are important.Assign the correct coordinate system to data based on the geographic extent and map objective.Assess the level of detail required for a map and apply generalization techniques when appropriate.

Purpose: The mission of the Geometric Design Laboratory (GDL) is to support the Office of Safety Research and Development in research related to the geometric design of roadways and the impacts on safety. The GDL provides technical support to develop, maintain, and enhance tools for the safety evaluation of highway geometric design alternatives. This includes coordination of the Highway Safety Manual (HSM) with related tools, e.g., the Interactive Highway Safety Design Model (IHSDM) and SafetyAnalyst. The GDL supports the HSM through implementation of HSM methods in IHSDM software; by providing technical support to HSM users; by performing HSM-related technology facilitation; and by conducting HSM-related training and research.The GDL also contributes to Federal Highway Administration's (FHWA's) Roadway Safety Data Program (RSDP) initiatives to advance State and local safety data systems and safety data analyses by supporting the use of Geographic Information Systems (GIS) for advancing the quantification of highway safety (e.g., through the integration of GIS with highway safety analysis tools); and supports the Safety Training and Analysis Center (STAC) in its mission to assist the research community and State departments of transportation (DOTs) in using data from the second Strategic Highway Research Program's (SHRP2) Naturalistic Driving Study (NDS) and Roadway Information Database (RID).Laboratory Description: GDL staff focuses on the following tasks.Research: Support IHSDM, Highway Safety Manual, and other highway safety-related research efforts.Software Development: Support the full life cycle of IHSDM software development, including developing functional specifications; performing verification and validation of the models that are core IHSDM components; providing recommendations to the IHSDM software developer on all facets of the software (e.g., the graphical user interface, output/reporting); preparing IHSDM documentation; performing alpha testing of IHSDM software; and coordinating the beta testing of IHSDM software by end users. The GDL also helps coordinate the interaction of key players in IHSDM software development, including research contractors, software developers, end users, and commercial computer-aided design (CAD)/roadway design software vendors.Technology Facilitation: Support technology facilitation for the IHSDM and HSM. The GDL provides the sole source of technical support to IHSDM users and provides technical support to HSM users. GDL markets IHSDM and HSM to decisionmakers and potential end users, and participates in developing and delivering IHSDM/HSM training.Laboratory Capabilities: The staff of the GDL includes professionals with expertise in transportation engineering and familiarity with software development, which allows the GDL to support IHSDM development in various ways and to assume a unique coordination role. The GDL's transportation engineering expertise supports the laboratory's function of reviewing and assisting the development of the engineering models included in IHSDM for evaluating the safety of roadway designs. By combining transportation engineering and software development expertise, the GDL has the unique ability to evaluate software from both the software developer and end-user perspective.Communications and engineering skills help GDL staff to understand the needs of the audience (e.g., design engineers), thereby supporting effective technical assistance to end users.IHSDM development is a long-term effort, involving many research contractors, software developers, and FHWA staff. In addition, FHWA seeks input from end users and user organizations to help ensure that IHSDM is responsive to user needs. The staff of the GDL helps coordinate the interaction of all those involved with IHSDM development.Staff at the GDL participates in HSM development and technology facilitation. In addition, the IHSDM Crash Prediction Module is a faithful implementation of HSM Part C (Predictive Method). Therefore, GDL staff is well equipped to support HSM-related activities.Laboratory Equipment: The GDL is equipped with computer hardware and software typically employed by users of IHSDM, including commercial CAD/roadway design software.Laboratory Services: The GDL supports the HSM through implementation of HSM methods in IHSDM software; by providing technical support to HSM users; by performing HSM-related technology facilitation; and by conducting HSM-related research.To develop and promote IHSDM, GDL staff provides or has provided the following services:For all IHSDM safety evaluation modules (Crash Prediction, Design Consistency, Intersection Review, Policy Review, Traffic Analysis and Driver/Vehicle), the GDL conducts software testing to verify, validate, and evaluate the IHSDM software system and develops and/or finalizes the software's functional specifications.Participates in development and delivery of IHSDM training.Provides the sole source of technical assistance to IHSDM users ( ihsdm.support@dot.gov; 202-493-3407).Supports coordination and integration of IHSDM with civil design software packages.Develops, reviews, maintains, and enhances documentation for IHSDM users.Conducts technical reviews and prepares review comments on contract research deliverables.Provides technical support in the development, production, and dissemination of IHSDM-related marketing materials.Provides technical content for the IHSDM Web site.The GDL also contributes to FHWA Roadway Safety Data Program (RSDP) initiatives to advance State and local safety data systems and safety data analyses by supporting the use of GIS for advancing the quantification of highway safety; e.g., through the integration of GIS with highway safety analysis tools (including extraction of data from GIS for input to safety analyses and representation of safety analysis results in the GIS environment). Such contributions support efforts by State and local agencies to:Extract roadway geometrics from GIS/GPS data.Develop GIS-based tools for collecting roadway inventory data.Process data gathered using instrumented vehicles (e.g., LiDAR).Leverage GIS/GPS data for populating safety databases and performing safety analyses (e.g., safety management - HSM Part B, and crash prediction - HSM Part C). The GDL supports the Safety Training and Analysis Center (STAC) in assisting the research community and State DOTs in using data from the SHRP2 Naturalistic Driving Study (NDS) and Roadway Information Database (RID); e.g., by assessing analytical possibilities associated with GIS data linkages to the RID.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

