Mixed-methods designs, especially those in which case selection is regression-based, have become popular across the social sciences. In this paper, we highlight why tools from spatial analysis—which have largely been overlooked in the mixed-methods literature—can be used for case selection and be particularly fruitful for theory development. We discuss two tools for integrating quantitative and qualitative analysis: (1) spatial autocorrelation in the outcome of interest; and (2) spatial autocorrelation in the residuals of a regression model. The case selection strategies presented here enable scholars to systematically use geography to learn more about their data and select cases that help identify scope conditions, evaluate the appropriate unit or level of analysis, examine causal mechanisms, and uncover previously omitted variables.

Database created for replication of GeoStoryTelling. Our life stories evolve in specific and contextualized places. Although our homes may be our primarily shaping environment, our homes are themselves situated in neighborhoods that expose us to the immediate “real world” outside home. Indeed, the places where we are currently experiencing, and have experienced life, play a fundamental role in gaining a deeper and more nuanced understanding of our beliefs, fears, perceptions of the world, and even our prospects of social mobility. Despite the immediate impact of the places where we experience life in reaching a better understanding of our life stories, to date most qualitative and mixed methods researchers forego the analytic and elucidating power that geo-contextualizing our narratives bring to social and health research. From this view then, most research findings and conclusions may have been ignoring the spatial contexts that most likely have shaped the experiences of research participants. The main reason for the underuse of these geo-contextualized stories is the requirement of specialized training in geographical information systems and/or computer and statistical programming along with the absence of cost-free and user-friendly geo-visualization tools that may allow non-GIS experts to benefit from geo-contextualized outputs. To address this gap, we present GeoStoryTelling, an analytic framework and user-friendly, cost-free, multi-platform software that enables researchers to visualize their geo-contextualized data narratives. The use of this software (available in Mac and Windows operative systems) does not require users to learn GIS nor computer programming to obtain state-of-the-art, and visually appealing maps. In addition to providing a toy database to fully replicate the outputs presented, we detail the process that researchers need to follow to build their own databases without the need of specialized external software nor hardware. We show how the resulting HTML outputs are capable of integrating a variety of multi-media inputs (i.e., text, image, videos, sound recordings/music, and hyperlinks to other websites) to provide further context to the geo-located stories we are sharing (example https://cutt.ly/k7X9tfN). Accordingly, the goals of this paper are to describe the components of the methodology, the steps to construct the database, and to provide unrestricted access to the software tool, along with a toy dataset so that researchers may interact first-hand with GeoStoryTelling and fully replicate the outputs discussed herein. Since GeoStoryTelling relied on OpenStreetMap its applications may be used worldwide, thus strengthening its potential reach to the mixed methods and qualitative scientific communities, regardless of location around the world. Keywords: Geographical Information Systems; Interactive Visualizations; Data StoryTelling; Mixed Methods & Qualitative Research Methodologies; Spatial Data Science; Geo-Computation.

This qualitative data-set is of young people's spatial imaginaries within the UK context. It contains interviews carried out with young people aged 16/17 years across different geographic contexts. In particular, interviews focussed around the importance and significance of place, including: i) the role place has on the choices of young people who are socially and educationally similar but located in geographically diverse areas; ii) ways in which economically, socially, culturally or politically distinct places act as pull or push factors for different social groups; iii) what social, cultural, or economic importance particular localities hold for different groups.

