Current Geographic Information Systems Research Articles

3D VISIBILITY ANALYSES USING BIM & GIS INTEGRATION

Abstract. Visibility is a significant parameter for defining, developing, and evaluating space in the AEC (Architecture, Engineering, and Construction) Industry. Therefore, visibility is considered when designing visually comfortable buildings. Visibility is also a significant parameter in building appraisal. Therefore, in this study, visibility was examined through 3-dimensional (3D) isovists, which are thought to represent the human perception of vision. Visibility was examined regarding spatial openness and mathematically calculated “visual openness indexes” were developed for building valuation. The analysis area was selected in Istanbul, and the urban model was created by using Geographic Information System (GIS) data in Building Information Modelling (BIM) software. Due to, economic reasons affecting different stakeholders in building valuation, it is necessary to create an accurate urban model. For this reason, in the study, the process of obtaining current and accurate GIS data covering the world from open sources is preferred. Then, the process of correctly transferring GIS data to BIM is explained. A sufficient urban model for analysis is created. To calculate the openness indexes, the dormitory in the Golden Horn was modelled in BIM, and different rooms' openness indexes were calculated. Some solutions have been presented to ensure the modelling process with large file sizes and in the calculation of visibility factors. To allow different AEC stakeholders to calculate these indexes, modelling, and calculation is done within BIM. Eventually, it was calculated how much the building users cognitively perceive the openness of the view, and a mathematical comparison of the different rooms' openness indexes was provided.

Geographic Information Systems Applications in Business Decision-Making

The recent advancements in Geographic Information Systems (GIS) techniques and their applications across various business domains provide an overview of cutting-edge GIS methodologies, including spatial analytics, machine learning integration, and advanced visualization tools. The study sheds light on how these techniques enhance decision-making processes in industries such as retail, service, and manufacturing. Challenges encompass the complexity of integrating diverse datasets, issues of data usage, and ensuring the interpretability and efficient use of machine learning algorithms within GIS frameworks. In response to these challenges, the primary objective of this study is to address the limitations of current GIS methodologies by understanding their capabilities while ensuring the effective use of advanced technologies, and optimizing business decision making processes.

Multi-Scalar Oblique Photogrammetry-Supported 3D webGIS Approach to Preventive Mining-Induced Deformation Analysis

Underground coal mining will inevitably cause serious ground deformation, and therefore, preventive mining-induced deformation analysis (MIDA) is of great importance in assisting mining planning and decision-making. Current web-based Geographic Information System (webGIS)-based applications usually use 2D GIS data and lack a holistic framework. This study presents a multi-scalar oblique photogrammetry-supported unified 3D webGIS framework for MIDA applications to fill this gap. The developed web platform uses multiple open-source JavaScript libraries, and the prototype system provides user-friendly interfaces for GIS data collecting and corresponding database establishment, geo-visualization and query, dynamic prediction, and spatial overlapping analysis within the same framework. The proposed framework was tested and evaluated in the Qianyingzi mining area in eastern China. The results demonstrated that multi-scalar oblique photogrammetry balances data quality and acquisition efficiency and provides a good source of GIS datasets, and the web-based platform has a good absolute and relative spatial accuracy verified by two types of validation data. Practical application results proved the feasibility and reliability of the system. The developed web-based MIDA prototype system attains an appealing performance and can be easily extended to similar geoscience applications.

