Geographic Information Science Research Articles

Multiple Watermarking Algorithms for Vector Geographic Data Based on Multiple Quantization Index Modulation

Multiple digital watermarking is an important and challenging task in geographic information science and data security. Vector geographic data are a basic data format for digital geographic data storage, and the security protection of these data involves copyright protection and tracking. As part of the solution, existing digital watermarking algorithms have made contributions to vector geographic data protection. However, when vector geographic data flow through multiple units, they need to be marked to ensure that the original data are not destroyed during data processing. Existing single or multiple data watermarking algorithms often fail in the presence of data processing because the new watermarks overlay the old ones. Consequently, a multiple digital watermarking algorithm based on multiple QIM (quantization index modulation) is proposed. First, based on traditional quantization index modulation (QIM), a multiple QIM is proposed. Unlike traditional QIM, in multiple QIM, the process of quantization is executed multiple times depending on the number of watermarks. Then, the vertices are quantized into different quantization intervals according to the multiple QIM. Finally, multiple watermarks are embedded into different quantization intervals to reduce the interference among multiple watermarks, and the original watermarks are not needed in the process of watermark detection. We then conducted experiments to test the multiple watermark method’s robustness and capacity, with an emphasis on datasets with a lower data volume. The experimental results show that the proposed algorithm achieves good performance in terms of its robustness against common issues, such as vertices addition, data simplification, data cropping, and feature deletion; this holds true for both normal and small amounts of data. Additionally, it has a high multiple watermark capacity.

Artificial Intelligence and Geoinformation Systems

This article discusses modern aspects of the use of artificial intelligence (AI) in GIS. Integrating AI into GIS opens new opportunities and prospects for analyzing and processing geographic data. This paper examines the role of artificial intelligence in GIS and its impact on various aspects of geographic information science. The main tasks that AI can solve in GIS are described, such as automatic recognition of objects on the map, forecasting and modeling of geographic processes, optimization of routes and decision making. It also discusses various methods and types of artificial intelligence that are used in GIS, such as neural networks and machine learning. Examples of specific applications of AI in GIS are described, such as fire forecasting, terrain analysis to find resources, determining the optimal location of infrastructure, and others. The stages of using artificial intelligence to solve geoinformation problems are presented. The study shows that the use of artificial intelligence in geographic information systems contributes to more accurate and efficient analysis of geographic data, helps to identify hidden patterns, and improve decision making in various areas. Artificial intelligence is becoming an important tool for working with geodata and contributes to the development of more intelligent and automated GIS.

MultiLineStringNet: a deep neural network for linear feature set recognition

ABSTRACT Pattern recognition of linear feature sets, such as river networks, road networks, and contour clusters, is essential in cartography and geographic information science. Previous studies have investigated many methods to identify the patterns of linear feature sets; the key to each of these studies is to generate a reasonable and computable representation for each set. However, most existing methods are only designed for a specific task or data type and cannot provide a general solution for formalizing linear feature sets owing to their complex geometric characteristics, spatial relations and distributions. In addition, some methods require human involvement to specify characteristics, choose parameters, and determine the weights of different measures. To reduce human intervention and improve adaptability to various feature types, this paper proposes a novel deep learning architecture for learning the representations of linear feature sets. The presented model accepts vector data directly without extra data conversion and feature extraction. After generating local, neighborhood, and global representations of inputs, the representations are aggregated accordingly to perform pattern recognition tasks, including classification and segmentation. In the experiments, building groups classification and road interchanges segmentation achieved accuracies of 98% and 89%, respectively, indicating the model’s effectiveness and adaptability.

Perspectives on Advanced Technologies in Spatial Data Collection and Analysis

The motivation to organize this Special Issue originated from the observation of rapid changes taking place in the domain of geographical information science and systems over the past few decades [...]

Development of a composite model for predicting urban surface temperature distribution in the context of GIS

In this paper, we present a new model for simulating radiation heat transfer and surface temperatures in urban environments, which is a prerequisite for simulating outdoor mean radiant temperature for pedestrians. We propose a new model whose novelty lies in a new algorithm for simulating diffuse radiation by combining the Nusselt unit sphere method and Monte Carlo ray tracing algorithm. This combined use of different methods is automatically activated depending on the complexity of the scene and, in particular, the arrangement of the facets facing each other. The model is implemented in a Python-based tool, t4gpd, which combines the capabilities of geographic information science and related technologies (GIS &T) with efficient ray-casting solutions. The model is tested on a theoretical mock-up of nine virtual buildings in Nantes under summer and winter weather conditions and compared with SOLENE-Microclimat results. The view factor and solar radiation flux simulation results agree well with the reference solution, with a significant reduction in simulation computation time. However, the model has limitations due to the exclusion of vegetation evapotranspiration characteristics, wind profile model, and soil with complete stratigraphy. Overall, this new model provides an efficient tool for simulating outdoor pedestrian thermal comfort in urban environments and has potential for further development and validation.