The creation of a fairer society through social mobility is high on the political agenda in the UK. It is often assumed that widening participation in higher education (HE), through various policies and initiatives, will equate to a fairer and more socially mobile society. Yet, while more disadvantaged groups are now progressing to HE, social mobility remains weak, suggesting that this is an over-simplified picture of the ways in which social inequalities are (re)produced in countries like the UK. The geographical (im)mobility of young people at this key transition point is rarely alluded to here, in terms of its significance in shaping social (im)mobility. In spatially diverse countries like the UK, access to universities, key labour markets, social networks, and other valuable resources often necessitate some degree of geographical mobility. In addressing social inequalities in wider society, it is therefore crucial to understand the nature of student flows across diverse parts of the UK, including the rationales different young people have for their (im)mobility to and from different places. There is already some evidence to suggest that the costs of HE study can deter the most disadvantaged young people from moving away for their studies, but what other place-based factors, including the cultural, social, and economic characteristics of localities might be important in shaping student (im)mobility? This interdisciplinary project will undertake an innovative and far-reaching programme of policy relevant research addressing the mobility patterns of UK HE students. The value of this research has been endorsed by all four UK HE Funding Councils, the UK Government's Social Mobility and Child Poverty Commission (Chaired by Rt. Hon. Alan Milburn), The Sutton Trust, and Universities UK. These organisations are members of the project stakeholder group and will be closely involved in the research and dissemination programme, ensuring that the research addresses areas of policy relevance and reaches a wide audience. This novel research will uncover, for the first time, the nature of student flows within and across the four countries of the UK, together with rich and in-depth understandings about how they are shaped. Taking into account the socially, economically, politically and culturally diverse nature of UK society, the project will seek to understand the placed nature of educational decision making in particular. This unique work is interdisciplinary in nature, drawing on, and contributing to, the academic disciplines of geography, education, and sociology. The research is mixed methods and organised around two distinct but sequential phases, which include large scale quantitative analysis of UK-wide student records data (phase 1) that will frame the collection of new qualitative data (phase 2). Phase 1 will involve advanced spatial analysis to examine student flows at country, region, and locality levels, producing innovative graphics displaying these spatial movements in visual form. This analysis will explore patterns and relationships between student movements and social as well as spatial characteristics. In the second phase, qualitative research will take place in 10 purposefully selected case study schools across the UK, selected on the basis of criteria developed from the quantitative analysis. To explore the sorts of factors shaping young people's mobility patterns, data collection will involve interviews with young people, two members of their social network, as well as observation of their school contexts. These rich qualitative data will dig beneath the surface of the quantitative patterns, capturing how young people's subjective experiences of space and their own geographical imaginaries impact on their geographic (im)mobility. It will explore how these relationships to place and mobility intentions are constructed and influenced by their individual biographies, social network and school.

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

The Geographic Information System Analytics Market size was valued at USD 9.1 USD Billion in 2023 and is projected to reach USD 24.80 USD Billion by 2032, exhibiting a CAGR of 15.4 % during the forecast period. Geographic Information System (GIS) Analytics is the process by which quantitative and qualitative information of geographic locales are employed to describe features, conduct statistical analysis, and discover correlations of geographical regions. Some of the key types are referred to as spatial, in which cases the locations and characteristics of features are analyzed to determine their spatial associations, and temporal which investigate variations in the features over time. Network analysis is also compiled within the GIS Analytics; this process is based upon the connection and throughput in networks and the overlay model that overlays data on top of each other for interaction determinations. Some of the features often utilized in GIS include the generation of maps, spatial analysis, and georeferencing amongst others. It has diverse types of applications in civil, planning and development, environment, disaster management, and transport sectors to understand and analyze spatial information and support the organization’s decision-making process. Key drivers for this market are: Increasing Adoption of Cloud-based Managed Services to Drive Market Growth. Potential restraints include: Growing Security Threats to Hamper the Market . Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

The tabular dataset is a product of household survey conducted in 2018. The sampling geography was a predefined coastal region of North and South Carolina adjacent to offshore wind development areas. The subject of the data collection was resident perceptions of local offshore wind energy development. Variables relate to place attachment, recreational activities, social value of favorite places, awareness, perceived impact to important quality of life items, support level, past and future action, and demographic and household characteristics. There are two formats of the tabular dataset provided: csv and STATA. (2019-07-19) The spatial/GIS dataset is a product of household survey conducted in 2018. The sampling geography was a predefined coastal region of North and South Carolina adjacent to offshore wind development areas. The subject of the data collection was resident perceptions of local offshore wind energy development. The spatial/GIS dataset is associated with a mapping question, Question 6 of the survey instrument. Question 6 asked respondents to identify three Favorite Places on a map of the study region, as well as to identify associated social values for each location. Locations identified were assigned to 10 square km cells during the survey coding process for spatial analysis and to visualize the spatial distribution of Favorite Places. Shapefiles are provided in the dataset. (OMB CONTROL NUMBER: 0648-0744) (2019-07-19)