Seamlessly Coupling Hydrological Modelling Systems and GIS through Object-Oriented Programming

Coupling hydrological modelling systems (HMS) with a geographic information system (GIS) can significantly enhance hydrological research and expand its applications. The calculation for HMS requires geographic information data; however, the current GIS data structure is not equipped to support the object-oriented hydrological modelling. Due to different objectives and design concepts, the differences between HMS and GIS have been profound, especially in their data structures from the perspective of object-oriented programming (OOP). This research introduces a novel approach to extend ArcGIS data structures for HMS, facilitating seamless coupling. This approach employs Microsoft Component Object Model (COM) technology to construct custom data structures that align with hydrological OOP principles. These can then be integrated into ArcGIS through a custom ArcGIS layer as an add-on. As a result, the HMS can leverage the full functionality of ArcGIS without the need for re-coding existing modelling systems. Moreover, HMS can be readily developed by using COM compatible computer languages, enabling the easy adaptation of this coupling approach to other HMS to ensure computational efficiency and to maximise the benefits of ArcGIS features. This new approach has been successfully implemented with the Xin’anjiang model, and the results validate its effectiveness in coastal areas.

Biocultural mapping: unpacking the myth of an unsuitable Country in the arid zone, Willandra Lakes Region World Heritage Area, Australia

Defining spaces and places in retrojective Geographical Information Systems (GIS) of land use and occupancy is not an easy task. Research into the areas described as suitable for land use and occupancy need to incorporate multiple perspectives of what makes a land use patch useful or salient ecologically. The effect of the concept of 'Terra Nullius' and European colonisation is deeply apparent in the current GIS models of historical land use and occupancy of Aboriginal communities within arid zones in Australia. Biocultural zones of land use and occupancy zones omit spaces and places of habitation due to European bias of what a suitable ecological or hydrological land use zone should look like. This article employs Exploratory GIS methods to interrogate the data layers within the Willandra Lakes Region World Heritage Area, NSW, Australia. This work conclusively demonstrates that there are ranges of areas and land suitability zones prior to colonisation in the nineteenth century. In turn, these Exploratory GIS models of an active Country comprehensively address the question of why visually salient areas of hydrological and ecological Indigenous land use and occupancy continue to be ignored, destroyed, and damaged by settlements in semi-arid regions. Biocultural GIS mapping unpacks the myth that areas were empty or uninhabited by Aboriginal communities and underlines the need for biocultural GIS mapping tools to understand the habitable spaces and places of the arid zone.

Optimal Transshipment Route Planning Method Based on Deep Learning for Multimodal Transport Scenarios

The optimal path problem is an important topic in the current geographic information system (GIS) and computer science fields. The Dijkstra algorithm is a commonly used method to find the shortest path, which is usually used to find the least cost path from a single source. Based on the analysis and research of the traditional Dijkstra algorithm, this paper points out the problems of the Dijkstra algorithm and optimizes it to improve its storage capacity and operation efficiency. Then, combined with the traffic elements, a new network-based optimal path planning method is established. However, the existing network is far from actual operation in terms of the expansion of the transportation network, the uncertainty of the transportation environment, and the differences in the transportation area. Therefore, this paper proposes an optimal transshipment path planning method based on deep learning, which is oriented to multimodal transportation scenarios. This paper mainly introduces the intelligent transportation system and intelligent navigation system, and then conducts in-depth research on optimal path planning. This paper also uses the deep neural network algorithm to optimize the calculation, and finally analyzes its use and application. Simulation experiments were also performed to analyze the relationship between energy consumption, emissions, speed, load cost, and other factors under the optimal path. The final experimental results show that within the range of the emission limit of [100,200], the emission is 50%, the emission is less than 100%, but the emission is higher than 75%. In [100,200], 75% of the loading rate emits no less than 100%. In [200,300], the 50% and 100% emissions are the same. This also means that the emissions are the same but the paths are not necessarily the same.

