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  Subjects -> GEOGRAPHY (Total: 493 journals)
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Journal of Geographical Systems
Journal Prestige (SJR): 0.589
Citation Impact (citeScore): 2
Number of Followers: 8  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1435-5949 - ISSN (Online) 1435-5930
Published by Springer-Verlag Homepage  [2467 journals]
  • Induced earthquakes and house prices: the role of spatiotemporal and
           global effects

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      Abstract: Abstract This paper contributes to the existing literature on the explanation of housing prices. First, our proposed methodology accounts for cross-sectional dependence, both locally and globally, using individual data of more than 200,000 transactions in the three most northern provinces of the Netherlands over the period 1993–2014. Second, the selection of houses within each focal house’s sub-market is not only based on distance and time, but also on their degree of similarity. Third, global cross-sectional dependence is not modeled by time-fixed effects, as in previous studies, but by cross-sectional price averages. Fourth, we accumulate the strength and frequency with which earthquakes affect each focal house before it was sold into one single measure using a seismological model and then subdivide it into different bins to account for nonlinear effects and to determine a threshold below which earthquakes have no effect. Please verify if the provided city and country are correct and amend if necessary. This way we are able to investigate the propagation of the detrimental impact of earthquakes on housing prices over space and time without the need to select a reference area in advance, which potentially might also have been affected by earthquakes.
      PubDate: 2023-02-02
       
  • Evaluating multiscale and multimodal transport inequalities in Chinese
           cities with massive open-source path data

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      Abstract: Abstract Accessibility is an effective variable for identifying mobility needs and evaluating transport inequalities. In recent years, the construction of transport facilities has been often considered to have a significant effect on the accessibility of cities. This study focused on the regional inequalities of transport accessibility in China and the influence of transport mode disparity. A novel approach integrating intercity and intracity transport networks is modeled for detailed calculations of travel time using open-source massive path data. The proposed approach provides further improvement in the accuracy, and it can reflect realistic patterns of multiscale accessibility. Four indicators based on travel time estimation were employed to evaluate transport accessibility and inequality: weighted average travel time (WATT), potential value (PV), daily accessibility (DA), and coefficient of variation (CV). Results show that transport accessibility is the highest in the eastern region, followed by the midland, northeastern, and western regions; this trend is consistent with the level of urban development and transport facilities construction. The inequality of transport accessibility between cities is obvious, and the western region has a considerably greater inequality than other areas. With regard to transport facilities, the car driving mode has higher accessibility and lower discrepancy than the public transit mode at the national level; however, the construction of public transit infrastructure, especially the high-speed railway, should considerably improve the daily accessibility of cities. Several policy suggestions are provided for transport departments and decision makers that can effectively improve the level and equality of transport accessibility of cities in China.
      PubDate: 2023-01-27
       
  • Effect of sub-indicator weighting schemes on the spatial dependence of
           multidimensional phenomena

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      Abstract: Abstract The weighting of sub-indicators is widely debated in the composite indicator literature. However, these weighting schemes’ effects on the composite indicator’s spatial dependence property are still little known. This research reveals a direct relationship between the weighting scheme of sub-indicators and the spatial autocorrelation of the composite indicator. The Global Moran's Index (I) of composite indicators built using Data-driven (Moran’s I = 0.636) and Hybrid (Moran’s I = 0.597) weighting schemes is, on average, eleven percent higher than in the Equal-weights (Moran's I = 0.549) and Expert opinion (Moran's I = 0.560) weighting schemes. The average score of the composite indicator is higher when they are built by weighting schemes that better describe the spatial dependence. The spatial dependence of sub-indicators and composite indicators are not related. All fifteen sub-indicators show lower spatial autocorrelation than the composite indicators built by Expert opinion, Hybrid, and Data-driven weighting schemes. The spatial weighting matrix influences the spatial autocorrelation but does not change the robustness and quality parameters of the composite indicator. The research develops a Data-driven weighting scheme that allows individually or simultaneously considering the opinion of experts and parameters of quality and robustness of the composite indicator. It also offers the means to reduce judgment errors and evaluation biases in Expert opinion sub-indicator weighting schemes.
      PubDate: 2022-12-29
       