The Socio-Economic Atlas of Myanmar focuses on the analysis and evaluation of regional differences in geographical conditions, natural resources, infrastructure and, in particular, the socio-economic development in the states and regions of the country in the current transformation process of Myanmar. The Atlas is based on international literature, statistical data, qualitative research and spatial information in a Geographic Information System on Myanmar. The spatial analyses aim to increase the state of knowledge about Myanmar both within the country and abroad, and to support decision-making on spatial development policy.

Mapping Layer Data Released: 06/15/2017, | Last Updated 04/20/2024Data Currency: This data is checked semi-annually from it's enterprise federal source fo 2010 CENSUS Data and will support mapping, analysis, data exports and the Open Geospatial Consortium (OGC) Application Programming Interface (API).Data Update Frequency: Twice, YearlyData Cycle | History (as required below)QA/QC Performed: December, 2024Next Scheduled Data QA/QC: July, 2024CDC PLACES (2010 CENSUS) FEATURE LAYERData Requester: Rhode Island Executive Office of Health and Human Service (OHHS) via Health Equity Institute (HEI).Data Requester: Rhode Island Department of Health, Maternal Child Health via Health Equity Institute (HEI).Data Request: Provide a database deliverable via download that contains both US CENSUS tracts and USPS Zip Code Tabulation Areas (ZCTA).HEALTH EQUITY INSTITUTE DATA CONNECT RI Using Modern GIS (Mapping)🡅 Click IT 🡅Facilitate transformative mapping visualizations that engage constituents and measure the impact of real-world solutions.Instructions to Join Your Data Provided Below STEP 1: Video (Pending)STEP 2: Video (Pending)STEP 3: Video (Pending)There are twenty-two U.S. CENSUS fields (download here) that you can join to your datasets. For additional insight, please contact the Center for Health Data and Analysis (CHDA) Rhode Island Department of Health (GIS) Mapping Department for assistance.Database Enhancement: This database contains two (2) additional data fields for consideration to be added to the existing 2020 State of Rhode Island Health Equity Map.Zip Code Tabulation Area (ZCTA)ZCTA/Tract Relationship (Singular ZCTAs per Tract, versus Multiple ZCTAs per Tract)Additional Information: While ZCTAs can be useful for certain qualitative purposes, such as broad or general high level analysis, they may not provide the level of granularity and accuracy required for in-depth demographic research which is required for policy mapping. ZCTAs can change frequently as the US Postal Service (USPS) adjusts postal routes and boundaries. These changes can lead to inconsistencies and challenges in tracking demographic trends and making accurate comparisons over time.RIDOH GIS encourages analysts to make the appropriate choice of using census based data, with their consistent boundaries readily available for suitability for spatial analysis when conducting detailed demographic research.Here are a few reasons why you might want to consider using census based data (tracts, block groups, and blocks) instead of ZCTAs:1. Inaccurate Representations: ZCTAs are not designed for statistical analysis or demographic research. They are created by the United States Postal Service (USPS) for efficient mail delivery and can often span multiple cities, counties, or even states. As a result, ZCTAs may not accurately represent the actual geographic boundaries or demographic characteristics of a specific area.2. Lack of Granularity: ZCTAs are typically larger than census tracts, which are smaller, more homogeneous geographic units defined by the U.S. Census Bureau. Census tracts are designed to be relatively consistent in terms of population size, allowing for more detailed analysis at a local level. ZCTAs, on the other hand, can vary significantly in terms of population size, making it challenging to draw precise conclusions about specific neighborhoods or communities.3. Data Availability and Compatibility: Census tracts are used by the U.S. Census Bureau to collect and report demographic data. Consequently, a wide range of demographic