Geographic Information Science Research Articles

Quantitative monitoring of pollution in Mitrovica Lake using empirical model and geographic information science & systems

ABSTRACT Continuous monitoring of water is crucial for assessing its quality. This paper aims to evaluate water quality in the Artificial Lake of Mitrovica, an area known for heavy metal pollution due to nearby industrial activities. To achieve this, we conducted a comprehensive year-long investigation of the lake, involving the assessment of water quality, spatial analysis, and pollutant evaluation at six different sampling locations, collected in April, June, and September to ensure consistent monitoring. We utilised atomic absorption spectroscopy (AAS) to measure concentrations of arsenic, chromium, copper, nickel, lead, zinc, and cadmium. Indices such as the Heavy Metal Pollution Index (HPI) and Metal Index (MI) were used to assess water quality. Additionally, spatial analysis was conducted using QGIS and Ordinary Kriging to visualise contamination levels across the study area. While many water parameters met international standards, certain sites, particularly those with lead concentrations (Pb2+), exceeded permissible limits, indicating areas of potential concern. This study offers valuable insights for policymaking at both local levels in Mitrovica as well as nationally in Kosova. The study overall emphasises the importance of addressing heavy metal pollution in the region.

Assessing publication trends in selected GIScience journals

This study examines recent publishing trends in geographic information science (GIScience) journals. The study considers citable items (research articles, reviews) in 24 selected journals, and assesses them for the period between 2018 and 2023. The study begins by providing an overview of GIScience journal publishing trends, including growth in absolute numbers and the proportion in which they disseminate research as open access (OA). The geographic footprint of GIScience authors and international collaboration are also explored. Journals and their impact, reputation, and perceived quality are assessed using common journal metrics, including impact factor, 5-year impact factor, CiteScore, source normalized impact per paper (SNIP), SCImago Journal Rank (SJR), immediacy index, article influence score and normalized Eigenfactor. A composite index consisting of all these metrics from 2022 is established. The paper finds that GIScience is increasingly disseminated as OA. There is evidence that the Global South is underrepresented in GIScience, which necessitates greater inclusion and support in these regions. Finally, the assessment of journal metrics helps researchers identify prominent GIScience journals for disseminating their work effectively. This paper contributes to the knowledge and understanding of recent publishing trends in GIScience, offering valuable insights to researchers, practitioners and journal editors.

A Novel Address-Matching Framework Based on Region Proposal

Geocoding is a fundamental component of geographic information science that plays a crucial role in various geographical studies and applications involving text data. Current mainstream geocoding methods fall into two categories: geodesic-grid prediction and address matching. However, the geodesic-grid-prediction method’s localization accuracy is hindered by the density of grid partitioning, struggling to strike a balance between prediction accuracy and grid density. Address-matching methods mainly focus on the semantics of query text. However, they tend to ignore keyword information that can be used to distinguish candidates and introduce potential interference, which reduces matching accuracy. Inspired by the human map-usage process, we propose a two-stage address-matching approach that integrates geodesic-grid prediction and text-matching models. Initially, a multi-level text-classification model is used to generate a retrieval region proposal for an input query text. Subsequently, we search for the most relevant point of interest (POI) within the region-proposal area using a semantics-based text-retrieval model. We evaluated the proposed method using POI data from the Beijing Chaoyang District. The experimental results indicate that the proposed method provides high address-matching accuracy, increasing Recall@1 by 0.55 to 1.56 percentage points and MRR@5 by 0.54 to 1.68 percentage points.

Regional Prediction of Crop Yield Success Rate in the Philippines using Geographic Trend Analysis Algorithm