The rapid expansion of geospatial technology has brought the need for new skills to be able to understand and apply the newly available tools – not just for GIS professionals but also for engineers, urban planners, rural development specialists, conservationists and many others. Experience from other countries has shown a rapid growth in the use of of geospatial technologies in government ministries, in the private and development sectors, and in the academic sector as a source of learning and research. The Myanmar Information Management Unit / MIMU has organised this two-day Symposium on May 24-25 with the support of the Government of Canada. It will bring together over 120 participants to discuss the evolving use of geospatial technologies in Myanmar and how training can be best oriented to meet these needs. Participants include academics from the 25 universities across the country offering courses in the use of geospatial technologies, representatives of government departments, the private and development sectors. International experts from leading institutions from the Netherlands and Thailand will also join to share experience and approaches. Key issues which will be explored in the Symposium: Can skilled geospatial workers in Myanmar meet the needs of today’s academic, government, private and development sector? What steps should training institutions and professionals take to ensure capacity for future needs, as the geospatial field continues its evolution.

Professional Development Section training bulletin manual focuses on community relations, legal issues, patrol procedures and officer safety.

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

You will learn how to combine layout composition, color, symbology, and text to design a map that clearly communicates your intended message.

Goal 4Ensure inclusive and equitable quality education and promote lifelong learning opportunities for allTarget 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomesIndicator 4.1.1: Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sexSE_TOT_PRFL: Proportion of children and young people achieving a minimum proficiency level in reading and mathematics (%)Indicator 4.1.2: Completion rate (primary education, lower secondary education, upper secondary education)SE_TOT_CPLR: Completion rate, by sex, location, wealth quintile and education level (%)Target 4.2: By 2030, ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary educationIndicator 4.2.1: Proportion of children aged 24-59 months who are developmentally on track in health, learning and psychosocial well-being, by sexiSE_DEV_ONTRK: Proportion of children aged 36−59 months who are developmentally on track in at least three of the following domains: literacy-numeracy, physical development, social-emotional development, and learning (% of children aged 36-59 months)Indicator 4.2.2: Participation rate in organized learning (one year before the official primary entry age), by sexSE_PRE_PARTN: Participation rate in organized learning (one year before the official primary entry age), by sex (%)Target 4.3: By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including universityIndicator 4.3.1: Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, by sexSE_ADT_EDUCTRN: Participation rate in formal and non-formal education and training, by sex (%)Target 4.4: By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurshipIndicator 4.4.1: Proportion of youth and adults with information and communications technology (ICT) skills, by type of skillSE_ADT_ACTS: Proportion of youth and adults with information and communications technology (ICT) skills, by sex and type of skill (%)Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situationsIndicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregatedSE_GPI_PTNPRE: Gender parity index for participation rate in organized learning (one year before the official primary entry age), (ratio)SE_GPI_TCAQ: Gender parity index of trained teachers, by education level (ratio)SE_GPI_PART: Gender parity index for participation rate in formal and non-formal education and training (ratio)SE_GPI_ICTS: Gender parity index for youth/adults with information and communications technology (ICT) skills, by type of skill (ratio)SE_IMP_FPOF: Immigration status parity index for achieving at least a fixed level of proficiency in functional skills, by numeracy/literacy skills (ratio)SE_NAP_ACHI: Native parity index for achievement (ratio)SE_LGP_ACHI: Language test parity index for achievement (ratio)SE_TOT_GPI: Gender parity index for achievement (ratio)SE_TOT_SESPI: Low to high socio-economic parity status index for achievement (ratio)SE_TOT_RUPI: Rural to urban parity index for achievement (ratio)SE_ALP_CPLR: Adjusted location parity index for completion rate, by sex, location, wealth quintile and education levelSE_AWP_CPRA: Adjusted wealth parity index for completion rate, by sex, location, wealth quintile and education levelSE_AGP_CPRA: Adjusted gender parity index for completion rate, by sex, location, wealth quintile and education levelTarget 4.6: By 2030, ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracyIndicator 4.6.1: Proportion of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sexSE_ADT_FUNS: Proportion of population achieving at least a fixed level of proficiency in functional skills, by sex, age and type of skill (%)Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture’s contribution to sustainable developmentIndicator 4.7.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessmentTarget 4.a: Build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for allIndicator 4.a.1: Proportion of schools offering basic services, by type of serviceSE_ACS_CMPTR: Schools with access to computers for pedagogical purposes, by education level (%)SE_ACS_H2O: Schools with access to basic drinking water, by education level (%)SE_ACS_ELECT: Schools with access to electricity, by education level (%)SE_ACC_HNDWSH: Schools with basic handwashing facilities, by education level (%)SE_ACS_INTNT: Schools with access to the internet for pedagogical purposes, by education level (%)SE_ACS_SANIT: Schools with access to access to single-sex basic sanitation, by education level (%)SE_INF_DSBL: Proportion of schools with access to adapted infrastructure and materials for students with disabilities, by education level (%)Target 4.b: By 2020, substantially expand globally the number of scholarships available to developing countries, in particular least developed countries, small island developing States and African countries, for enrolment in higher education, including vocational training and information and communications technology, technical, engineering and scientific programmes, in developed countries and other developing countriesIndicator 4.b.1: Volume of official development assistance flows for scholarships by sector and type of studyDC_TOF_SCHIPSL: Total official flows for scholarships, by recipient countries (millions of constant 2018 United States dollars)Target 4.c: By 2030, substantially increase the supply of qualified teachers, including through international cooperation for teacher training in developing countries, especially least developed countries and small island developing StatesIndicator 4.c.1: Proportion of teachers with the minimum required qualifications, by education leveliSE_TRA_GRDL: Proportion of teachers who have received at least the minimum organized teacher training (e.g. pedagogical training) pre-service or in-service required for teaching at the relevant level in a given country, by sex and education level (%)

