A dataset that explores Green Card sponsorship trends, salary data, and employer insights for public admin concentration geographic info systems in the U.S.

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.

This Innovative Technology Experiences for Students and Teachers (ITEST) project has developed, implemented, and evaluated a series of innovative Socio-Environmental Science Investigations (SESI) using a geospatial curriculum approach. It is targeted for economically disadvantaged 9th grade high school students in Allentown, PA, and involves hands-on geospatial technology to help develop STEM-related skills. SESI focuses on societal issues related to environmental science. These issues are multi-disciplinary, involve decision-making that is based on the analysis of merged scientific and sociological data, and have direct implications for the social agency and equity milieu faced by these and other school students. This project employed a design partnership between Lehigh University natural science, social science, and education professors, high school science and social studies teachers, and STEM professionals in the local community to develop geospatial investigations with Web-based Geographic Information Systems (GIS). These were designed to provide students with geospatial skills, career awareness, and motivation to pursue appropriate education pathways for STEM-related occupations, in addition to building a more geographically and scientifically literate citizenry. The learning activities provide opportunities for students to collaborate, seek evidence, problem-solve, master technology, develop geospatial thinking and reasoning skills, and practice communication skills that are essential for the STEM workplace and beyond. Despite the accelerating growth in geospatial industries and congruence across STEM, few school-based programs integrate geospatial technology within their curricula, and even fewer are designed to promote interest and aspiration in the STEM-related occupations that will maintain American prominence in science and technology. The SESI project is based on a transformative curriculum approach for geospatial learning using Web GIS to develop STEM-related skills and promote STEM-related career interest in students who are traditionally underrepresented in STEM-related fields. This project attends to a significant challenge in STEM education: the recognized deficiency in quality locally-based and relevant high school curriculum for under-represented students that focuses on local social issues related to the environment. Environmental issues have great societal relevance, and because many environmental problems have a disproportionate impact on underrepresented and disadvantaged groups, they provide a compelling subject of study for students from these groups in developing STEM-related skills. Once piloted in the relatively challenging environment of an urban school with many unengaged learners, the results will be readily transferable to any school district to enhance geospatial reasoning skills nationally.

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

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

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.

Employment and wages data for 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

Employment and wages data for all locations, 2001 to 2016. Note on use for analysis: This data set mixes scale. It includes rows for census areas and economic regions, which contain multiple CDP's and cities from this same data set in many cases. To view this data by year and by borough, economic region, or city, add 'Employment and Wages Group Layers' to a WebMap or to the Build Your Own Map application. Contact dcraresearchandanalysis@alaska.gov with questions.Source: Alaska Department of Labor and Workforce Development.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

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

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

Spatial evaluation of the region is associated with the assessment of the Quality of Life (QoL). Despite numerous research endeavoring to define, measure, quantify, and map the quality of life, there exists a consistent fault in Sri Lanka. Hence, the objective of this study was to construct a QoL index and determine the spatial disparities of QoL from the Polpitigma town to its periphery. The assessment was conducted by employing 20 geographical factors that quantify QoL using the Geographic Information Systems (GIS). The evaluation assigned weights to each criterion based on the assessments of both local residents and experts, utilizing the Multi-Criteria Decision Analysis (MCDA) and the Analytical Hierarchy Process (AHP). The findings indicated that cultural factors made a greater contribution compared to the environment,service functions,security and socioeconomic factors. Within the study area, the region with a higher quality of life (HQoL) only covered 4.5% (17.3 km2), whilst the lower QoL zone encompassed 63.8% (252 km2). And also, the distance from the town is a crucial factor in determining the spatial variations in QoL. The derived model can serve as a road map for local-level planning, as it has been validated and shown to have an accuracy of 74% through the Receiver operating characteristic (ROC) curve. Considering the lack of previous research in this field, this study offers a crucial contribution in enhancing the QoL for underprivileged communities in the study area by improving employment, income, and accessibility to physical infrastructure, public utility services, and cultural and recreational facilities. Especially the findings of this study can efficiently guide decisions for the distribution of financial resources to enhance the QoL in impoverished rural communities on the rural periphery of DS.

Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed.

This dataset features detailed maps of material stock and population, as well as the distribution of jobs, for Austria on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642, data: https://zenodo.org/record/4522892), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data.

Temporal extent

The data contains annual maps from 1985 to 2018.

Data format and units

Per Austrian federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.

All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018.

The dataset features

• population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell)
• jobs (Scaled by 100 to reduce data storage size. Divide by 100 to get jobs per cell)
• mass (in tons) of …
  • total material stock
    • material stock in buildings
      • in commercial and industrial buildings
      • in multi-family residential buildings
      • in high-rise buildings
      • in single-family residential buildings
      • in lightweight buildings
    • material stock in road infrastructure
    • material stock in rail infrastructure
    • material stock in other infrastructure

Further information

For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Funding

This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

City and employment centre boundary delineations for Spain (except Basque Country and Navarra), based on building footprint from the Cadastre, and created using the A-DBSCAN algorithm.
GeoJSON polygons expressed in WGS-84 (ie. lon/lat), GeoPackage polygons expressed in ETRS89 / UTM zone 30N (EPSG:25830).
Files included in this dataset:
- sp_blg_adbscan_city_boundaries_v1.geojson: city boundaries in GeoJSON format. Each boundary contains a city ID (city_id in the table) and the number of building footprints it contains (n_buildings in the table).
- sp_blg_adbscan_city_boundaries_v1.gpkg: city boundaries in GeoPackage format. Each boundary contains a city ID (city_id in the table) and the number of building footprints it contains (n_buildings in the table).
- sp_blg_adbscan_emp_centre_boundaries_v1.geojson: employment centre boundaries in GeoJSON format. Each boundary contains a city ID (city_id in the table), an employment centre ID (centre_id in the table) and the number of building footprints it contains (n_buildings in the table).
- sp_blg_adbscan_emp_centre_boundaries_v1.gpkg: employment centre boundaries in GeoPackage format. Each boundary contains a city ID (city_id in the table), an employment centre ID (centre_id in the table) and the number of building footprints it contains (n_buildings in the table).
For further reference, please see original paper:
Arribas-Bel, D.; Garcia-Lopez, M. A.; Viladecans-Marsal, E. (2020). "Building(s and) cities: Delineating urban areas with a machine learning algorithm". Journal of Urban Economics.

Abstract

High quality postgraduate training in science, technology, engineering and mathematics (STEM) related disciplines in sub-Saharan Africa (SSA) is important to strengthen research evidence to advance development and ensure countries achieve the Sustainable Development Goals (SDGs). Equally, participation of women in STEM careers is vital, to ensure that countries develop economies that work for all their citizens. However, women and girls remain underrepresented in STEM due to gender stereotyping, lack of visible role models, and unsupportive policies and work environments. Therefore, there is a need to consolidate information on participation and experiences of women in STEM related postgraduate training and careers in SSA to enhance their contribution to realizing the SDGs. The primary objective of this study is to examine the participation and experiences of women in postgraduate training, and their subsequent recruitment, retention and progression in STEM careers in East Africa. A secondary objective is to establish the gender gaps in training and career engagement in selected STEM related academic disciplines in East Africa. The descriptive study will employ a mixed methods approach, including a scoping review, qualitative interviews, and quantitative analysis of secondary data. We will synthesize results to inform the development of an effective gendered approach and framework to improve participation and experiences of women in STEM training and career engagements in SSA. We will conduct the study over a period of five years.

Geographic coverage

Regional coverage (East Africa Region)

Analysis unit

Individual Women in STEM

Universe

Qualitative data: Women in Science Technology Engineering and Mathematics (STEM) in postgraduate training and career Quantitative data: Postgraduate students, faculty, reseachers and supervisors (both men and women) in STEM in Inter-University Council for East Africa (IUCEA) member Universitiies

Sampling procedure

The study utilized a purposive sampling technique and targeted all universities that offered doctoral programs in applied sciences, technology, engineering, and mathematics. At the time, only 23 of the 74 universities in Kenya—equivalent to 30%—offered doctoral degrees in STEM. It was assumed that a similar or lower percentage would be found in the other five countries, namely Uganda, Tanzania, Rwanda, Burundi, and South Sudan.