Agriculture holds an important role in the economy of the Philippines by ensuring the food security of domestic products. It involves about 40 % of Filipino workers and it contributes an average of 20 percent to the Gross Domestic Product. Crop prediction in the Philippine’s agriculture is a big problem. At the present time, farmers are having a difficult time in choosing the right crops due to unnatural climate changes, soil type, rainfall, and other environmental factors. These can affect the economic life of farmers and can make them less familiar in forecasting future crops. This project aims to guide the farmers for sowing the reasonable crops by using geographic trend analysis algorithm. The findings from this study demonstrate the potential of the Geographic Trend Analysis Algorithm as a powerful tool for regional crop yield prediction. The success rate of each region will always equate to 100% in context with the set optimal yield prediction where the crop with highest percentage appears to be the dominant one. Irrigated palay (rice) demonstrates a greater rate of success across diverse Philippine regions when contrasted with rainfed or non-irrigated palay, as well as white and yellow corn. Additionally, the methodology employed by the researchers sets the groundwork for upcoming investigations in the domain of agricultural data analytics and geographic information science. This paves the way for a data-centered and sustainable strategy toward crop production in the Philippines.

Spatio-Temporal Changes and Driving Forces Analysis of Urban Open Spaces in Shanghai between 1980 and 2020: An Integrated Geospatial Approach

Although there is extensive research demonstrating the significant loss and fragmentation of urban spaces caused by rapid urbanization, to date, no empirical research in Shanghai has investigated the spatiotemporal dynamics of urban open spaces using a comprehensive set of integrated geospatial techniques based on long-sequence time series. Based on the Google Earth Engine (GEE) platform and using the Random Forest (RF) classifier, multiple techniques, namely landscape metrics, trend analysis, open space ratio, transition matrix, Normalized Difference Vegetation Index (NDVI), and fractal dimension analysis, were applied to analyze the Landsat satellite data. Next, Geographic Detector (GeoDetector) methods were used to investigate the driving forces of such spatial variations. The results showed that (1) the RF classification algorithm, supported by the GEE, can accurately and quickly obtain a research object dataset, and that calculating the optimal spatial grain size for open space pattern was 70 m; (2) open spaces exhibited declining and contracting trends; and open spaces in the city experienced a decline from 91.83% in 1980 to 69.63% in 2020. Meanwhile, the degree of open spaces in each district increased to different extents, whilst connectivity markedly decreased. Furthermore, the open space of city center districts showed the lowest rate of decrease, with open space patterns fragmenting due to encroaching urbanization; (3) the contribution of socioeconomic factors to the spatial–temporal changes in open space continually has increased over the past 40 years, and were also higher than natural geographic factors to some extent. Apart from offering policy insights guiding the future spatial planning and development of the city, this paper has contributions from both methodological and empirical perspectives. Based on integrated remote sensing and geographic information science (GIS) techniques, this paper provides updated evidence and a clearer understanding of the spatiotemporal variations in urban spaces and their influencing mechanisms in Shanghai.

Dynamic map‐making technology and the application in geographic information science

AbstractThe dynamic map is applied instead of traditional static maps in the teaching of geographic information science (GIS)‐related majors so as to meet the need for teaching animations and teaching interaction in new teaching methods such as mobile personalized learning, massive open online course, and Flipped Classroom. The research introduces the concept and the making tools of dynamic maps, studies the making principles and key technologies of dynamic maps, and constructs the relevant theories such as the “frame” model based on time granularity, the dynamic interpolation algorithm based on spatial characteristics, dynamic symbolic logic model, and algorithm. Based on the above principles and theories, taking a dynamic map of the change in the population of each city in Shandong province as an example, the research demonstrates the making method and process of the dynamic maps for teaching with Flash MX. At last, it analyzes the role of dynamic map in GIS‐related courses teaching and evaluates the teaching effect of dynamic maps.

Ensuring timely completion and successful thesis outcomes: a case study of an online GIS master’s degree program

ABSTRACT Master of Science (M.S.) programs, including geography through geographic information science and technology (M.S. GIST), play a key part in training the science, technology, engineering, and math (STEM) workforce. A master’s thesis has been a traditional part of geography programs, and across all kinds of disciplines the value of a thesis is well-recognized. However, delivering master’s thesis supervision for large cohorts may result in lengthy completion times and poor completion rates. A growing literature focuses on master’s thesis supervision across a wide array of disciplines, but no study focuses on the master’s thesis for M.S. GIST programs. We offer a mixed-methods case study of a large, online M.S. GIST program showing how problems developed and were resolved over a 13-year period with program-wide structural reforms. Reform elements included increased attention to writing skills in admissions review, addition of writing instruction, earlier student engagement, staff engagement, faculty engagement, and development of thesis courses. Reforms shortened completion times and increased thesis completion rates while sustaining diversity and quality of thesis projects. Although this case study echoes many strategies found in the literature, it emphasizes that replication likely requires linking solutions together through structural reforms across a master’s degree program.