information, such as population counts, age distribution, income levels, and education levels, is readily available at the census tract level. In contrast, data specifically tailored to ZCTAs may be more limited, making it difficult to obtain comprehensive and consistent data for demographic analysis.4. Changes Over Time: Census tracts are relatively stable over time, allowing for consistent longitudinal analysis. ZCTAs, however, can change frequently as the USPS adjusts postal routes and boundaries. These changes can lead to inconsistencies and challenges in tracking demographic trends and making accurate comparisons over time.5. Spatial Analysis: Census tracts are designed to maintain a level of spatial proximity, adjacency, or connectedness of these data containers while providing consistency and continuity over time - making them useful for spatial analysis. Mapping. ZCTAs, on the other hand, may not exhibit the same level of spatial coherence due to their primary purpose being mail delivery efficiency rather than geographic representation.State Agencies - Contact RIDOH GIS - Learn More About Mapping Data Available at the Census Tract LevelRIDOH GIS releases this database with the caveats noted above and that the researcher can accurately align the ZCTAs with the corresponding census tracts. Careful consideration should be given to the comparability and compatibility of the data collected at different geographic levels to ensure valid and meaningful statistical conclusions. Data Dictionary: 2010 Decennial CensusOBJECT ID - the count of each census tract entity.GEOID (10) STATE,COUNTY,TRACT - Numeric US CENSUS Tract Description (2010) HEZ (10) - Health Equity Zone (2020)LOCATION (10) - Plain Language Census Tract Descriptor (2010)COUNTY (10) NAME - County Name (2010)STATE (10) NAME - State Name (2010)ZCTA (23) - Zip Code Tabulation Area - Numeric US CENSUS ZCTA Description (2023)ZCTA/TRACT CONTEXT - Number of ZCTAs (Singular/Multiple) that reside within a US CENSUS TractST (10) - Numeric US CENSUS Tract Description (2010) CO (10) - Numeric US CENSUS Tract Description (2010)ST (10) CO (10) - Numeric US CENSUS Tract Description (2010)TRACT (10) - Numeric US CENSUS Tract Description (2010)GEOID (10) - Numeric US CENSUS Tract Description (2010)TRIBAL TRACT (10) - Numeric US CENSUS Tract Description (2010)Additional Mapping DataThe user is provided authoritative Federal Information Processing Standards (FIPS) such as numeric descriptions of state, county and tract identification, in addition to shape and length measurements of each census tract for data joining purposes.STATE (10) - Federal Information Processing Standards (FIPS)COUNTY (10) - Federal Information Processing Standards (FIPS)STATE (10), COUNTY (10) - Federal Information Processing Standards (FIPS)TRACT (10) - Federal Information Processing Standards (FIPS)TRIBAL TRACT (10) - Federal Information Processing Standards (FIPS)ST ABBRV (10) - State AbbreviationShape_Length - Total length of the polygon's (census tract) perimeter, in the units used by the feature class' coordinate system.Shape_Area - Total area of the polygon's (census tract) in the units used by the feature class' coordinate system.Data Source: Series Information for 2020 Census 5-Digit ZIP Code Tabulation Area (ZCTA5) National TIGER/Line Shapefiles, Current Open Geospatial Consortium (OGC) Application Programming Interface (API) Census ZIP Code Tabulation Areas - OGC Features copy this link to embed it in OGC Compliant viewers. For more information, please visit: ZIP Code Tabulation Areas (ZCTAs)To Report Data Discrepancies Contact the Rhode Island Department of Health (RIDOH) GIS (mapping) OfficePlease Be Certain To --Provide a Brief Description of What the Discrepancy IsInclude Your, Name, Organization, Telephone NumberAttach the Complete .xlsx with the Discrepancy Highlighted

Dataset consists of csv files with sample languages identified by name and Glottocode. Coding for four sociolinguistic variables, as well as an overall "orientation type." Each file corresponds to a different method for coding languages employing multiple spatial orientation strategies, as described in the document coding.pdf.