Review of the Use of GIS in Public Health and Food Safety

The use of geographic information systems (GIS) is increasingly common in the scientific domains of public health and food safety. In this paper, we present the results of three analyses that we conducted to explore the application of GIS in these domains. The analysis includes a case study to test the feasibility of providing GIS services through an application developed with open-source tools. The first section presents the results from a scoping review looking at the current GIS practices in the public health and food safety domains as found in scientific literature. The second section shares the findings from a visual exploration of the use of GIS by EU agencies and supra-national bodies in the public health and food safety domains. The third section presents the current scenario of GIS used at the European Food Safety Agency (EFSA), which was tracked with an internal survey circulated among EFSA's scientific community. Alongside the three analyses, we look at current practices in terms of 1) fields in which GIS is used, 2) the infrastructure used for GIS, 3) GIS data, and 4) the methodology used for data collection, processing and analysis. The results show that GIS is used in multiple fields related to public health and food safety, such as epidemiology, plant health, animal health, and agriculture. We conclude that despite facing challenges regarding data quality issues, legal implications, and technical limitations, GIS has great potential given its relevance to the current environmental and health crises: such a holistic approach is in line with the One Health concept supported by EFSA and different European and international institutions. Moreover, GIS facilitates transition from control to prevention thanks to its capacity to develop spatial models that move from description to prediction. Finally, GIS facilitates communication, which guides better policy making.

A data relocation approach for terrain surface analysis on multi-GPU systems: a case study on the total viewshed problem

ABSTRACT Digital Elevation Models (DEMs) are important datasets for modelling the line of sight, such as radio signals, sound waves and human vision. These are commonly analyzed using rotational sweep algorithms. However, such algorithms require large numbers of memory accesses to 2D arrays which, despite being regular, result in poor data locality in memory. Here, we propose a new methodology called skewed Digital Elevation Model (sDEM), which substantially improves the locality of memory accesses and increases the inherent parallelism involved in the computation of rotational sweep-based algorithms. In particular, sDEM applies a data restructuring technique before accessing the memory and performing the computation. To demonstrate the high efficiency of sDEM, we use the problem of total viewshed computation as a case study considering different implementations for single-core, multi-core, single-GPU and multi-GPU platforms. We conducted two experiments to compare sDEM with (i) the most commonly used geographic information systems (GIS) software and (ii) the state-of-the-art algorithm. In the first experiment, sDEM is on average 8.8x faster than current GIS software despite being able to consider only few points because of their limitations. In the second experiment, sDEM is 827.3x faster than the state-of-the-art algorithm in the best case.

Sistem Informasi Geografis Letak Industri Kecil dan Menengah berbasis android di Provinsi Gorontalo

Small and Medium Industry is the program and policy of the Local Government of Gorontalo Province to achieve development goals in the field of economy, especially related to the development efforts of small and medium industry sectors (IKM). The Cooperative, Industrial and Trade Office of Gorontalo Province desperately needs accurate data information for the search and determination of small and medium industry locations and also in terms of regional development planning and spatial arrangement, but in reality, to obtain data and information on the spread of Small and Medium Industry spread almost throughout Gorontalo province is very difficult, this is due to the absence of an information system that handles this. The Government's current geographic information system cannot access the search and location of the Small and Medium Industry. The expansion of the area that occurs in Gorontalo Province from 6 districts, the addition of fickle regions and addresses can confuse and make it difficult for local people or tourists outside Gorontalo province to get information about the small and medium industry in Gorontalo Province. The lack of information to the public about the location of Small and Medium Industry locations in Gorontalo province can make it difficult to conduct cooperation that can drive the regional economy. Information search and access user information and communication technology through high mobility media such as smartphones. Today's thriving smartphones are smartphones with android operating systems. Android smartphone besides having high mobility, also has very useful features such as google maps and GPS. Google maps and GPS features can collaborate in an android app program creation. Application program creation is designed using android and PHP programming languages collaborated with MySQL databases as data container media. The research aims to make it easier for people to get information about small and medium-sized industries and information about small and medium-sized industries. The availability of information can be a bridge for the community to cooperate and drive the regional economy.