Building Detection from SkySat Images with Transfer Learning: a Case Study over Ankara

AbstractThe detection and continuous updating of buildings in geodatabases has long been a major research area in geographic information science and is an important theme for national mapping agencies. Advancements in machine learning techniques, particularly state-of-the-art deep learning (DL) models, offer promising solutions for extracting and modeling building rooftops from images. However, tasks such as automatic labelling of learning data and the generalizability of models remain challenging. In this study, we assessed the sensor and geographic area adaptation capabilities of a pretrained DL model implemented in the ArcGIS environment using very-high-resolution (50 cm) SkySat imagery. The model was trained for digitizing building footprints via Mask R‑CNN with a ResNet50 backbone using aerial and satellite images from parts of the USA. Here, we utilized images from three different SkySat satellites with various acquisition dates and off-nadir angles and refined the pretrained model using small numbers of buildings as training data (5–53 buildings) over Ankara. We evaluated the buildings in areas with different characteristics, such as urban transformation, slums, regular, and obtained high accuracies with F‑1 scores of 0.92, 0.94, and 0.96 from SkySat 4, 7, and 17, respectively. The study findings showed that the DL model has high transfer learning capability for Ankara using only a few buildings and that the recent SkySat satellites demonstrate superior image quality.

An urban cellular automata model based on a spatiotemporal non-stationary neighborhood

Spatiotemporal modeling has long been a major concern of geographic information science. Even though previous research has shown the importance of temporal and spatial information in quantifying the neighborhood effects of urban cellular automata (CA) models, constructing a spatiotemporal non-stationary neighborhood remains a challenge, due to the complexity of the spatiotemporal models. In this study, we introduced spatiotemporal modeling into the neighborhood of an urban CA model and constructed a geographically and temporally weighted neighborhood (GTWN). A corresponding approach to optimizing the bandwidth of the GTWN was also developed. Taking Beijing and Wuhan in China as examples, the GTWN-CA model was employed to simulate their urban expansion. The experimental results indicate that the GTWN-CA model has a better and performance than other CA models whose neighborhood is constructed based on the assumption of temporal or spatial stationarity, highlighting the advantages of spatiotemporal modeling in quantifying the neighborhood effect. Compared with the commonly used CA model with a homogeneous neighborhood (HON-CA), in terms of the figure of merit (FoM), the calibration accuracy of the GTWN-CA model was improved by 0.87% in Beijing and 5.4% in Wuhan, and the validation accuracy was improved by 7.9% in Beijing and 8.9% in Wuhan.

Understanding route choice behaviors' impact on traffic throughput in a dynamic transportation network

Route planning is one of the most important and attractive topics in complex networks, geographical information science (GIS) and logistics. Travelers' route choice behaviors may affect the actual throughput of a transportation network. This study aims to analyze the influence of travelers' route choice behaviors on network traffic throughput. Two kinds of route choice behaviors, called continuous optimization behavior and hybrid optimization behavior, are defined. Based on this, two traffic scenarios are constructed to describe these behaviors and examine their effect on the network's actual traffic throughput. The application of our approach to urban street networks, Barabási-Albert scale-free network and Erdös-Rényi random network demonstrates that if all travelers choose optimal paths constraint with time from the start, network throughput is insensitive to the continuous optimization behavior but depends on the relationship between time and distance in the routing algorithm. Besides, network throughput is sensitive to the hybrid optimization behavior that the more travelers choose time-dependent optimal paths, the higher the throughput. The results indicate that the choice of initial paths and the time dependence of routing algorithms can directly affect the network's actual throughput which is enhanced >50 % when the time dependence parameter is >0.2 or the proportion of time-dependent shortest paths in initial paths is >0.2. Our findings provide supports for exploring efficient routing algorithms to enhance the throughput of urban street networks and other kinds of networks.