Orientation type

land-sea = axis oriented orthogonal to the coast, based on opposition between landward (inland) and seaward (toward the coast), regardless of whether these terms reflect PAN *daya and *lahud

land-sea* = land-sea systems in which the land-sea opposition is indistinguishable from geophysical elevation

coastal = axis oriented parallel to the coast, often but not necessarily co-lexified with vertical up' anddown'

elevation = axis that distinguishes global or geophysical elevation with respect to deictic center

riverine = axis oriented parallel to the river, typically with secondary axis orientated orthogonal to river

cardinal = axis fixed according to conventions which do not vary with local geography (although they may be motivated by environmental factors such as wind and the sun)

Distribution

distributed

island

village

Economy

diversified

agriculture

subsistence

Geography

diversified

inland

coast

Terrain

mountainous

non-mountainous

Spatial weights matrices used in quantitative geography furnish maps with their individual latent eigenvectors, whose geographic distributions portray distinct spatial autocorrelation (SA) components. These polygon patterns on maps have specific meaning, partially in terms of geographic scale, which this article describes. The goal of this description is to enable spatial analysts to better understand and interpret these maps individually, as well as mixtures of them, when accounting for SA in a spatial analysis. Linear combinations of Moran eigenvector maps supply a powerful and relatively simple tool that can explain SA in regression residuals, with an ability to render reasonably accurate reproductions of empirical geographic distributions with or without the aid of substantive covariates. The focus of this article is positive SA, the most commonly encountered nature of autocorrelation in georeferenced data. The principal innovative contribution of this article is establishing a better clarification of what the synthetic SA variates extracted from spatial weights matrices epitomize with regard to global, regional, and local clusters of similar values on a map. This article shows that the Getis-Ord Gi* statistic provides a useful tool for classifying Moran eigenvector maps into these three qualitative categories, illustrating findings with a range of specimen geographic landscapes.

Click here to access the data directly from the Illinois State Geospatial Data Clearinghouse. These lidar data are processed Classified LAS 1.4 files, formatted to 2,117 individual 2500 ft x 2500 ft tiles; used to create Reflectance Images, 3D breaklines and hydro-flattened DEMs as necessary. Geographic Extent: Lake county, Illinois covering approximately 466 square miles. Dataset Description: WI Kenosha-Racine Counties and IL 4 County QL1 Lidar project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a derived nominal pulse spacing (NPS) of 1 point every 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, U.S Survey Feet and vertical datum of NAVD88 (GEOID12B), U.S. Survey Feet. Lidar data was delivered as processed Classified LAS 1.4 files, formatted to 2,117 individual 2500 ft x 2500 ft tiles, as tiled Reflectance Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema. Ground Conditions: Lidar was collected April-May 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Ayers established a total of 66 ground control points that were used to calibrate the lidar to known ground locations established throughout the WI Kenosha-Racine Counties and IL 4 County QL1 project area. An additional 195 independent accuracy checkpoints, 116 in Bare Earth and Urban landcovers (116 NVA points), 79 in Tall Grass and Brushland/Low Trees categories (79 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data. Users should be aware that temporal changes may have occurred since this dataset was collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of its limitations. Acknowledgement of the U.S. Geological Survey would be appreciated for products derived from these data. These LAS data files include all data points collected. No points have been removed or excluded. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The raw point cloud is of good quality and data passes Non-Vegetated Vertical Accuracy specifications.Link Source: Illinois Geospatial Data Clearinghouse

This file contains the data and code to reproduce the results from the paper "Reasoning over higher-order qualitative spatial relations via spatially explicit neural network". Instructions of running the code can be found at README.txt

