Curated geospatial workforce literature

Transition from geospatial data to geospatial knowledge: analysis of the conceptual apparatus of current Russian standards and modern domestic scientific discourse

E.K. Litvintseva · Geodesy and Cartography · Jun 2025

The article deals with the study of transformation processes in the view of using geospatial information in Russia. Attention is focused on the problems of legal regulation in the field of geospatial data and knowledge. The author analyzes Russian state standards (GOSTs), determines the levels of their coherence and identifies contradictions in terminology. The study is based on the theory and practice of transition from simple geodata to full-fledged geospatial knowledge necessary for effective territorial management and decision-making. A gradation of the certainty of terms that helps to identify weak points in the existing regulatory framework is proposed. A conclusion is made on the need to modernize the standards and bring them closer to the requirements of the digital economy, which can increase the abilities of the state and accelerate the pace of its development. The results of the study are relevant for a wide range of specialists involved in the management of geospatial information and its legal support

Enhancing the interpretability of port economic modeling via implicit spatial relationship discovery

Xiaoli Cai, Sheng Wu, Peixiao Wang et al. · International Journal of Applied Earth Observation and Geoinformation · Feb 2026

Geodemography: Geospatial Stream of Demographic Analysis

Bishal Gnyawali, Uddhav Sigdel · Janajyoti Journal · Aug 2025

Geo-demography, an emergent subfield at the intersection of geography and demography, integrates spatialtechnologies such as Geographic Information Systems (GIS), remote sensing, and spatial statistics to enhancethe understanding of population dynamics within their spatial and socio-environmental contexts. Thiscomprehensive review critically examines the theoretical foundations, methodological innovations, analyticaltechniques, and diverse applications of geo-demography in urban planning, public health, market segmentation,and disaster management, with a regional focus on South Asia. Employing a qualitative meta-synthesisapproach and adhering to PRISMA guidelines, the study synthesizes 30 key scholarly works selected fromglobal academic databases. It highlights how geo-demography contributes to spatially informed and equitabledecision-making by revealing patterns in migration, urbanization, health disparities, and service accessibility.However, the review also identifies critical gaps, including inconsistent data quality, lack of methodologicalstandardization, limited application in rural and low-income settings, and ethical concerns related to privacy andsurveillance. It underscores the need for participatory approaches, interdisciplinary collaboration, and theintegration of critical geographic perspectives to address spatial inequalities and demographic vulnerabilities.Looking ahead, the review advocates for the incorporation of Artificial Intelligence (AI), real-time data, andethical data governance to expand the scope and impact of geo-demography in the Global South. Ultimately, thestudy positions geo-demography as a vital tool for sustainable development, digital governance, and inclusivepolicy-making in an increasingly spatially complex world.

Exploratory Assessment of Spatial Thinking among Geospatial Professionals in South Africa

Sanet Carow, Serena Coetzee, Peter Schmitz · South African Journal of Geomatics · Nov 2025

Spatial thinking is the cognitive skill of combining concepts of space, representation tools, and reasoning processes to solve complex spatial problems. Despite significant advances in geospatial technologies, individuals still lack this important skill, especially when working with maps and geospatial data. Spatial thinking abilities have been assessed for school learners and university students, but to our knowledge, not yet for those with work experience. A better understanding of the level of spatial thinking abilities in the geospatial industry can help us to understand whether experience and daily work with maps and geospatial data improve spatial thinking abilities. In this paper, we present the results of an exploratory study in which respondents from the geospatial industry of South Africa completed the spatial thinking ability test (STAT) developed by Lee and Bednarz (2012). Results indicate that gender, age, qualification level and discipline do not influence an individual’s spatial thinking ability. However, work experience and the type of work people engage with on a daily basis impact a person’s spatial thinking ability. Our results provide a baseline for further comparative studies, e.g., between the Global South and North, and also with other industries. In further research, the influence of other factors, e.g., dance, art and travel experience, on spatial thinking abilities should also be investigated. We encourage others to conduct similar studies so that the understanding of what develops spatial thinking abilities beyond a school and university education can be improved.