Measuring the similarity between shapes of buildings using graph edit distance

ABSTRACT Building data play a crucial role in geographic information science, and building shape similarity can be used for spatial inquiry, cartographic generalization, map updating, data quality assessment, and other spatial data mining applications. However, conventional shape representation and measurement methods use geometric and statistical measures and may not effectively capture the subtle differences among building shapes. Geospatial artificial intelligence methods often overlook nuanced differences in building contours, posing a challenge when precise fine-grained measurements are required. Furthermore, they have low interpretability. This study proposes a graph edit distance-based method for measuring the similarity between building shapes. It uses geometrics characteristics to construct a graph, matches and associates similar regional features between two building contours, and defines edit costs based on geometric and positional differences that align with the Gestalt principle of visual similarity. It converts the optimal edit path problem to the maximum weighted cluster solution problem and utilizes a heuristic approach to achieve the exact optimal solution within a moderately suitable timeframe. The experimental results demonstrated that the proposed method identifies detailed differences between building contours, providing accurate similarity measurement with high interpretability.

Examining the impact of head‐mounted augmented reality on learning engineering drawings: A case study for three‐view drawing

AbstractAs the cornerstone of spatial geometry and engineering drawings, three‐view drawing lays the necessary foundation for students to learn geography, graphics, industrial design, geographic information science, and so forth. However, teaching three‐view drawing can be challenging due to its abstraction. Currently, desktop and handheld augmented reality (AR) systems are widely applied to three‐view teaching. However, existing systems suffer from visuo‐tactile inconsistency and contain unfriendly interactions, resulting in a poor learning experience. In this paper, head‐mounted AR is introduced for the first time in the teaching of three‐view drawing. A system was designed for learning three‐view drawing to facilitate students' free exploration and learning in lessons. Additionally, a comparative experiment was designed and conducted to investigate whether multimodal interaction and head‐mounted AR help students to better understand the relationship between concrete three‐dimensional (3D) objects and abstract two‐dimensional planes and the impact on subjective perceptions. The results demonstrated that the proposed system significantly improved learning performance. Furthermore, the proposed system heightened students' interest in learning, made reproducing 3D structures more intuitive, and students were more willing to use such systems in their future studies.

A method for finding a maximum value region with a minimum width in raster space

Given a grid of cells, each of which is assigned a numerical value quantifying its suitability for a certain use, one problem in geographic information science concerns the selection of a region, i.e. a connected set of cells, with a specified size that maximizes the sum of all their values. This task can be cast as a combinatorial optimization problem called the maximum value region problem, and exact and heuristic methods exist for its solution. While those solutions are guaranteed to be feasible (if not optimal), they may not be desirable for practical use if they contain too narrow segments (down to the width of a single cell). In this paper, we present a new variation of the maximum value region problem—the maximum value wide region problem—that requires a region to be at least as wide as a specified width. We offer a heuristic method for its solution which models a region as a set of neighborhoods and test its performance through computational experiments. Results demonstrate that the method generates good feasible solutions in terms of connectedness, size, width, and value, but requires more computing time than methods for maximum value regions without minimum width requirements.

Semantic-Enhanced Graph Convolutional Neural Networks for Multi-Scale Urban Functional-Feature Identification Based on Human Mobility

Precise identification of spatial unit functional features in the city is a pre-condition for urban planning and policy-making. However, inferring unknown attributes of urban spatial units from data mining of spatial interaction remains a challenge in geographic information science. Although neural-network approaches have been widely applied to this field, urban dynamics, spatial semantics, and their relationship with urban functional features have not been deeply discussed. To this end, we proposed semantic-enhanced graph convolutional neural networks (GCNNs) to facilitate the multi-scale embedding of urban spatial units, based on which the identification of urban land use is achieved by leveraging the characteristics of human mobility extracted from the largest mobile phone datasets to date. Given the heterogeneity of multi-modal spatial data, we introduced the combination of a systematic data-alignment method and a generative feature-fusion method for the robust construction of heterogeneous graphs, providing an adaptive solution to improve GCNNs’ performance in node-classification tasks. Our work explicitly examined the scale effect on GCNN backbones, for the first time. The results prove that large-scale tasks are more sensitive to the directionality of spatial interaction, and small-scale tasks are more sensitive to the adjacency of spatial interaction. Quantitative experiments conducted in Shenzhen demonstrate the superior performance of our proposed framework compared to state-of-the-art methods. The best accuracy is achieved by the inductive GraphSAGE model at the scale of 250 m, exceeding the baseline by 25.4%. Furthermore, we innovatively explained the role of spatial-interaction factors in the identification of urban land use through the deep learning method.