Measuring the Influence of Multiscale Geographic Space on the Heterogeneity of Crime Distribution

Urban crimes are not homogeneously distributed but exhibit spatial heterogeneity across a range of spatial scales. Meanwhile, while geographic space shapes human activities, it is also closely related to multiscale characteristics. Previous studies have explored the influence of underlying geographic space on crime occurrence from the mechanistic perspective, treating geographic space as a collection of points or lines, neglecting the multiscale nature of the spatial heterogeneity of crime and underlying geographic space. Therefore, inspired by the recent concept of “living structure” in geographic information science, this study applied a multiscale analysis method to explore the association between underlying geographic space and crime distribution. Firstly, the multiscale heterogeneity is described while simultaneously considering both the statistical and geometrical characteristics. Then, the spatial association rule mining approach is adopted to quantitatively measure the association between crime occurrence and geographic space at multiple scales. Finally, the effectiveness of the proposed methods is evaluated by crime incidents in the city of Philadelphia. Experimental results show that crime heterogeneity is indeed closely related with the spatial scales. It is also proven that the influence of underlying geographic space on crime heterogeneity varies with the spatial scales. This study may enrich the methodology in crime pattern and crime explanation analysis, and it provides useful insights for effective crime prevention.

Environmental scanning of cocaine trafficking in Brazil: Evidence from geospatial intelligence and natural language processing methods

Cocaine trafficking threatens countries' national security and is a major public health challenge. Cocaine is transported from producer countries to consumer markets using various routes, methods, and transportation means. These routes develop in the geographical environment, are carefully planned and are geo-strategic objects that respond to the opportunities that drug trafficking organisations (DTOs) find to reduce the risks of interdiction. In this sense, individual drug seizure data (IDS) become essential indicators for identifying trends and understanding trafficking flows associated with drug trafficking routes. However, due to the illicit nature of DTOs, the availability of these data is considerably limited, hindering the ability to analyse and identify trends. This study presents a methodology for collecting and processing data from open-source information reported by Brazil's federal government news website. Using geospatial intelligence and natural language processing methods, we created a dataset with 939 records and 44 variables related to cocaine seizures in Brazil in 2022. We applied geospatial analysis techniques from this dataset to identify trends and potential cocaine trafficking flows. The results were broadly consistent with existing literature on drug trafficking. They demonstrated the potential of open-source information for environmental scanning and knowledge generation through geographic information science. The approach proposed in our research provides tools that can be used to complement drug trafficking monitoring and formulate public policies to strengthen prevention and enforcement strategies.

Place and place names: a unified model.

The goal of this paper is to offer a unified account of Place as a central theoretical notion across different disciplines. We show that while psychology, geography and other sciences have been converging to a unified view of this notion, linguistics still offers a fragmented perspective. Consequently, place names lack a full-fledged analysis that connects this category to the psychological concept of place. We propose to overcome this impasse by introducing a multi-modal Discourse Representation Theory (DRT) account of place as a conceptual construct and place concepts as specific instances of this construct. We show that current variants of DRT permit us to model place names and their senses, i.e., the meaning(s) that individuals associate with Sydney. We then model non-linguistic place concepts, i.e., the mental representation(s) that individuals can have of the city carrying this name. We present a model of the relation between linguistic meaning and conceptual content via the notion of anchoring relations applied to place. We pair this formal treatment with a morpho-syntactic account of place names building on current generative syntax treatments of proper names. Once we have a morpho-syntactic and semantic model of place names, we use a frame semantics treatment to account for lexical relations among place names. We test the overarching model on a set of recalcitrant problems afflicting current linguistic and multi-disciplinary treatments of place. These are the grammatical complexity and lexical content of place names, place concepts and their networks, and inter-subjective, communicative models of place in discourse. By solving these problems, our account integrates several frameworks (DRT, conceptual analysis, generative syntax, frame semantics) and connects several disciplines (linguistics, psychology, geographic information science, communication models) via a novel, multi-modal account of place. We conclude by discussing the theoretical and empirical import of these results.