Welcome to the National Conservation Training Center’s (NCTC) annual report! The NCTC is the primary training facility for the U.S. Fish and Wildlife Service (USFWS) and its partners, located on a 533-acre campus along the Potomac River in Shepherdstown, West Virginia. We design and deliver a full range of mission-critical training and employee development programs for USFWS employees and the conservation community.This report features our high points for 2024, organized under five themes inspired by our strategic plan. We believe everyone has the potential to be a conservation leader, and together, we can build a lasting legacy.This year, we advanced learning and development to meet the various needs of the conservation community by identifying critical training needs, increasing course feedback, delivering hands-on programs, and providing access to scientific information.We nurture, inspire, and equip generations of leaders through various leadership development programs. By combining classroom learning with hands-on experiences and interactions with mentors and experts, NCTC is cultivating a legacy of conservation leadership.The NCTC brings together various organizations and communities to jointly address pressing conservation challenges. By hosting learning events, convening experts, and fostering collaboration, we are working to ensure a sustainable future .

To maximize the benefits that the agency and employees gain from learning and development activities and to ensure a supportive organizational culture, ADOT undertook a research study to explore employee perceptions of the training opportunities offered at ADOT and the metrics that can be used to measure the effectiveness of the EBD training courses and programs. Tina Samartinean, ADOT's EBD administrator and the project champion, wanted a framework to incorporate best practices for adult learning and a strategy for continual improvement of the agency’s learning and development programs.

Task 1.3: Employee Learning and Development Training Programs and Current Practices

Task 1.4: Literature Review

Task 2.1: Measures, Data Collection Tools, and Protocols

Task 2.2: Test and Validate Measures – Pilot

Task 3.1: Data Coding, Cleaning, and Validation Procedures

Task 3.2: Data Analysis

Data Summary

Final Report: Measuring and Improving the Effectiveness of ADOT’s Employee Learning and Development Program

Compendium

Development Policy Areas are generally based off of and follow the County's Boundary and Parcel geometry. The dataset was developed and revised over the course of the Plan2040 General Development Plan process. It was adopted by the County Council on May 3rd, 2021. The Region Plan process has a similar format and has been completed for Region 2 (Bill No. 6-24) and Region 7 (Bill No. 8-24). These areas have been updated based on the Region Plan adoptions and the outstanding regions will be appended upon approval. The Development Policy Areas data are effective as of June 23rd 2024.

The world is shifting. Recent events point to urgent needs for new design approaches and solutions. Geodesign provides a revolutionary way forward -- capitalizing on dynamic geospatial technologies to facilitate collaborations that result in true community engagement. This leads to adaptive and resilient strategies at any scale of the built and natural environment. Here you can see how professionals are advancing their skills to take advantage of new technologies and processes. Penn State’s online Geodesign graduate programs (in the Landscape Architecture Dept.) have an excellent 8-year record of helping ignite and retool careers. Options include taking a course for professional development, signing up for the one-year Graduate Certificate, or going for the Master in Professional Studies (MPS) in Geodesign.