Assessment of heavy metals at mangrove ecosystem, applying multiple approaches using in-situ and remote sensing techniques, Red Sea, Egypt.

Mangrove areas are considered the most retention zone for heavy metal pollution as it work as an edge that aggregates land and sea sediments. This study aims to examine if the heavy metals' existence in the mangrove sediment is related to contamination or natural resources. In addition, it gives an interpretation of the origin of these metals along the Egyptian Red Sea coast. Twenty-two samples of mangrove sediments were collected and then, analyzed for metals (Mn, Ni, Cu, Fe, Cd, Ag, and Pb) using inductively coupled plasma mass spectroscopy (ICP-MS). Integration between the in-situ data, contamination indices, and remote sensing and geographical information science (GIS), and multivariate statistical analysis techniques (PCA) were analyzed to assess and clarify the spatial origin of heavy metals in sediment at a regional scale. The average concentration of heavy metals from mangrove sediments were shown to be substantially lower than the referenced value, ranging from moderate to significant except the levels of Ag were very high. The heavy metals concentrations were expected to be naturally origin rather than anthropogenic and that be confirmed by mapping of Red Sea alteration zones spots. These alteration zones are parallel to mangrove sites and rich by several mineralization types including heavy metals that are carried by flooding to the coastline. Remote sensing and GIS techniques successfully contributed to interpreting the pattern of the origin of heavy metals and discharging systems that control the heavy metals concentration along the Red Sea coast.

Robust Variable Selection with Exponential Squared Loss for the Spatial Error Model

With the widespread application of spatial data in fields like econometrics and geographic information science, the methods to enhance the robustness of spatial econometric model estimation and variable selection have become a central focus of research. In the context of the spatial error model (SEM), this paper introduces a variable selection method based on exponential square loss and the adaptive lasso penalty. Due to the non-convex and non-differentiable nature of this proposed method, convex programming is not applicable for its solution. We develop a block coordinate descent algorithm, decompose the exponential square component into the difference of two convex functions, and utilize the CCCP algorithm in combination with parabolic interpolation for optimizing problem-solving. Numerical simulations demonstrate that neglecting the spatial effects of error terms can lead to reduced accuracy in selecting zero coefficients in SEM. The proposed method demonstrates robustness even when noise is present in the observed values and when the spatial weights matrix is inaccurate. Finally, we apply the model to the Boston housing dataset.

Evaluation of a Targeted COVID-19 Community Outreach Intervention: Case Report for Precision Public Health.