How to Measure Distance on a Digital Terrain Surface and Why it Matters in Geographical Analysis

Distance is the most fundamental metric in spatial analysis and modeling. Planar distance and geodesic distance are the common distance measurements in current geographic information systems and geospatial analytic tools. However, there is little understanding about how to measure distance in a digital terrain surface and the uncertainty of the measurement. To fill this gap, this study applies a Monte‐Carlo simulation to evaluate seven surface‐adjustment methods for distance measurement in digital terrain model. Using parallel computing techniques and a memory optimization method, the processing time for the distances calculation of 6,000 simulated transects has been reduced to a manageable level. The accuracy and computational efficiency of the surface‐adjustment methods were systematically compared in six study areas with various terrain types and in digital elevation models in different resolutions. Major findings of this study indicate a trade‐off between measurement accuracy and computational efficiency: calculations at finer resolution DEMs improve measurement accuracy but increase processing times. Among the methods compared, the weighted average demonstrates highest accuracy and second fastest processing time. Additionally, the choice of surface adjustment method has a greater impact on the accuracy of distance measurements in rougher terrain.

Assessment of the GIS-Aided Precise Approach Using the GNSS-GBAS Landing Systems

The radio navigational Instrument Landing Systems (ILSs) are currently intended to guide the aircrafts in lateral and vertical dimensions to the runway surface safely and precisely. Therefore, they are strongly related to the geographic location of an airport and its runway(s). The ILS systems use the aids of the radio frequency radiation to achieve this purpose, depending on the ground emitting stations, and providing the guidance to the runway centreline location along with the glide slope guidance during the Final Approach Segment (FAS). Furthermore, the new ILS systems are fully aided by the coordinates of the Global Positioning System (GPS) instead of the ground radiations, they use the waypoint fixes during the landing phase of flight by means of transmitting their corrections to the on-board receivers. Those new invented Ground Based Augmentation Systems (GBAS) are more precise and trustable, they also increase the capacity of the huge air traffic demands nowadays by multiple and non-straight approaches. As a result, the Geographic Information System (GIS) of any airport supported by the GBAS system is intended to be fully used and implemented in both instrumental and procedural aids. Many previous studies had indicated that the old procedural approaches should be changed to the new GIS aided ones, but without pointing out when and how to implement such important transfer. The purpose of this study is to assess the performance of the GIS aided precision procedures using the GBAS stations, and to identify to what extent they can enhance the navigational aviation in the air traffic management domain. A special focus will be put on the Hungarian Budapest international airport in terms of both capability motivating factors and the current GIS infrastructure aiding. Results showed a promising chance for more investment in installing the GBAS stations in the airport. That will enable more capacity and easier approaches in all weather conditions.

Zonal lacunarity analysis: a new spatial analysis tool for geographic information systems

Lacunarity as a scale-dependent measure of spatial heterogeneity has received great attention in landscape ecology. Most lacunarity measures have been obtained from greyscale or binary (0 and 1) data for an entire study area or fixed rectangular windows, and a zonal lacunarity tool for discrete raster data is still lacking in current geographic information systems. This short communication presents the development of a free zonal lacunarity analysis tool for ArcGIS to support applications involving scale-dependent analysis of spatial heterogeneity, including landscape ecology. The application of the tool is also demonstrated using 2001 and 2011 land cover data from the National Land Cover Database (NLCD). Based on the gliding-box algorithm for lacunarity estimation, a tool for zonal lacunarity analysis of discrete raster data is developed using ArcPy and the Python programming language. The tool uses discrete raster data as input, an optional zone feature class as zone data to partition the input raster data into different zones, and a spreadsheet with zonal lacunarity values as output. As a demonstration, lacunarity measurements of grasslands in Corinth and Lake Dallas, Texas were calculated from the 2001 and 2011 NLCD data using box sizes (scales) of 2, 3, 4, 5, 6, 7, 8, 9, and 10. The results show that measures of grassland lacunarity in Lake Dallas were higher than Corinth at all scales, and the measures of grassland lacunarity in 2011 were higher than 2001 for both cities because of the increasing gap sizes in grasslands. The increasing gap sizes in grasslands were caused by converting the grasslands into developed areas. The results suggest that the zonal lacunarity analysis tool can provide important information on the spatial distribution of gaps in the input discrete raster data at different scales. It is hoped that the zonal lacunarity analysis tool can be further evaluated using different datasets in landscape ecology.