DEVELOPMENT OF OCEAN RENEWABLE ENERGY MODEL IN INDONESIA TO SUPPORT ECO-FRIENDLY ENERGY

Abstract. Renewable energy is a solution for reducing environmental damage caused by greenhouse gas emissions from fossil fuels. Energy consumption in Indonesia involves sectors such as industry, households, transportation, and agriculture, which still heavily rely on non-renewable energy sources. Indonesia possesses significant maritime potential, boasting the second longest coastline in the world, a water area covering 71%, and abundant marine biological resources that can contribute to maritime economic growth. This research aims to leverage remote sensing technology and geographic information science to harness Indonesia's maritime potential. In the realm of renewable energy, this study emphasizes the potential of wave energy and current energy in Indonesia, with the objective of establishing an Ocean Renewable Energy (ORE) model. Additionally, the research will consider marine habitat suitability to mitigate any negative impact on biodiversity from power generation activities. Based on the research findings, the ocean current energy in Lombok Strait has a high energy potential reaching 1,035 Watts and Maluku Sea has 1,536 Watts, while the southwest region exhibits wave energy potential of Panaitan Island has a wave energy potential of 23,051 kW/m, while Sangiang Island has a potential of 12,842 kW/m. Furthermore, potential energy locations will be identified through the overlaying of potential fish zones. The ultimate goal of this research is to fulfill sustainable energy needs, decrease dependence on fossil fuels, and promote sustainable economic growth in Indonesia.

New steps against scientific gerrymandering: A new operational definition of geographic information science

Sometimes there are clear and natural limits to the scope of action of a science, and in other cases they are simply convenient ones. Geographic Information Science (GISc) is a transversal science, with contacts with all geosciences but also with various formal sciences such as Mathematics, Logic and Computer Science. A first approach to specifying the limits of a science is through its definition. Definitions of GISc are often so expansive that they have been rightly criticized for practicing gerrymandering, in particular with the rest of the geosciences. To avoid this, an operational definition is proposed that places GISc among the sciences that handle Data and not Information. This solves the gerrymandering problem without really implying a significant cut of what is usually considered within GISc. As an unforeseen consequence, this delimitation will allow it to be characterized as Formal Science, leaving it as the only geoscience with this characteristic.

A systematic literature review of applications of geographic information system in COVID-19 studies

Geographic information science (GIS) has established itself as a distinct domain and incredibly useful whenever the research is related to geography, space, and other spatio-temporal dimensions. However, the applications of GIS in the study of COVID-19 remained limited. In this systematic literature review, the current applications of GIS in COVID-19 studies were assesed. A total of 50 research articles related to applications of GIS were systematically retrieved and reviewed. There thematic groups were grouped, namely · The use of GIS for mapping COVID-19, · How to assess the impacts of the COVID-19 pandemic on socio-economies and environment, and · The assessment of the community vulnerability and risks during the pandemic. It was found that GIS shows great importance in the study of COVID-19. The wide range of applications offered by GIS could significantly improve how we fight the COVID pandemic.

BIM/IFC-based 3D spatial model for condominium ownership: A case study of China

ABSTRACT As the number of high-density buildings has increased, the management of property with complex condominium ownership has become an ongoing challenge in property registration and management. The three-dimensional (3D) modeling of condominium ownership has emerged as an effective means of meeting this challenge and has attracted great attention from fields such as geographical information science, urban planning and management, and property administration. Much progress has been made in building 3D models of condominium ownership; however, existing studies are all on a case-by-case basis and have left some critical issues unsolved, such as vague ownership boundaries, spatial rights without physical counterparts, and the unfixed spatial extent. The purpose of this study is to construct a 3D building ownership model with multiple levels of detail in the context of Chinese law to overcome the defects of 3D models above. This 3D model is presented in a case study of China by subdividing ownership boundaries based on clarifying the internal structure of condominium ownership, embedding the apportionment mechanism, and integrating the semantics, attributes, and geometry associated with the physical and legal entity of the condominium. The proposed 3D model is implemented by extending Building Information Modeling (BIM) based on the Industry Foundation Classes (IFC) and inheriting legal information from Land Administration Domain Model (LADM). In this study, examples of condominium ownership from three real legal dispute cases in China are analyzed and tested. The study clearly demonstrates that the proposed model can provide a better cognitive understanding of the legal space of property by rendering unambiguous ownership boundaries and presenting the spatial internal structure of ownership, which offers solid technical support for dealing with property registration and many legal dispute cases about condominium ownership.