  • Exploring the time geography of public transport networks with the
           gtfs2gps package

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      Abstract: Abstract The creation of the General Transit Feed Specification (GTFS) in the mid-2000s provided a new data format for cities to organize and share digital information on their public transport systems. GTFS feeds store geolocated data on public transport networks, including information on routes, stops, timetables, and service levels. The GTFS standard is now widely adopted by thousands of transport authorities and a wide variety of software applications for different purposes, including trip planning, timetable creation and accessibility analysis. Yet, there is still a lack of tools to parse GTFS data when the objective is to analyze the complex spatial and temporal patterns of public transport systems. This paper presents {gtfs2gps}, a new general-purpose computational tool to easily process static GTFS data that allows one to analyze the space–time trajectories of public transport vehicles at fine spatial and temporal resolutions. {gtfs2gps} is an open-source R package that employs parallel computing to convert GTFS feeds from relational text files into a trajectory data table, similar to GPS records, with the timestamps of vehicles in every trip. This paper explains the package functionalities and demonstrates how {gtfs2gps} can be used to articulate key concepts in time geography to explore and visualize the spatial and temporal patterns of public transport networks. We also present a case study looking at how {gtfs2gps} can be used to examine socioeconomic and spatial–temporal inequalities in access to public transport, providing key information to monitor cities’ progress toward the Sustainable Development Goals. The paper is accompanied by a computational notebook in R Markdown to support reproducibility of the results in this paper and to replicate the analysis for other contexts where GTFS data are available. Given the widespread use of GTFS, {gtfs2gps} opens new possibilities for researchers to examine the time geography of public transport systems in urban areas across the globe.
      PubDate: 2022-12-17
       
  • JGS Editors’ choice article

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      PubDate: 2022-11-05
      DOI: 10.1007/s10109-022-00399-1
       
  • A method for considering the evolution of the visible landscape

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      Abstract: Abstract The visible landscape represents an important consideration within landscape management activities, forming an inhabitants’ perception of their overall surroundings and providing them with a sense of landscape connection, sustainability and identity. The historical satellite imagery archive can provide key knowledge of the overall change in land use and land cover (LULC), which can inform a range of important management decisions. However, the evolution of the visible landscape at a terrestrial level using this information source has rarely been investigated. In this study, the Landsat archive is leveraged to develop a method that depicts changes within the visible landscape. Our method utilises other freely available data sources to determine the visibility of the landscape, and LULC composition, visible from road networks when the imagery was captured. This method was used to describe change in the visible landscape of a rural area in Ñuble, Chile, in the period from 1986 to 2018. Whilst native forests on the slopes of the mountains within the study area provide a natural backdrop, because of the flat topography of most of the area, the foreground dominates the overall landscape view. This has resulted in a visible transition from a landscape visibly dominated by agricultural use in 1986 to one of equal agriculture and plantation forestry in 2018. It is hoped that the method outlined within this study can be applied easily to other regions or at larger scales to provide insight for land managers regarding the visibility of LULC.
      PubDate: 2022-10-25
      DOI: 10.1007/s10109-022-00398-2
       
  • Estimating dynamic spatial panel data models with endogenous regressors
           using synthetic instruments

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      Abstract: Abstract The paper applies synthetic instruments, initially developed for cross-sectional regression, to estimate dynamic spatial panel data models. These have two main advantages. First, instruments correlated with endogenous variables and yet independent of the errors are difficult to find. Not only are synthetic instruments normally exogenous, but they are usually strongly correlated with endogenous variables, and thus help to avoid the problem of weak instruments. Secondly, they help to reduce instrumental variables proliferation, which is a common result of standard methods of avoiding endogeneity bias. As demonstrated by Monte Carlo simulation, instrument proliferation causes bias in the Sargan–Hansen J test statistic, which is an important indicator of instrument validity and hence estimation consistency. It is also associated with a downward bias in parameter standard error estimates. The paper shows the results of applying synthetic instruments across a variety of different specifications and data generating processes, and it illustrates the method with real data leading to more reliable inference of causal impacts on the level of employment across London districts.
      PubDate: 2022-10-14
      DOI: 10.1007/s10109-022-00397-3
       