Development and implementation of a dynamic and 4D GIS based on semantic location model

ABSTRACTIn the big data era, robust solutions are obliged to be proposed to integrate and represent data from different formats and with different contents to assist the decision-making. Current cartographic and geographic information systems have limited capabilities for solving these problems. This paper describes an automatic and comprehensive system that conducts data fusion from all potentially related sources. In this system, a new Semantic Location Model (SemLM) is established to present the semantic concepts and location feature and demonstrate how locations are interrelated. In the SemLM, various types of location descriptors in different application scenarios can be analyzed and understood. Additionally, considering the challenges involved in data-intensive computation and visualization, this paper implements a Place-based Pan-Information System (P2S) as an innovative 4D system that dynamically associates and visualizes place-based information, using public security as the case study.

Relative space-based GIS data model to analyze the group dynamics of moving objects

The relative motion of moving objects is an essential research topic in geographical information science (GIScience), which supports the innovation of geodatabases, spatial indexing, and geospatial services. This analysis is very popular in the domains of urban governance, transportation engineering, logistics and geospatial information services for individuals or industrials. Importantly, data models of moving objects are one of the most crucial approaches to support the analysis for dynamic relative motion between moving objects, even in the age of big data and cloud computing. Traditional geographic information systems (GIS) usually organize moving objects as point objects in absolute coordinated space. The derivation of relative motions among moving objects is not efficient because of the additional geo-computation of transformation between absolute space and relative space. Therefore, current GISs require an innovative approach to directly store, analyze and interpret the relative relationships of moving objects to support their efficient analysis. This paper proposes a relative space-based GIS data model of moving objects (RSMO) to construct, operate and analyze moving objects’ relationships and introduces two algorithms (relationship querying and relative relationship dynamic pattern matching) to derive and analyze the dynamic relationships of moving objects. Three scenarios (epidemic spreading, tracker finding, and motion-trend derivation of nearby crowds) are implemented to demonstrate the feasibility of the proposed model. The experimental results indicates the execution times of the proposed model are approximately 5–50% those of the absolute GIS method for the same function of these three scenarios. It’s better computational performance of the proposed model when analyzing the relative relationships of moving objects than the absolute methods in a famous commercial GIS software based on this experimental results. The proposed approach fills the gap of traditional GIS and shows promise for relative space-based geo-computation, analysis and service.

Location-Based Services

In the current information technology trend, GIS (Geographic Information Systems) and GPS (Global Positioning Systems) are potential systems providing convenient, valuable services. They are a fundamental base in location-based services (LBS). LBS is an object positioning and monitoring system with a major combination of advanced technologies such as mobile communications, GPS technology, GIS and the Internet. The article consists of two sections in which the overview of the LBS system is presented first, followed by the introduction of the LBS system structure used to build the VTS - Vehicle Tracking System at Dong A Bank. 
 
 
 

The Big Book of Canada: Exploring the Provinces and Territories by C. Moore

The Big Book of Canada: Exploring the Provinces and Territories by C. Moore

Development of GIS Integrated Big Data Research Toolbox (BigGIS-RTX) for Mobile CDR Data Processing in Disasters Management