Social media analysis reveals environmental injustices in Philadelphia urban parks

The United Nations Sustainable Development Goal (SDG) target 11.7 calls for access to safe and inclusive green spaces for all communities. Yet, historical residential segregation in the USA has resulted in poor quality urban parks near neighborhoods with primarily disadvantaged socioeconomic status groups, and an extensive park system that addresses the needs of primarily White middle-class residents. Here we center the voices of historically marginalized urban residents by using Natural Language Processing and Geographic Information Science to analyze a large dataset (n = 143,913) of Google Map reviews from 2011 to 2022 across 285 parks in the City of Philadelphia, USA. We find that parks in neighborhoods with a high number of residents from historically disadvantaged demographic groups are likely to receive lower scores on Google Maps. Physical characteristics of these parks based on aerial and satellite images and ancillary data corroborate the public perception of park quality. Topic modeling of park reviews reveal that the diverse environmental justice needs of historically marginalized communities must be met to reduce the uneven park quality—a goal in line with achieving SDG 11 by 2030.

Virtual ecological technology: Concepts, systems, and application perspectives.

Virtual ecological technology is a new technology that builds the foundation of a digital ecological system, simulates the process of ecological evolution, and establishes a global knowledge analysis system based on a unified spatio-temporal benchmark. It is a new direction for the interdisciplinary integration and development of eco-logy, geographic information science, computer science and other subjects towards modernization, informatization, and intelligence. Research, development, and application of virtual ecological technology is of great significance for the development of ecology as a discipline, ecosystem management, and regional sustainable development. Curren-tly, research on virtual ecological technology is still in its infancy and lacks a complete and clearly defined framework, making it difficult to support systematic iterative development and scientific analysis. In this paper, starting from the main theories and objectives of modern ecology, we summarized the main contents and technical requirements of virtual ecological construction, and proposed a key technical system of virtual ecology that integrated vir-tual geography and digital twin technology. From the perspective of application scenarios, we analyzed the application capabilities of virtual ecological technology in air pollution and energy analysis. Finally, we summarized the deve-lopment potential of virtual ecological technology in the digital construction of the ecological environment, intelligent computing, and realistic expression of different levels of ecological space resources transformation under the background of new generation of information technology, both at the local computing point breakthrough and the global technology point fusion aspects. This would enrich and advance the technical capabilities that support China's ecological space resource transformation.

A scenario-based food security analysis and halophyte crop suitability assessment in dying lake environments impacted by climate change

Climate change has significantly impacted food shortages over the past decades, and predicted projections have also indicated potentially intensive consequences for species diversity and natural environments in the coming decades. Climate change in the form of drought has resulted in environmental impacts on global dying lakes (GDLs), such as water scarcity, land degradation and food shortage. Thus, new comprehensive environmental impact assessment methods are urgently needed for GDLs to inform decision-making and minimise the negative impacts on the environment and human life and wellbeing. As state of art and progressive research, the main objective of this research is to develop an approach using a comprehensive and transferable geographic information science scenario-based suitability assessment to analyse the capability of halophyte plants as alternatives for crop modification in GDL areas. The approach was developed and examined for Salicornia suitability mapping in the Lake Urmia Basin as one of the most critical GDL cases. To obtain the ecological requirement information, we performed a scenario-based cultivation experiment on Salicornia under different salinity conditions. Accordingly, we employed relevant ecological requirements and environmental indictors for the Salicornia plant (e.g. soil characteristics and climate) to identify suitable areas for cultivating this plant under scenario-based spatial modelling and performed an uncertainty analysis. The results were achieved and validated in a field operation and laboratory analysis. According to the results, the Salicornia plant can be efficiently considered an alternative crop in farmlands around Lake Urmia that are impacted by intensive soil, aquifer salinisation and food production issues. Results pointed out that about 4.6% of the area (2372 km2) is highly suitable for the cultivation of Salicornia. As state of the art, our approach was developed in a semi-automated context but can also be customised and applied to similar case studies in GDL environments. We expect that the information and insights gained from such assessments will support stakeholders, decision makers and authorities in the development of effective policies and plans for mitigating the impacts of climate change on the fragile ecosystems of GDLs.

A brief review of the evolution of GIScience since the NCGIA research agenda initiatives

Geographical information science (GIScience) is progressively acknowledged as a scientific field based on a wide range of theories and methods that are constantly evolving. This motivates our attempt at a tentative observation of the research progress and challenges that have gone along with its gradual recognition as a domain of its own. The brief critical review presented in this paper develops an observation of such evolution. The peculiarity of our approach is that it is not based on a quantitative evaluation of the research outputs as identified by usual journal production metrics, but rather on a progressive identification of the research questions and their evolution, which the GIS academic community has been addressing over the past 30 years since the landmark NCGIA initiatives' research agendas have largely inspired and contributed to the development of geographical information science as a field.