  • A partition-free spatial clustering that preserves topology: application
           to built-up density

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      Abstract: Abstract Urban density is central to urban research and planning and can be defined in numerous ways. Most measures of urban density however are biased by arbitrary chosen spatial units at their denominator and ignore the relative location of elementary urban objects within those units. We solve these two problems by proposing a new graph-based density index which we apply to the case of buildings in Belgium. The method includes two main steps. First, a graph-based spatial descending hierarchical clustering (SDHC) delineates clusters of buildings with homogeneous inter-building distances. A Moran scatterplot and a maximum Cook’s distance are used to prune the minimum spanning tree at each iteration of the SDHC. Second, within each cluster, the ratio of the number of buildings to the sum of inter-building distances is calculated. This density of buildings is thus defined independently of the definition of any basic spatial unit and preserves the built-up topology, i.e. the relative position of buildings. The method is parsimonious in parameters and can easily be transferred to other punctual objects or extended to account for additional attributes.
      PubDate: 2022-10-07
      DOI: 10.1007/s10109-022-00396-4
       
  • Spatiotemporal high-resolution prediction and mapping: methodology and
           application to dengue disease

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      Abstract: Abstract Dengue disease has become a major public health problem. Accurate and precise identification, prediction and mapping of high-risk areas are crucial elements of an effective and efficient early warning system in countering the spread of dengue disease. In this paper, we present the fusion area-cell spatiotemporal generalized geoadditive-Gaussian Markov random field (FGG-GMRF) framework for joint estimation of an area-cell model, involving temporally varying coefficients, spatially and temporally structured and unstructured random effects, and spatiotemporal interaction of the random effects. The spatiotemporal Gaussian field is applied to determine the unobserved relative risk at cell level. It is transformed to a Gaussian Markov random field using the finite element method and the linear stochastic partial differential equation approach to solve the “big n” problem. Sub-area relative risk estimates are obtained as block averages of the cell outcomes within each sub-area boundary. The FGG-GMRF model is estimated by applying Bayesian Integrated Nested Laplace Approximation. In the application to Bandung city, Indonesia, we combine low-resolution area level (district) spatiotemporal data on population at risk and incidence and high-resolution cell level data on weather variables to obtain predictions of relative risk at subdistrict level. The predicted dengue relative risk at subdistrict level suggests significant fine-scale heterogeneities which are not apparent when examining the area level. The relative risk varies considerably across subdistricts and time, with the latter showing an increase in the period January–July and a decrease in the period August–December.
      PubDate: 2022-10-01
      DOI: 10.1007/s10109-021-00368-0
       
  • A cluster-driven classification approach to truck stop location
           identification using passive GPS data

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      Abstract: Abstract Classifying the type of truck stops is vital in transportation planning and goods movement strategies. Truck stops could be classified into primary or secondary. While the latter entail stopping to re-fuel or rest, the former takes place to deliver or pick up merchandize. The availability of GPS transponders on board moving trucks and the ability to access such information in recent years has made it possible to analyze various freight aspects including movement trajectories and stopped locations. This paper utilizes machine learning methods and proposes a two-step cluster-based classification approach to classify truck stop locations into either primary or secondary. The DBSCAN clustering technique is applied on the GPS dataset to obtain stop locations. Next, several features per location are derived to classify the stops using well-known classification models. The generated information is then used to evaluate the approach using a large truck GPS dataset for the year 2016. The Random Forest classifier is chosen as it can identify primary stop locations with an accuracy of 97%. The overall accuracy of the classifier for correctly identifying both types of stops is 83%. Further, the prediction accuracy for primary stops is 92%.
      PubDate: 2022-10-01
      DOI: 10.1007/s10109-022-00380-y
       