Purposive sampling was used to recruit participants from purposively selected universities and national higher education commissions and agencies for the study. In universities, all students enrolled in doctoral programs in STEM were considered. Additionally, female and male students' lecturers, supervisors, mentors, and other faculty members and researchers in the identified institutions were also considered for participation in the study.

Purposive sampling of doctoral students, faculty, and early career researchers (post-doctoral fellows within the first six years since receiving their PhD) was conducted using the following inclusion criteria:

Inclusion criteria i. Worked in a STEM field/discipline ii. Enrolled in a doctoral program within a STEM field iii. Early career researchers in a STEM field in research organizations iv. Faculty in a STEM field at a university

Additionally, registrars, postgraduate training coordinators, heads of departments, and officials from national agencies and ministries related to postgraduate training and research were purposively selected from all the identified universities to provide input on existing policies, guidelines, and enrollment data. For each of the mentioned groups, 7-12 interviews were conducted, totaling 60 interviews.

Sampling deviation

Qualitative For the Key informant interviews one participant was interviewed from the engineers board despite the scope being Inter-University Council for East Africa (IUCEA) member Universities.

Quantitative The online survey was completed by some researchers not working/teaching in IUCEA member universities

Mode of data collection

Other [oth]

Research instrument

Quantitative data collection A. Online Survey This was carried out through an online survey questionnaire that was circulated via email and other digital platforms such as WhatsApp. The questionnaire had various parts: Part A - Participants characteristics This section mainly collected demographic details such as age, gender, nationality, residence, marital status, income, highest level of education completed, year of study, supervision and mentoship relationship, field of study in STEM (Science, Technology, Enginnering and Mathematics), mode of funding of postgraduate degree,

Part B - Status of Gender equality This section collected information on students enrollment and graduation in masters and PhD in STEM looking at gender distribution,

Part C - Factors that contribute to participation of women in STEM This section collected information on the factors or situations encountered while pursuing career in STEM in your specific discipline

Part D - Strategies for Optimizing Women's Engagement in STEM This section collected information on the strategies can maximize engagement of women in STEM training PhD level and subsequent careers

Part E - Effect of the COVID-19 pandemic on women's progression In this section collected information on COVID-19 pandemic affect on research progress or deadline for submission of thesis, COVID-19 pandemic affect on current research funding, COVID-19 pandemic caused researchers to work from home, working from affected progress in studies, any direct responsibilities caring for children, number of children being taken care of, change of domestic work responsibilities since the COVID-19 outbreak, change of domestic work responsibilities since the COVID-19 outbreak on studies, COVID-19 pandemic affect on access to these research tools which inlude: Computer or laptop, Reliable Internet, Assistive Technology, Laboratory equipment, University Library, Archives/special collections and Access to patients/research participants. It als collected information on: any benefits to COVID-19 pandemic for your work, some ways one thinks their supervisor or line manager could support or help one manage the impacts of COVID-19 on studies

The questionnaire was developed in English and was latertranslated into French to accommodate the French speaking countries i.e Burundi and Rwanda. The French questionnaire was backtlanslated to English to ensure the questions still maintained their original meaning. This work was done by an external consultant and the French questionnaires were reviewed by the research assistant from Burundi and tested among postgraduate students in Light University.

All questionnares and modules are provided as external resources.

Cleaning operations

Qualitative The data was collected through qualitative interviews (In-depth interviews) and focus group discussions. They were audio recorded and the recordings were transcribed on Ms Ofiice.The transcript were subjected to data quality checks and the clean transcripts were anonyzed for data protection.

QUANTITATIVE Secondary data The data was collected from the five countries in an Ms Excel designed data abstraction sheet. The data abstraction sheet helped the universities administrators and rergistrars to directly enter the data only in the required field and for the defined or specific variables. For the dataset that was in hardcopy format the data entry was also done using the data abstraction sheets. The data sets were subjected to data quality checks for data quality. We used a standard template to ensure data editing took place during data entry.