Abstract of the paper: Qualitative spatial reasoning has been a core research topic in GIScience and AI for decades. It has been adopted in a wide range of applications such as wayfinding, question answering, and robotics. Most developed spatial inference engines use symbolic representation and reasoning, which focuses on small and densely connected data sets, and struggles to deal with noise and vagueness. However, with more sensors becoming available, reasoning over spatial relations on large-scale and noisy geospatial data sets requires more robust alternatives. This paper, therefore, proposes a subsymbolic approach using neural networks to facilitate qualitative spatial reasoning. More specifically, we focus on higher-order spatial relations as those have been largely ignored due to the binary nature of the underlying representations, e.g., knowledge graphs. We specifically explore the use of neural networks to reason over ternary projective relations such as between. We consider multiple types of spatial constraint, including higher-order relatedness and the conceptual neighborhood of ternary projective relations to make the proposed model spatially explicit. We introduce evaluating results demonstrating that the proposed spatially explicit method substantially outperforms existing baseline by about 20%.

Fundamental Base of Geographic Data of the Czech Republic (ZABAGED®) is a digital geographic model of the territory of the Czech Republic (ČR).

At present time the planimetric component of ZABAGED® consists of 134 feature types as settlements, communications, utility networks and pipelines, hydrography, administrative units and protected areas, vegetation and land cover, terrain relief and selected data about survey control points.

The features are represented by 2D vector spatial component and a descriptive component containing qualitative and quantitative information about features.

BackgroundGlobally, over 81 million people use e-cigarettes, and the majority of them are young adults. Using e-cigarettes causes different types of adverse health effects both in adults and elderly people. Over time, using e-cigarettes has detrimental consequences on lung function, brain development and numerous other illnesses.MethodsThis study employed a mixed-methods conducted between June and September 2023, comprising two phases: Geographical Information System (GIS) mapping of available e-cigarette point-of-sale (POS) locations and conducting 15 in-depth interviews (IDIs) with e-cigarette retailers, along with 5 key informant interviews (KIIs) involving tobacco control activists and policy experts. ArcGIS was employed for spatial analysis, creating distribution and type maps, and buffer and multi-buffer ring analyses were conducted to assess proximity to hospitals and academic institutions. Data analysis involved descriptive statistics for GIS mapping and qualitative analysis for interview transcripts, utilizing a priori codebook and thematic analysis.ResultsA total of 276 POS were mapped in the entire Dhaka city. About 55 POS were found within 100m distance from academic institutions in Dhaka city, which offers the easy accessibility of young generations to e-cigarettes. The younger generation is becoming the major target for e-cigarettes because of their alluring flavors, appealing looks, and variation in flavors. Sellers have been using different marketing tactics such as postering, offering discounts and using internet marketing on social media. Moreover, they try to convince the customers by saying that e-cigarettes are ‘not harmful’ or ‘less harmful’. However, retailers were mostly taking e-cigarettes from local wholesalers or distributors. Customers buy these products both from in-store and online services. Due to the absence of laws and regulations on e-cigarettes in Bangladesh, the availability, marketing, and selling of e-cigarettes are increasing alarmingly.ConclusionE-cigarette retail shops are mostly surrounded by academic institutions, and it is expanding. Besides, frequent exposure, easy accessibility, and tactful promotion encourage the younger generations to consume e-cigarettes. The government should take necessary control measures on manufacturing, storage, advertising, promotion, sponsorship, marketing, distribution, sale, import, and export in order to safeguard the health and safety of young and future generations.

• Automotive Geospatial Analytics Market Scope:

The report on Global Automotive Geospatial Analytics Market offers in-depth analysis on market trends, drivers, restraints, opportunities etc. Along with qualitative information, this report include the quantitative analysis of various segments in terms of market share, growth, opportunity analysis, market value, etc. for the forecast years. The Global automotive geospatial analytics Market is segmented on the basis of type, application, and geography.

