Network Geographic Information Research Articles

Road Accessibility during Natural Hazards Based on Volunteered Geographic Information Data and Network Analysis

Natural hazards can present a significant risk to road infrastructure. This infrastructure is a fundamental component of the transportation infrastructure, with significant importance. During emergencies, society heavily relies on the functionality of the road infrastructure to facilitate evacuation and access to emergency facilities. This study introduces a versatile, multi-scale framework designed to analyze accessibility within road networks during natural hazard scenarios. The first module of the framework focuses on assessing the influence of natural hazards on road infrastructure to identify damaged or blocked road segments and intersections. It relies on near real-time information, often provided by citizen science through Volunteered Geographic Information (VGI) data and Natural Language Processing (NLP) of VGI texts. The second module conducts network analysis based on freely available Open Street Map (OSM) data, differentiating between intact and degraded road networks. Four accessibility measures are employed: betweenness centrality, closeness centrality, a free-flow assumption index, and a novel alternative routing assumption measure considering congestion scenarios. The study showcases its framework through an exemplary application in California, the United States, considering different hazard scenarios, where degraded roads and connected roads impacted by the hazard can be identified. The road extraction methodology allows the extraction of 75% to 100% of the impacted roads mentioned in VGI text messages for the respective case studies. In addition to the directly extracted impacted roads, constructing the degraded network also involves finding road segments that overlap with hazard impact zones, as these are at risk of being impacted. Conducting the network analysis with the four different measures on the intact and degraded network, changes in network accessibility due to the impacts of hazards can be identified. The results show that using each measure is justified, as each measure could demonstrate the accessibility change. However, their combination and comparison provide valuable insights. In conclusion, this study successfully addresses the challenges of developing a generic, complete framework from impact extraction to network analysis independently of the scale and characteristics of road network types.

Coupling effect of key factors on ecosystem services in border areas: a study of the Pu'er region, Southwestern China.

The coupling effects created by transboundary and local factors on ecosystem services are often difficult to determine. This poses great challenges for ecosystem protection and management in border areas. To decrease uncertainty, it is crucial to quantify and spatialize the impact multiple factors have on ecosystem services within different scenarios. In this study, we identified key transboundary and local factors from a set of 15 sorted factors related to four main ecosystem services. We employed a Bayesian Network-Geographic Information System (BN-GIS) model to simulate 90 scenarios with multiple factors combinations, quantifying and spatializing the coupling effects on the main ecosystem services. These simulations were conducted in the Pu'er region, which is situated alongside three countries, and serves as a representative border area in southwest China. The results showed that: (1) The coupling effects of multiple factors yield significant variations when combined in different scenarios. Managers can optimize ecosystem services by strategically regulating factors within specific areas through the acquisition of various probabilistic distributions and combinations of key factors in positive coupling effect scenarios. The outcome is a positive coupling effect. (2) Among the four main ecosystem services in the Pu'er region, food availability and biodiversity were affected by key transboundary and local factors. This suggests that the coupling of transboundary and local factors is more likely to have a significant impact on these two ecosystem services. Of the 45 combination scenarios on food availability, the majority exhibit a negative coupling effect. In contrast, among the 45 combination scenarios on biodiversity, most scenarios have a positive coupling effect. This indicates that food availability is at a higher risk of being influenced by the coupling effects of multiple factors, while biodiversity faces a lower risk. (3) Transboundary pests & diseases, application of pesticides, fertilizer & filming , population density, and land use were the key factors affecting food availability. Bio-invasion, the normalized differential vegetation index, precipitation, and the landscape contagion index were the key factors affecting biodiversity. In this case, focusing on preventing transboundary factors such as transboundary pests & disease and bio-invasion should be the goal. (4) Attention should also be paid to the conditions under which these transboundary factors combine with local factors. In the areas where these negative coupling effects occur, enhanced monitoring of both transboundary and local factors is essential to prevent adverse effects.