Online survey Data entry was in form of responding to the survey. Data editing was done while cleaning the data.

Response rate

Quantitaive The online survey link was circulated using contacts within universities and research institutions in East Africa via email and social media platforms such as WhatApp hence it is impossible to track those who received the survey and hence it is not possible t calculate the survey response rate.

Sampling error estimates

NA

Resilience—the keen ability of people to adapt to changing physical environments—is essential in today's world of unexpected changes.Resilient Communities across Geographies edited by Sheila Lakshmi Steinberg and Steven J. Steinberg focuses on how applying GIS to environmental and socio-economic challenges for analysis and planning helps make communities more resilient.A hybrid of theory and action, Resilient Communities across Geographies uses an interdisciplinary approach to explore resilience studied by experts in geography, social sciences, planning, landscape architecture, urban and rural sociology, economics, migration, community development, meteorology, oceanography, and other fields. Geographies covered include urban and rural, coastal and mountainous, indigenous areas in the United State and Australia, and more. Geographical Information Systems (GIS) is the unifying tool that helped researchers understand resilience.This book shows how GIS:integrates quantitative, qualitative, and spatial data to produce a holistic view of a need for resilience.serves as a valuable tool to capture and integrate knowledge of local people, places, and resources.allows us to visualize data clearly as portrayed in a real-time map or spatial dashboard, thus leading to opportunities to make decisions.lets us see patterns and communicate what the data means.helps us see what resources they have and where they are located.provides a big vision for action by layering valuable pieces of information together to see where gaps are located, where action is needed, or how policies can be instituted to manage and improve community resilience.Resilience is not only an ideal; it is something that people and communities can actively work to achieve through intelligent planning and assessment. The stories shared by the contributing authors in Resilient Communities across Geographies help readers to develop an expanded sense of the power of GIS to address the difficult problems we collectively face in an ever-changing world.AUDIENCEProfessional and scholarly. Higher education.AUTHOR BIOSSheila Lakshmi Steinberg is a professor of Social and Environmental Sciences at Brandman University and Chair of the GIS Committee, where she leads the university to incorporate GIS across the curriculum. Her research interests include interdisciplinary research methods, culture, community, environmental sociology, geospatial approaches, ethnicity, health policy, and teaching pedagogy.Steven J. Steinberg is the Geographic Information Officer for the County of Los Angeles, California. Throughout his career, he has taught GIS as a professor of geospatial sciences for the California State University and, since 2011, has worked as a geospatial scientist in the public sector, applying GIS across a wide range of both environmental and human contexts.Pub Date: Print: 11/24/2020 Digital: 10/27/2020ISBN: Print: 9781589484818 Digital: 9781589484825Price: Print: $49.99 USD Digital: $49.99 USDPages: 350 Trim: 7.5 x 9.25 in.Table of ContentsPrefaceChapter 1. Conceptualizing spatial resilience Dr. Sheila Steinberg and Dr Steven J. SteinbergChapter 2. Resilience in coastal regions: the case of Georgia, USAChapter 3. Building resilient regions: Spatial analysis as a tool for ecosystem-based climate adaptationChapter 4. The mouth of the Columbia River: USACE, GIS and resilience in a dynamic coastal systemChapter 5. Urban resilience: Neighborhood complexity and the importance of social connectivityChapter 6. Mapping Indigenous LAChapter 7. Indigenous Martu knowledge: Mapping place through song and storyChapter 8. Developing resiliency through place-based inquiry in CanadaChapter 9. Engaging Youth in Spatial Modes of Thought toward Social and Environmental ResilienceChapter 10. Health, Place, and Space: Public Participation GIS for Rural Community PowerChapter 11. Best Practices for Using Local KnowledgeContributorsIndex

These data represent high-technology employment clusters across central Arizona-Phoenix. These data are presented by industry: aerospace, bio-industry, information, software for the year 2000.

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

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