This article reports the development of a geographical information system (GIS) embedded text-based geospatial Big Data research toolbox (BigGIS-RTX) designed especially for mobile CDR (Call Details Record) data processing in urban transport planning and disaster management. BigGIS-RTX is a standalone computer program that aims to provide a bridge between geospatial Big Data and end users (i.e. students and researchers) by reducing difficulties in handling geospatial Big Data processing and analysis tasks. This research toolbox makes it possible to handle text-based geospatial Big Data cleaning, formatting, subsetting, and extraction by keywords or structured query language (SQL), CDR data aggregation by base transceiver stations (BTSs), generation of origin–destination (OD) trips, OD matrices, and OD routes, and computation of OD links. Moreover, this research toolbox can be integrated with current commercial GIS software to perform further geospatial analysis functions to improve spatial decision making in urban and transport planning and disaster management. In this report, we discuss two current research outputs using BigGIS-RTX: first, multitemporal grid square population estimation and second, human mobility studies in transportation planning. These research outputs are essential for disaster management and emergency preparedness in terms of providing knowledge and information about population distribution changes over space and time, human mobility flow by a user defined time frame, and travel volume or link count information for individual road segments. This research is part of the core project “Development of a Comprehensive Disaster Resilience System and Collaboration Platform in Myanmar” in a research collaboration between Yangon Technological University, Myanmar, and The University of Tokyo, Japan, sponsored by the Japan Science and Technology Agency (JST) and the Japan International Cooperation Agency (JICA).

Computing with cognitive spatial frames of reference in GIS

AbstractIn everyday communication, people effortlessly translate between spatial cognitive frames of reference. For example, a tourist guide translates from a map (“the fountain is north‐west of the church”) into a cognitive frame for a tourist (“the fountain in front of the church”). While different types of cognitive reference frames and their relevance for language cultures have been studied in considerable depth, we still lack adequate transformation models. In this article, we argue that transformations in current Geographic Information Systems (GIS) are inappropriate to this end. Appropriate transformation models need to go beyond point discretization to take into account vague transformations, in order to deal with forms, sizes, and vagueness of spatial relations relative to ground objects. We argue that neural fields should be used to denote fuzzy positions, directions, and sizes in a particular frame. We propose fuzzy vector spaces to approximate neural field behavior with affine transformations, including fuzzy translation, rotation, and scaling, in order to efficiently transform between different cognitive perspectives. We use an implementation in Haskell to describe a geographic map from the perspective of six well‐known cognitive frames of reference. Based on these findings, we give an outlook on the principles of a “neural GIS.”

An Overview of GIS-Based Modeling and Assessment of Mining-Induced Hazards: Soil, Water, and Forest

In this study, current geographic information system (GIS)-based methods and their application for the modeling and assessment of mining-induced hazards were reviewed. Various types of mining-induced hazard, including soil contamination, soil erosion, water pollution, and deforestation were considered in the discussion of the strength and role of GIS as a viable problem-solving tool in relation to mining-induced hazards. The various types of mining-induced hazard were classified into two or three subtopics according to the steps involved in the reclamation procedure, or elements of the hazard of interest. Because GIS is appropriated for the handling of geospatial data in relation to mining-induced hazards, the application and feasibility of exploiting GIS-based modeling and assessment of mining-induced hazards within the mining industry could be expanded further.

Spatially quantitative characterization of geological processes associated with polymetallic Sn-W mineralization in the Malipo mineral district, Southeastern Yunnan, China

The current geographic information system (GIS)-based mineral prospecting models focus on delineating high-probability locations for mineral deposits. These models do not attempt to characterize the geological evolution of the area under study either spatially or quantitatively. The study described in this paper demonstrates an innovative approach that quantitatively describes the mineralization processes in a spatial way. This improves predictions by adding information concerning the areas geologic evolution. This study quantitatively characterizes the mineralization in the Malipo mineral district, China, in relation to tectono-magmatic activity from Caledonian to Yanshanian time. These findings extend the existing geological knowledge of the study area, a critical step for further investigating and interpreting the geochemical signatures. The Malipo mineral district is a well-studied area and thus appropriate for evaluating a new modeling technique. Our preliminary results show that the quantitative spatial characterizations retrieved from our model offer highly correlated predictive information regarding geologic evolution when compared with the information obtained from traditional ground-truthing investigations. Therefore, employing our model in a new and under-studied area would potentially provide significant benefits and advance the level of knowledge in the new area.