The next layer: Towards open pedagogy in geospatial education

AbstractOpen‐source software and open data are becoming increasingly popular in the teaching and learning of geographic information science. The cost savings that are derived from using free software over proprietary software are one driving factor, yet the move from “closed” to “open” represents much more than financial austerity—it signifies a broader shift in educational philosophy. This article documents the gradual transition of an introductory undergraduate course in geographic information systems from an entirely closed course to one that has become increasingly open. Having completely adopted the first three layers of open—software, data, and educational resources—the course is now turning toward the next layer: embracing the philosophy of open pedagogy.

Researching Student Interaction with GIS Software While Learning Spatial Concepts: Toward a Standard Measure of GIS Interaction

A better understanding of differences in the nature of human computer interaction (HCI) relating to GIS software can help with learning spatial concepts and spatial thinking skills. This motivates our study, which aims to create a standard measure of interaction with GIS software that can be used individually or along with existing measurement instruments for spatial thinking to generalize results across studies for evaluating GIS interaction and usability. The study developed a composite taxonomy identifying types of user interactions and strategies with GIS in relation to different spatial concepts and processes of reasoning. The taxonomy was used as a framework to create tasks which users can complete with GIS software as a measure interaction with GIS. Study data were collected from 35 students enrolled in an online undergraduate course covering fundamentals of geographic information science and technology. Following three course assignments using GIS software to demonstrate a range of spatial concepts for problem solving, students completed various tasks constituting the standard measure of GIS interaction. The results indicate that the standard measure succeeds in measuring how individuals interact with GIS and strategize to complete different spatial tasks. The standard measure of GIS interaction will be useful for the development of usability guidelines for GIS and further contributes to research in the acquisition and application of spatial concepts and reasoning in learning environments.

GIScience can facilitate the development of solar cities for energy transition

The energy transition is increasingly being discussed and implemented to cope with the growing environmental crisis. However, great challenges remain for effectively harvesting and utilizing solar energy in cities related to time and location-dependant supply and demand, which needs more accurate forecasting- and an in-depth understanding of the electricity production and dynamic balancing of the flexible energy supplies concerning the electricity market. To tackle this problem, this article discusses the development of solar cities over the past few decades and proposes a refined and enriched concept of a sustainable solar city with six integrated modules, namely, land surface solar irradiation, three-dimensional (3D) urban surfaces, spatiotemporal solar distribution on 3D urban surfaces, solar photovoltaic (PV) planning, solar PV penetration into different urban systems, and the consequent socio-economic and environmental impacts. In this context, Geographical Information Science (GIScience) demonstrates its potent capability in building the conceptualized solar city with a dynamic balance between power supply and demand over time and space, which includes the production of multi-sourced spatiotemporal big data, the development of spatiotemporal data modelling, analysing and optimization, and geo-visualization. To facilitate the development of such a solar city, this article from the GIScience perspective discusses the achievements and challenges of (i) the development of spatiotemporal big data used for solar farming, (ii) the estimation of solar PV potential on 3D urban surfaces, (iii) the penetration of distributed PV systems in digital cities that contains the effects of urban morphology on solar accessibility, optimization of PV systems for dynamic balancing between supply and demand, and PV penetration represented by the solar charging of urban mobility, and (iv) the interaction between PV systems and urban thermal environment. We suggest that GIScience is the foundation, while further development of GIS models is required to achieve the proposed sustainable solar city.

Urbanization Process: A Simulation Method of Urban Expansion Based on RF-SNSCNN-CA Model

One of the focal points in Geographic Information Science (GIS) is to uncover the spatial distribution patterns of geographical phenomena. In response to the insufficient spatial feature learning concerning neighborhoods in traditional machine learning-based Cellular Automata (CA) models for land use change, this study couples the Random Forest (RF) model and the Spatially Non-Stationary Convolutional Neural Network (SNSCNN) model to the CA model. The resulting RF-SNSCNN-CA model considers the issue of spatial non-stationarity by incorporating attention mechanisms. Using observed urban land change data from 2010 to 2017 in the 21 districts of Chongqing’s main city as an example, two sets of experiments comprising eight scenarios were designed to verify the neighborhood effects. The results demonstrate that the proposed RF-SNSCNN-CA model achieves an Overall Accuracy (OA) of 97.82%, Kappa of 0.7683, and Figure of Merit (FoM) of 0.3836. The study reveals the following findings. Firstly, the RF-SNSCNN-CA model integrates the dual advantages of traditional machine learning and deep learning models, in which SNSCNN improves by the combined effect of channel and spatial attention mechanisms improves the learning of neighborhood features; secondly, the machine learning-like urban sprawl CA modeling process, regardless of the approach taken to obtain development suitability, cannot completely replace the learning of the neighborhood part; lastly, the use of traditional neighborhood modeling methods may produce suppression of simulation results and make the model inadequately learn spatial features.