The Worldwide market for Global Automotive Geospatial Analytics Market is expected to grow at a CAGR of roughly x.x% over the next ten years, and will reach US$ XX.X Mn in 2028, from US$ XX.X Mn in 2018, according to a new Market.us (Prudour Research) study. Read More

While clines in environmental tolerance and phenotypic plasticity along a single species' range have been reported repeatedly and are of special interest in the context of adaptation to environmental changes, we know little about their evolution. Recent empirical findings in ectotherms suggest that processes underlying dynamic species' ranges can give rise to spatial differences in environmental tolerance and phenotypic plasticity within species. We used individual-based simulations to investigate how plasticity and tolerance evolve in the course of three scenarios of species' range shifts and range expansions on environmental gradients. We found that regions of a species' range which experienced a longer history or larger extent of environmental change generally exhibited increased plasticity or tolerance. Such regions may be at the trailing edge when a species is tracking its ecological niche in space (e.g., in a climate change scenario) or at the front edge when a species expands into a new habitat (e.g., in an expansion/invasion scenario). Elevated tolerance and plasticity in the distribution center was detected when asymmetric environmental change (e.g., polar amplification) led to a range expansion. However, tolerance and plasticity clines were transient and slowly flattened out after range dynamics because of genetic assimilation.

AbstractEvery species experiences limits to its geographic distribution. Some evolutionary models predict that populations at range edges are less well-adapted to their local environments due to drift, expansion load, or swamping gene flow from the range interior. Alternatively, populations near range edges might be uniquely adapted to marginal environments. In this study, we use a database of transplant studies that quantify performance at broad geographic scales to test how local adaptation, site quality, and population quality change from spatial and climatic range centers towards edges. We find that populations from poleward edges perform relatively poorly, both on average across sites (15% lower population quality) and when compared to other populations at home (31% relative fitness disadvantage), consistent with these populations harboring high genetic load. Populations from equatorial edges also perform poorly on average (18% lower population quality) but, in contrast, outperform foreign populations (16% relative fitness advantage), suggesting that populations from equatorial edges have adapted to unique environments. Finally, we find that populations from sites that are thermally extreme relative to the species' niche demonstrate strong local adaptation, regardless of geographic position. Our findings indicate that both nonadaptive processes and adaptive evolution contribute to variation in adaptation across species' ranges., MethodsThis dataset contains fitness data gathered from a systematic literature search of transplant experiments, along with geographic and climatic covariates derived for this study. Included is the final data file and model running scripts, as well as scripts, GBIF occurrence data, and intermediate files demonstrating how spatial and climatic predictors were calculated., Usage notesSee README file.