Forecasting market opportunities for urban and regional air mobility

Analysts predict that future air taxis will fundamentally change the travel behavior of society. However, current market forecasts for air taxi demand varies widely because of inconsistencies in assumptions about travel purpose, technology acceptance, time savings, affordability, and safety. To successfully deploy Advanced Air Mobility (AAM), stakeholders need to reliably forecast routes where demand will be highest. To increase reliability, this study focuses on the Uber Elevate multimodal use case and combines top-down and bottom-up methodologies to forecast demand. The hybrid methodology forecasts demand within four distance bands from 100 miles to 400 miles, in 100-mile increments. Forecasting within distance bands informs a range roadmap for electrified vertical takeoff and landing (eVTOL) aircraft. Geographic information system (GIS) and network trimming techniques identify 2083 viable routes among 859 U.S. cities. The findings are that approximately 78,000 passengers daily will access at most 4214 vertipads to fly on 3023 four-passenger eVTOL aircraft. Serving routes within the first 100-mile band will require two and five times more capital for aircraft and vertiports, respectively, than for longer routes. AAM stakeholders can utilize the hybrid methodology to forecast demand for specific routes in other regions of the world and for additional use cases.

Exploring the research dynamics of futures studies: An analysis of six top journals

This paper focuses on the global literature on Futures Studies and foresight over the last thirty years by using a bibliographic dataset from the Scopus and using an integrated statistical methodological approach. Bibliometric measures, knowledge mapping tools, topic modelling, Geographical Information Systems and network analysis are used to understand the scholarly literature’s evolution, main research areas, temporal evolution, geographical differences, and fragmentation. This allows to outline a separation between research areas and understand the dynamics of the main topics. The aim of this research is to fill the gap in the literature regarding the mapping of research themes in Futures Studies and foresight, as well as their temporal evolution and geographical distribution. Results showed a notable growth in the number of published articles in the last 32 years and identified (through Latent Dirichlet Allocation) 21 topics, which summarize the most important research themes in the context of Future Studies and foresight. A dynamic topic model helped to understand the evolution of topics, while the network analysis provided quantitative measures on the interactions between the topics as well as the international collaborations. Finally, a geographical analysis of both authors and topics highlighted the global distribution of research on Futures Studies.

Wireless Intelligent Sensor Network in Dynamic Environmental Monitoring of Archaeological Excavation Site

This paper intends to apply the Intelligent Wireless Sensor Network (IWSN) to the archaeological excavation site and make the dynamic site of archaeological excavation can be monitored intelligently in real-time. This paper firstly discusses the structure and function of Wireless Sensor Network (WSN). Secondly, according to the problem of limited dynamic monitoring of WSN, an IWSN is established. The dynamic environmental monitoring of archaeological excavation sites is achieved by using Geographic Information System (GIS) and remote sensing technology combined with IWSN. Finally, this paper analyses the environmental monitoring results of the archaeological excavation site, the dynamic environmental monitoring location of the archaeological excavation site of the IWSN, and the simulation results of the Intelligent Wireless Sensor Network-Geographic Information System (IWSN-GIS) in the dynamic environmental monitoring of the archaeological excavation site. The results show that the Inverse Distance to a Power (IDP) algorithm in temperature and humidity monitoring data on the overall downward trend, the algorithm performance is relatively stable, and the algorithm performance is better than the other two algorithms. The monitoring performance of IWSN-GIS system is better than that of IWSN system. It shows that with the increase in the communication distance of sensor nodes, the relative error of different monitoring systems for the dynamic monitoring position of the archaeological excavation site is decreasing. IWSN-GIS can monitor the environment before, during, and after the archaeological site. This paper realizes the intelligent, scientific, and technological archaeology of archaeological excavation through IWSN, GIS, and remote sensing image technology, and provides technical support for digital archaeology in archaeological research.