  • A spatio-temporal autoregressive model for monitoring and predicting COVID
           infection rates

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      Abstract: Abstract The COVID-19 epidemic has raised major issues with regard to modelling and forecasting outcomes such as cases, deaths and hospitalisations. In particular, the forecasting of area-specific counts of infectious disease poses problems when counts are changing rapidly and there are infection hotspots, as in epidemic situations. Such forecasts are of central importance for prioritizing interventions or making severity designations for different areas. In this paper, we consider different specifications of autoregressive dependence in incidence counts as these may considerably impact on adaptivity in epidemic situations. In particular, we introduce parameters to allow temporal adaptivity in autoregressive dependence. A case study considers COVID-19 data for 144 English local authorities during the UK epidemic second wave in late 2020 and early 2021, which demonstrate geographical clustering in new cases—linked to the then emergent alpha variant. The model allows for both spatial and time variation in autoregressive effects. We assess sensitivity in short-term predictions and fit to specification (spatial vs space-time autoregression, linear vs log-linear, and form of space decay), and show improved one-step ahead and in-sample prediction using space-time autoregression including temporal adaptivity.
      PubDate: 2022-10-01
      DOI: 10.1007/s10109-021-00366-2
       
  • An analysis about the accuracy of geographic profiling in relation to the
           number of observations and the buffer zone

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      Abstract: Abstract Geographic Profiling (GP) attempts to reconstruct the spreading centre of a series of events due to the same cause. The result of the analysis provides an approximated localization of the spreading centre within an area (often represented as a red red), where the probability of finding it is higher than a given threshold (typically 95%). The analysis has as an assumption that the events will be likely to occur at very low probability around the spreading centre, in a ring-shaped zone called the buffer zone. Obvious examples are series of crimes perpetrated by an offender (unwilling to perpetrate offences close to home), or the localities of spread of an invasive species, where the buffer zone, if present, depends on the biological features of the species. Our first aim was to show how the addition of new events may change the preliminary approximate localization of the spreading centre. The analyses of the simulated data showed that if B, the parameter used to represent the radius of the buffer zone, varies within a range of 10% from the real value, after a low number of events (7–8), the method yields converging results in terms of distance between the barycentre of the red zone and the “real” user provided spreading centre of a simulated data set. The convergence occurs more slowly with the increase in inaccuracy of B. These results provide further validity to the method of the GP, showing that even an approximate choice of the B value can be sufficient for an accurate location of the spreading centre. The results allow also to quantify how many samples are needed in relation to the uncertainty of the chosen parameters, to obtain feasible results.
      PubDate: 2022-10-01
      DOI: 10.1007/s10109-022-00379-5
       
  • Accessibility analysis of urban fire stations within communities: a
           fine-scale perspective

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      Abstract: Abstract In recent years, with the rapid expansion of urban space and the explosion of population within communities in China, fire stations face challenges in providing timely response to potential demands throughout their service coverage. To ensure speedy and equitable provision of fire services, it is essential to evaluate the accessibility of fire stations under the current fire service systems. Traditional accessibility analysis is often based on the aggregated large areal units, such as the census tracks, failing to assess accessibility of individual buildings. In this regard, this study seeks to analyze potential accessibility to urban fire stations at a fine scale, i.e., the building level, and to provide valuable information to assist in strategic planning of fire stations in urban areas and within local communities. Because the detailed intra-community roads are not stored in the official city map database, we propose to use a classic GIS technology, the Delaunay triangulation model, to automatically extract the intra-community roads from building footprints. With these private roads integrated into the existing city roads, a simulated road network is constructed. Then, the Voronoi-based method and buffering method are used to assess accessibility to urban fire stations. Results reveal that the current layout of fire stations in the study area is not sufficient to achieve a complete coverage of potential demands in the stipulated time, and the traditional central areas enjoy better access to fire services compared to the periphery areas. The building-level analyses will not only enable decision-makers to strategically allocate new fire stations in the urban areas, but also help local authorities to enhance fire safety management within the community. The proposed method can also be applied to fine-scale analysis of neighborhood services’ accessibility in other cities.
      PubDate: 2022-10-01
      DOI: 10.1007/s10109-022-00381-x
       