Educational campaigning has received little attention in the literature. This study investigates long-term and organised urban campaigns that are collectively lobbying the Victorian State Government in Australia, for a new public high school to be constructed in their suburb. A public high school is also known as a state school, government school, or an ordinary comprehensive school. It receives the majority of its funding from the State and Federal Australian Government, and is generally regarded as ‘free’ education, in comparison to a private school. Whilst the campaigners frame their requests as for a ‘public school’, their primary appeal is for a local school in their community. This study questions how collective campaigning for a locale-specific public school is influenced by geography, class and identity. In order to explore these campaigns, I draw on formative studies of middle-class school choice from an Australian and United Kingdom perspective (Campbell, Proctor, & Sherington, 2009; Reay, Crozier, & James, 2011). To think about the role of geography and space in these processes of choice, I look to apply Harvey’s (1973) theory of absolute, relational and relative space. I use Bourdieu (1999b) as a sociological lens that is attentive to “site effects” and it is through this lens that I think about class as a “collection of properties” (Bourdieu, 1984, p. 106), actualised via mechanisms of identity and representation (Hall, 1996; Rose, 1996a, 1996b). This study redresses three distinct gaps in the literature: first, I focus attention on a contemporary middle-class choice strategy—that is, collective campaigning for a public school. Research within this field is significantly under-developed, despite this choice strategy being on the rise. Second, previous research argues that certain middle-class choosers regard the local public school as “inferior” in some way (Reay, et al., 2011, p. 111), merely acting as a “safety net” (Campbell, et al., 2009, p. 5) and connected to the working-class chooser (Reay & Ball, 1997). The campaigners are characteristic of the middle-class school chooser, but they are purposefully and strategically seeking out the local public school. Therefore, this study looks to build on work by Reay, et al. (2011) in thinking about “against-the-grain school choice”, specifically within the Australian context. Third, this study uses visual and graphic methods in order to examine the influence of geography in the education market (Taylor, 2001). I see the visualisation of space and schooling that I offer in this dissertation as a key theoretical contribution of this study. I draw on a number of data sets, both qualitative and quantitative, to explore the research questions. I interviewed campaigners and attended campaign meetings as participant observer; I collected statistical data from fifteen different suburbs and schools, and conducted comparative analyses of each. These analyses are displayed by using visual graphs. This study uses maps created by a professional graphic designer and photographs by a professional photographer; I draw on publications by the campaigners themselves, such as surveys, reports and social media; but also, interviews with campaigners that are published in local or state newspapers. The multiple data sets enable an immersive and rich graphic ethnography. This study contributes by building on understandings of how particular sociological cohorts of choosers are engaging with, and choosing, the urban public school in Australia. It is relevant for policy making, in that it comes at a time of increasing privatisation and a move toward independent public schools. This study identifies cohorts of choosers that are employing individual and collective political strategies to obtain a specific school, and it identifies this cohort via explicit class-based characteristics and their school choice behaviours. I look to use fresh theoretical and methodological approaches that emphasise space and geography, theorising geo-identity and the pseudo-private school.

BackgroundGlobally, over 81 million people use e-cigarettes, and the majority of them are young adults. Using e-cigarettes causes different types of adverse health effects both in adults and elderly people. Over time, using e-cigarettes has detrimental consequences on lung function, brain development and numerous other illnesses.MethodsThis study employed a mixed-methods conducted between June and September 2023, comprising two phases: Geographical Information System (GIS) mapping of available e-cigarette point-of-sale (POS) locations and conducting 15 in-depth interviews (IDIs) with e-cigarette retailers, along with 5 key informant interviews (KIIs) involving tobacco control activists and policy experts. ArcGIS was employed for spatial analysis, creating distribution and type maps, and buffer and multi-buffer ring analyses were conducted to assess proximity to hospitals and academic institutions. Data analysis involved descriptive statistics for GIS mapping and qualitative analysis for interview transcripts, utilizing a priori codebook and thematic analysis.ResultsA total of 276 POS were mapped in the entire Dhaka city. About 55 POS were found within 100m distance from academic institutions in Dhaka city, which offers the easy accessibility of young generations to e-cigarettes. The younger generation is becoming the major target for e-cigarettes because of their alluring flavors, appealing looks, and variation in flavors. Sellers have been using different marketing tactics such as postering, offering discounts and using internet marketing on social media. Moreover, they try to convince the customers by saying that e-cigarettes are ‘not harmful’ or ‘less harmful’. However, retailers were mostly taking e-cigarettes from local wholesalers or distributors. Customers buy these products both from in-store and online services. Due to the absence of laws and regulations on e-cigarettes in Bangladesh, the availability, marketing, and selling of e-cigarettes are increasing alarmingly.ConclusionE-cigarette retail shops are mostly surrounded by academic institutions, and it is expanding. Besides, frequent exposure, easy accessibility, and tactful promotion encourage the younger generations to consume e-cigarettes. The government should take necessary control measures on manufacturing, storage, advertising, promotion, sponsorship, marketing, distribution, sale, import, and export in order to safeguard the health and safety of young and future generations.

This folder provides the exemplar data and codes for the manuscript `Location retrieval using qualitative place signatures of visible landmarks' submitted to the 'International Journal of Geographical Information Science'. It should be read in conjunction with the main manuscript.