Intelligent algorithm-based efficient planning of electric vehicle charging station: A case study of metropolitan city of India

Electric vehicles are gaining popularity and going to become the mainstream mode of transportation in urban and rural areas not only in India but globally in the next few years. In the adoption of electric vehicles, there are certain issues like proper charging infrastructure, charging time etc. and out of these the sizing and siting of the charging station particularly in urban areas where the cost of land and location plays an important role. Thus, it is important that the charging station location should be easily accessible for the electric vehicle users and cost effective as well. This paper presents an intelligent algorithm based efficient planning of electric vehicle charging station considering geographical information and road network. The cost function has considered such as investment cost, charging station electrification cost, electric vehicle energy loss cost and travel time cost. An intelligent algorithm-based approach is employed to solve the planning problem of electric vehicle charging stations. Further, the impact on reliability of the grid is also evaluated by determining the charging cost loss on each considered location. The result shows that the applied method provides better optimized solutions which are beneficial for electric vehicles users, charging station operator and utility grid.

Efficient [formula omitted]-closest entity matching over heterogeneous information networks

This work investigates a novel m-closest entity (mCE) matching problem over geographic heterogeneous information networks (Geo-HINs). That is, given a Geo-HIN G and m query graphs {q1,q2,…,qm}, mCE matching aims to find a group of geographic entities (geo-entities) whose patterns match the query graphs {q1,q2,…,qm} correspondingly, for which the maximum distance between any geo-entity pair (i.e., the diameter) in the group is minimized. As a fundamental problem, the mCE matching can be applied for many scenarios, e.g., travel itinerary recommendation and city planning. The existing works have not simultaneously considered the characteristics of patterns matching and spatial search so that they cannot solve our problem, which is computationally expensive. To solve this problem efficiently, we propose a unified framework named Fuzzy−Exact framework to process entity matching and spatial search comprehensively, in which pruning abilities at non-spatial and spatial layers are cooperatively explored. Two mutually adaptive auxiliary data structures named Arc−Tree and Arc−Forest are devised to maintain the intermediate search results which are exploited to enhance the search process between non-spatial and spatial layers. Experimental results demonstrate that our algorithm can outperform the baseline methods by 2 orders of magnitude on runtime.

Placial-Discursive Topologies of Violence: Volunteered Geographic Information and the Reproduction of Violent Places in Recife, Brazil

Knowledge and experiences of violence transform the ways in which individuals perceive the urban landscape, construct and reproduce (un)safety, and make everyday decisions regarding mobility and the use of space. This knowledge and these experiences are placially anchored and are shaped by everyday regionalisations. In the context of interpersonal violence in Recife, Brazil, we examine the ways in which volunteered geographic information (VGI), formal and informal information exchange networks, and individual experience contribute to the reproduction of violent spaces. During interviews with civilian residents and police officers, we explore the knowledge and information flows and their spatial anchorings before and after presenting informants with a VGI-based map of firearms violence. Following coding, interviews were also analysed using a novel, semiautomated text mining algorithm to produce context-sensitive co-occurrence graphs of key arguments within participant narratives. The results indicate strong differences in the placial anchorings between police officers and civilians, and highlight key dynamics in the flows of VGI amongst residents and local news organisations, as well as through social media. These forms of placial knowledge exchange are in constant negotiation with individuals’ perceptions and experiences of the study area and reflect cognitive-discursive reproductions of everyday geographies of (un)safety.

A Schelling Extended Model in Networks—Characterization of Ghettos in Washington D.C.

Segregation affects millions of urban dwellers. The main expression of this reality is the creation of ghettos which are city parts characterized by a combination of features: low income, poor cultural level… Segregation models have been usually defined over regular lattices. However, in recent years, the focus has shifted from these unrealistic frameworks to other environments defined via geographic information systems (GIS) or networks. Nevertheless, each one of them has its drawbacks: GIS demands high-resolution data, that are not always available, and networks tend to have limited real-world applications. Our work tries to fill the gap between them. First, we use some basic GIS information to define the network, and then, run an extended Schelling model on it. As a result, we obtain the location of ghettos. After that, we analyze which parts of the city are segregated, via spatial analysis and machine learning and compare our results. For the case study of Washington D.C., we obtain an 80% accuracy.