Cameron County, a low-income south Texas-Mexico border county marked by severe health disparities, was consistently among the top counties with the highest COVID-19 mortality in Texas at the onset of the pandemic. The disparity in COVID-19 burden within Texas counties revealed the need for effective interventions to address the specific needs of local health departments and their communities. Publicly available COVID-19 surveillance data were not sufficiently timely or granular to deliver such targeted interventions. An agency-academic collaboration in Cameron used novel geographic information science methods to produce granular COVID-19 surveillance data. These data were used to strategically target an educational outreach intervention named "Boots on the Ground" (BOG) in the City of Brownsville (COB). This study aimed to evaluate the impact of a spatially targeted community intervention on daily COVID-19 test counts. The agency-academic collaboration between the COB and UTHealth Houston led to the creation of weekly COVID-19 epidemiological reports at the census tract level. These reports guided the selection of census tracts to deliver targeted BOG between April 21 and June 8, 2020. Recordkeeping of the targeted BOG tracts and the intervention dates, along with COVID-19 daily testing counts per census tract, provided data for intervention evaluation. An interrupted time series design was used to evaluate the impact on COVID-19 test counts 2 weeks before and after targeted BOG. A piecewise Poisson regression analysis was used to quantify the slope (sustained) and intercept (immediate) change between pre- and post-BOG COVID-19 daily test count trends. Additional analysis of COB tracts that did not receive targeted BOG was conducted for comparison purposes. During the intervention period, 18 of the 48 COB census tracts received targeted BOG. Among these, a significant change in the slope between pre- and post-BOG daily test counts was observed in 5 tracts, 80% (n=4) of which had a positive slope change. A positive slope change implied a significant increase in daily COVID-19 test counts 2 weeks after targeted BOG compared to the testing trend observed 2 weeks before intervention. In an additional analysis of the 30 census tracts that did not receive targeted BOG, significant slope changes were observed in 10 tracts, of which positive slope changes were only observed in 20% (n=2). In summary, we found that BOG-targeted tracts had mostly positive daily COVID-19 test count slope changes, whereas untargeted tracts had mostly negative daily COVID-19 test count slope changes. Evaluation of spatially targeted community interventions is necessary to strengthen the evidence base of this important approach for local emergency preparedness. This report highlights how an academic-agency collaboration established and evaluated the impact of a real-time, targeted intervention delivering precision public health to a small community.

Geovisualization: Current Trends, Challenges, and Applications

Geovisualization (or Geographic Visualization) represents an interdisciplinary scientific field spanning cartography, geographic information science (GIScience) and technology, computer science and human–computer interaction (HCI), psychology, and cognitive science [...]

Quantifying the impacts of urbanization on urban green, evidences from Maga City, Lahore Pakistan

Globally, green spaces are considered as a fundamental part of biodiversity to balance the environment in an ecosystem particularly in urban areas. Contrary to this, urban green is declining in the least developing nations particularly due to rapid urbanization. This impact alter the condition of environmental services in term of non-provision and insufficient urban green spaces for the local population. This study takes Shahdara Town as an area of interest that is a congested urban area of Lahore Pakistan, and currently facing social injustice in terms of a dense urban area with few green spaces. The study uses Geographical Information Science for the identification of green spaces, calculation of their total area, and quantification of green cover extent by using NDVI, to comparing them with population to analyzed the environmental justice. To check the social perspective of the population regarding green areas and their importance among urban people, a comparison has been made with the presence of parks in the area, their association with living conditions and quality of life of locals. This social prospective was investigated by applying the Multiple Linear Regression Model (MLRM) by taking the availability of park/urban green in area as dependent variable and accessibility to parks, its total distance from a house, and frequency of visit as independent variables. The resultant analysis shows that according to international standards, per capita green spaces are less in this town, of Lahore. This imbalance provision of environmental services is a challenging task, for policymakers and a hurdle for urban sustainability. Finally, the outcomes of the research provide a documented base evidence and social analysis for environmental sustainability in terms of provisions of environmental services for the urban dwellers in the region.

A Computational Geospatial Approach to Assessing Land-Use Compatibility in Urban Planning

Amidst rapid urbanization, sustainable development requires moving beyond subjective land-use planning techniques toward innovative computational geospatial models. This paper introduces a GIS-based quantitative framework to enable objective, rigorous land-use compatibility analysis. Uniquely, the model evaluates radial impacts and expert-defined criteria across multiple scales, overcoming the limitations of qualitative approaches. Cell-by-cell computation identifies emerging spatial conflicts with enhanced realism. A case study in Qaemshahr, Iran, demonstrated the model’s proficiency in revealing incompatibilities and hotspots, surpassing conventional methodologies. Quantitative analysis provided accurate, transparent insights for evidence-based planning and consistency in evaluation. Ongoing improvements through 3D, real-time data integration and machine learning will further the objectivity. While extensive testing across diverse urban contexts is still needed, this pioneering computational technique marks a transition from subjective to objective methodologies. Situated at the intersection of geographic information science and urban planning, this study serves as a launchpad for advancing robust geospatial models to shape more equitable, resilient urban futures amidst complex sustainability challenges. The development of rigorous computational techniques remains fundamental, and the present innovative model can be used to provide objective, scientifically grounded compatibility analyses to guide land-use planning.