Adaptive simulated annealing for autonomous labour market delimitation

Jamintha Samarakoon, Helen Thompson, Edwin Lu et al. · Environment and Planning B: Urban Analytics and City Science · Mar 2026

Labour market areas (LMAs) are functional regions where job workers and employers interact to exchange labour services, playing a crucial role in regional economic analysis and policy-making. Accurate LMA definitions help ensure that regional boundaries reflect actual labour market dynamics. However, most existing methods for defining LMAs are susceptible to threshold values, which can vary significantly across regions due to differences in spatial and commuting interaction patterns. When region-specific thresholds are not well aligned with local commuting patterns, existing methods tend to generate unstable solutions, reflected in large variation in fitness values across algorithm repetitions and misallocations in which regions would perform better if placed in a neighbouring LMA, through stronger cohesion without a substantial loss in self-containment. This undermines the reliable identification of labour market structures. To address these challenges, we developed the Adaptive Simulated Annealing for Autonomous Labour Market Delimitation (AdSA-ALMD) algorithm. The method preserves labour market boundaries by using soft constraints to guide threshold selection, thereby reducing solution variance and minimising misallocations. Our method integrates simulated annealing with an adaptive cooling schedule, allowing efficient exploration of the solution space. Additionally, a penalty-based fitness function guides threshold adjustments, ensuring the resulting thresholds reflect the underlying commuting patterns. We validated our methodology using simulated data and micro-level journey-to-work data from the 2016 Census provided by the Australian Bureau of Statistics. Across both simulated and empirical applications, the method produced regionalisations with stronger cohesion without a substantial loss in self-containment, and fewer misallocations, relative to the Travel-to-Work Area algorithm, the Grouping Evolutionary Algorithm, and a recent Multi-step Approach. The use of soft constraints enables the approach to accommodate varying commuting structures without requiring region-specific tuning. Overall, the results demonstrate that AdSA-ALMD yields reproducible labour market delineations that reduce sensitivity to threshold specification while preserving alignment with observed commuting interactions.

A bivariate Wurman Dot approach to mapping job density and commuting inflows

Štefan Gábor · Environment and Planning B: Urban Analytics and City Science · Dec 2025

Commuting to work is a key form of everyday mobility. This study introduces a bivariate Wurman Dot method to visualise the relationship between commuting inflows and the Job Density Index (JDI) in Slovakia. Using 2021 Population and Housing Census data and a hexagonal grid, both indicators are visualised through a combination of symbol size and a bivariate colour scheme. The method reduces biases typical of choropleth and proportional-symbol maps by minimising shape heterogeneity, preventing symbol overlap, and enabling joint interpretation of absolute and relative labour-market indicators. The resulting visualisation reveals the hierarchy of employment centres, peripheral labour markets, and spatial mismatches between labour demand and commuting intensity, demonstrating the analytical value of the approach.

Using open geographic information science practices to create cascading improvements in research quality and to support discovery

Joseph Holler, Peter Kedron, Derrick Burt et al. · Cartography and Geographic Information Science · Dec 2025

DIGITAL ACTIVITY LOCATION CLUSTERING BASED ON TWITTER GEOSPATIAL DATA FOR SPATIOTEMPORAL BUSINESS INTELLIGENCE

Triyan Agung Laksono, Widyastuti Andriyani, Fadhlih Girindra Putra et al. · Journal of Intelligent Software Systems · Jul 2025

This research develops an approach for clustering digital activity locations based on Twitter geospatial data with the aim of supporting business intelligence spatiotemporal . By utilizing the Twitter Geospatial Data dataset containing more than 14 million tweets geo-tagged from the United States, this study implements and compares the DBSCAN and K- Means algorithms to identify spatial and temporal patterns of Twitter user activity. The research process begins with the data pre -processing stage using the Knowledge Discovery Database (KDD), followed by the implementation of the clustering algorithm , and ending with the integration of the results into the dashboard.business intelligence using Power BI . The results show that DBSCAN is able to detect irregular clusters that follow geographic patterns and population density, while K- Means produces a division of the region into three main clusters (West Coast, Central Region, and East Coast) with different temporal activity patterns. Integration of clustering results into a BI dashboard produces actionable business insights , such as identification of digital activity hotspots , optimal time for content delivery, geographic segmentation for marketing strategies, and temporal activity patterns for campaign scheduling. This research contributes to the development of an integrated spatiotemporal analysis pipeline to support data-driven decision making.