Проблема сталого розвитку в кризових умовах і місце в ній питань ментальності й архетипіки

The paper examines the consideration of the problem of sustainable development in crises (including in the war and the postwar period). It evaluates how sustainable development research is changing and what tools modifications are needed for mathematical modelling these processes in this case. The author considers the examples of possible transformation of such problems in current conditions. The proposed research is based on the previous work of the authors on sustainable development, modelling of social and economic systems, and problems in education and science. In particular, the possibility of applying models from the class of ordinary differential equations, their systems, cellular automata, neural networks, multiagent systems, system dynamics and network theory, various databases and geographic information systems is emphasized. The approach’s main feature is to consider the possibility of formalizing the proposed sustainable development tasks, especially in crisis conditions. As a result of research and development, new knowledge was gained about the relevant processes, problem statements and the choice of mathematical models and methods for their study. The offered research and developments will help to consider specific tasks of sustainable development of functioning of Ukraine, including functions of education and science and many other things. Such research will help ensure the sustainable development of Ukraine in light of the conditions of war. Among the sections of the concept of sustainable development, an important place is taken by the influence of the role and impact of the human factor on the behaviour of large social systems. This can be addressed as conditionally questions of mentality. Among them, archetypal issues are essential. The archetype can be conditionally called the field of research, which considers the deep structures of consciousness, their stability and possible changes. In times of crisis, investigation into the influence of mental constructions and their possible changes is becoming increasingly important.

COVID-19 and Minimizing Micro-Spatial Interactions

COVID-19, the novel coronavirus that has disrupted lives around the world, continues to challenge how humans interact in public and shared environments. Repopulating the micro-spatial setting of an office building, with virus spread and transmission mitigation measures, is critical for a return to normalcy. Advice from public health experts, such as maintaining physical distancing from others and well-ventilated spaces, are essential, yet there is a lack of sound guidance on configuring office usage that allows for a safe return of workers. This paper highlights the potential for decision-making and planning insights through location analytics, particularly within an office setting. Proposed is a spatial analytic framework addressing the need for physical distancing and limiting worker interaction, supported by geographic information systems, network science, and spatial optimization. The developed modeling approach addresses dispersion of assigned office spaces as well as associated movement within the office environment. This can be used to support the design and utilization of offices in a manner that minimizes the risk of COVID-19 transmission. Our proposed model produces two main findings: (1) that the consideration of minimizing potential interaction as an objective has implications for the safety of work environments, and (2) that current social distancing measures may be inadequate within office settings. Our results show that leveraging exploratory spatial data analyses through the integration of geographic information systems, network science, and spatial optimization, enables the identification of workspace allocation alternatives in support of office repopulation efforts.

Forest Ecological Diversity Change Prediction Discrete Dynamic Model

Forest ecosystem is the most important terrestrial ecosystem type of species diversity in the world. The protection and sustainable use of forest ecological diversity is of great significance to forest protection and sustainable management. In order to maintain biodiversity, the traditional concept of forest resources management must be changed. In recent years, the mining and application of big data have become the frontier content of international ecological diversity and macroecology research. By establishing the discrete dynamic model of forest ecological diversity change, this study finds a method that can keep the number of trees stable in a short time and ensure the sustainable development of forest resources. This article studies and constructs the forest information system and constructs an open-source WebGIS (network geographic information system) scheme for the field of spatial information and supporting OGC (occupationally generated content). At the same time, the grey theory GM (1,1) model is used to predict the development trend of forest resources in China. The results show that under the promotion of policies and research projects, China’s forestry development has realized the double growth of forest area and volume, and the forest coverage rate has reached a new level.

Day–Night Monitoring of Volcanic SO2 and Ash Clouds for Aviation Avoidance at Northern Polar Latitudes

We describe NASA’s Applied Sciences Disasters Program, which is a collaborative project between the Direct Readout Laboratory (DRL), ozone processing team, Jet Propulsion Laboratory, Geographic Information Network of Alaska (GINA), and Finnish Meteorological Institute (FMI), to expedite the processing and delivery of direct readout (DR) volcanic ash and sulfur dioxide (SO2) satellite data. We developed low-latency quantitative retrievals of SO2 column density from the solar backscattered ultraviolet (UV) measurements using the Ozone Mapping and Profiler Suite (OMPS) spectrometers as well as the thermal infrared (TIR) SO2 and ash indices using Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, all flying aboard US polar-orbiting meteorological satellites. The VIIRS TIR indices were developed to address the critical need for nighttime coverage over northern polar regions. Our UV and TIR SO2 and ash software packages were designed for the DRL’s International Planetary Observation Processing Package (IPOPP); IPOPP runs operationally at GINA and FMI stations in Fairbanks, Alaska, and Sodankylä, Finland. The data are produced within 30 min of satellite overpasses and are distributed to the Alaska Volcano Observatory and Anchorage Volcanic Ash Advisory Center. FMI receives DR data from GINA and posts composite Arctic maps for ozone, volcanic SO2, and UV aerosol index (UVAI, proxy for ash or smoke) on its public website and provides DR data to EUMETCast users. The IPOPP-based software packages are available through DRL to a broad DR user community worldwide.

Topological network and GIS approach to modeling earthquake risk of infrastructure systems: A case study in Japan

In this study, we propose a network-geographic information system (GIS) modeling framework to construct a risk map for revealing infrastructure vulnerability to seismic hazards. The framework considers the cascading network of interdependencies among the effects and intermediate effects of earthquakes. Network modeling is applied to analyze infrastructure vulnerabilities and identify the infrastructure types that are vulnerable to earthquakes, while geographical modeling is applied to identify seismic hazards in terms of ground motion intensity and earthquake source faults. Reported earthquake cases in Japan are selected for analysis, and an illustrative example of the modeling of a risk map is conducted on a section of a conventional train line in Japan. In the network modeling, statistics and network diagrams are developed to analyze the relational earthquake incident data. The results demonstrate that earthquakes can cause direct and indirect effects on infrastructure, and interdependencies can be found among the effects and intermediate effects. In the geographical modeling, probabilistic distribution is adopted to predict the seismic characteristics; the results reveal the intensity occurrence probability and cascading consequences of different earthquake intensities, and predict specific degrees of destruction. Infrastructure vulnerability and seismic hazard maps are developed according to the analyzed results. A risk map is then constructed by integrating the infrastructure vulnerability map with the seismic hazard map. This study characterizes the risk of earthquakes to infrastructures, capturing the complex interdependencies to reveal the cascading effects from earthquakes, and quantifying and demonstrating infrastructure areas vulnerable to seismic hazards.

Discovering latent structures with integrated propagation algorithms in geographical information networks

The real-world exists various kinds of complex network systems, including geographical information systems (GIS) as the important components. However, it is hard to understand the functions scientifically because that the structures are latent for our eyes in these systems. In this paper, a specific model is proposed to deal with the expression of geographical information networks. And by integrating the belief propagation algorithm and label propagation algorithm, our algorithm is able to discover the latent structures and achieve some understandings of the networks without knowing the number of communities. We test the algorithm on the benchmark networks as well as the real-world subway-road networks. The results show the availability for the applications of our algorithm to the geographical information networks.

Road versus Rail: Assessing the Implications of Transport Infrastructure for Spatial Growth Pattern in China’s Megaregions

The exacerbation of the concentration of population and economic activities in a few core metropolitan areas has become an urgent issue for China’s regional development, and necessitates a more polycentric spatial development pattern. Transportation infrastructure improvement can serve as a vital instrument to achieve the objective of polycentric spatial development. Focusing on the 10 megaregions of China, this research examines a fundamental issue for China’s urban and regional development in the context of massive investment in highway and railway: the impacts of road and rail network on the spatial pattern of growth and development. Using the county-level national censuses of 1982, 1990, 2000, and 2010 and the historical transport network geographic information system (GIS) data, this paper analyzes the differentiated effects of road and rail for population growth. The research results revealed the stronger positive growth impacts of rail compared with road, and further examination indicates that railway has stronger positive impacts for regional growth in the less-developed areas.

Representing Complex Evolving Spatial Networks: Geographic Network Automata

Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of evolving networks. Existing network automata approaches simulate evolving network structures, but do not consider the representation of evolving networks embedded in geographic space nor integrating actual geospatial data. Therefore, the objective of this study is to integrate network automata with geographic information systems (GIS) to develop a novel modelling framework, Geographic Network Automata (GNA), for representing and analyzing complex dynamic spatial systems as evolving geospatial networks. The GNA framework is implemented and presented for two case studies including a spatial network representation of (1) Conway’s Game of Life model and (2) Schelling’s model of segregation. The simulated evolving spatial network structures are measured using graph theory. Obtained results demonstrate that the integration of concepts from geographic information science, complex systems, and network theory offers new means to represent and analyze complex spatial systems. The presented GNA modelling framework is both general and flexible, useful for modelling a variety of real geospatial phenomena and characterizing and exploring network structure, dynamics, and evolution of real spatial systems. The proposed GNA modelling framework fits within the larger framework of geographic automata systems (GAS) alongside cellular automata and agent-based modelling.

The r toolbox grainscape for modelling and visualizing landscape connectivity using spatially explicit networks

Abstract Networks are widely used for modelling landscape connectivity and have many ecological and conservation applications. The nodes in these models describe geographic locations (such as habitat patches or protected areas) and links describe the potential for organisms (or their propagules) to move among nodes. We present the r package grainscape which facilitates working with these networks within a spatially explicit framework. Package analyses are based on the minimum planar graph, a class of network where links among nodes are influenced by the spatial characteristics of features across the entire landscape. Modelling outputs are compatible with downstream packages including igraph for network analysis and ggplot2 for visualization. Tools for analysis (e.g. finding corridors) and scaling networks (e.g. grains of connectivity) are also provided. Models can be exported for visualization and analysis in Geographic Information System (GIS) or network software. This package provides a computationally‐efficient programmatic toolbox for many landscape connectivity research questions, enabling researchers to easily customize models, work at large geographic extents, generate their own network metrics, conduct sensitivity analyses and seamlessly employ r statistical functions to test models using biological data. A detailed guide, provided as an Appendix, illustrates common analysis and model variants with accompanying r code.

Open Capacity Enhancement Model of Medium Voltage Distribution Network With Mobile Energy Storage System

In order to meet the demand of prosumer for power quality and new load in distribution network, an open capacity expansion model of distribution network with mobile energy storage system (MESS) is proposed in this paper. The model gives priority to the problem of voltage violation of prosumer group on feeders. Combined with the mobile energy storage path model, the open capacity of distribution network is calculated. Firstly, the actual distribution network geographic information and traffic routes are combined to establish the distribution network model based on mesh generation. Secondly, the traffic route model of the mobile energy storage system is established on the distribution network model based on mesh generation. Then, the optimal power flow of distribution network is calculated with the optimal network loss, and the mobile energy storage system is scheduled according to the situation of voltage violation. And the specific vehicle model and its scheduling are determined, which can better deal with the problem of distributed energy consumption and make prosumer operate safely and economically. Finally, according to the proposed N-1 security check constraint of distribution network with mobile energy storage system, the maximum open capacity of distribution network is calculated after the voltage regulation is completed. The example shows the practicability and correctness of the model in this paper. This paper formulates a mobile energy storage operation strategy to improve the open capacity of distribution network. The strategy not only fully meets users' needs, but also better guides the operation of power grid. It is conducive to releasing more potential power supply space of distribution network and improving asset utilization.

Automatic Generation Algorithm of Distribution Network Topology Map Based on GIS Drawing

In view of the inaccuracy and completeness of the distribution network system diagram in the current power grid, the power network structure and the business characteristics of the power industry are investigated and analyzed. The relationship model between substations is analyzed, and the inheritance circuit layout design idea is applied to the distribution. In the grid system topology diagram. The paper proposes a method of dividing the power supply area according to the line contact relationship, and develops an equal density constrained space layout algorithm. Make full use of the distribution network geographic information (GIS) drawings, carry out the power grid topology and line information maintenance, so that the system diagram covers all electrical equipment on the GIS, and can carry out the original electrical topological relationship of the gemstones, and realize the absence of each subsystem. The seams provide image support for a variety of applications. The experimental results show that the proposed spatial topology algorithm is efficient and easy to implement.