  • Rents and wages derived from spatial interaction analysis in Shanghai

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      Abstract: Abstract Using the 1% National Population Sampling Survey, collected in 2015, this paper performs sensitivity analysis of the parameters in a spatial interaction model to evaluate and compare the locational benefits of origins and destinations among different hukou (locals and migrants) and educational types in a large Chinese city (Shanghai). While the macro patterns are consistent with other case studies, Shanghai presents some unique features that include residency status and differential educational levels, and these factors result in notable patterns of spatial organization. Compared with migrants, locals have longer trip lengths, and higher rents and wages. Well-educated workers travel longer and have higher rents and wages than the poorly-educated. The presence of high-tech, high-prestige employers (e.g., Huawei) in the east-central area both attracts longer distance educated commuters, as well as generating higher wages, and potentially higher rents for those who choose to live and work in the same area, especially for locals. However, it is reassuring to see that there is a marked rent gradient over the distance from the CBD.
      PubDate: 2022-08-21
      DOI: 10.1007/s10109-022-00394-6
       
  • Correction to: An analysis about the accuracy of geographic profiling in
           relation to the number of observations and the buffer zone

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      PubDate: 2022-08-06
      DOI: 10.1007/s10109-022-00393-7
       
  • Introduction to the special issue on scale and spatial analytics

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      PubDate: 2022-07-28
      DOI: 10.1007/s10109-022-00391-9
       
  • JGS Editors’ choice article

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      PubDate: 2022-07-27
      DOI: 10.1007/s10109-022-00392-8
       
  • Platial mobility: expanding place and mobility in GIS via platio-temporal
           representations and the mobilities paradigm

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      Abstract: Abstract While platial representations are being developed for sedentary entities, a parallel and useful endeavor would be to consider time in so-called “platio-temporal” representations that would also expand notions of mobility in GIScience, that are solely dependent on Euclidean space and time. Besides enhancing such aspects of place and mobility via spatio-temporal, we also include human aspects of these representations via considerations of the sociological notions of mobility via the mobilities paradigm that can systematically introduce representation of both platial information along with mobilities associated with ‘moving places.’ We condense these aspects into ‘platial mobility,’ a novel conceptual framework, as an integration in GIScience and the mobilities paradigm in sociology, that denotes movement of places in our platio-temporal and sociology-based representations. As illustrative cases for further study using platial mobility as a framework, we explore its benefits and methodological aspects toward developing better understanding for disaster management, disaster risk reduction and pandemics. We then discuss some of the illustrative use cases to clarify the concept of platial mobility and its application prospects in the areas of disaster management, disaster risk reduction and pandemics. These use cases, which include flood events and the ongoing COVID-19 pandemic, have led to displaced and restricted communities having to change practices and places, which would be particularly amenable to the conceptual framework developed in our work.
      PubDate: 2022-07-16
      DOI: 10.1007/s10109-022-00389-3
       
  • The geography of connectivity: a review of mobile positioning data for
           economic geography

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      Abstract: Abstract Connectivity between and within places is one of the cornerstones of geography. However, the data and methodologies used to capture connectivity are limited due to the difficulty in gathering and analysing detailed observations in time and space. Mobile phone data potentially offer a rich and unprecedented source of data, which is exhaustive in time and space following movements and communication activities of individuals. This approach to study the connectivity patterns of societies is still rather unexplored in economic geography. However, a substantial body of work in related fields provides methodological and theoretical foundations, which warrant an in-depth review to make it applicable in economic geography. This paper reviews and discusses the state-of-the-art in the analysis of mobile phone and positioning data, with a focus on call detail records. It identifies methodological challenges, elaborates on key findings for geography, and provides an outline for future research on the geography of connectivity.
      PubDate: 2022-07-12
      DOI: 10.1007/s10109-022-00388-4
       
  • Geographically weighted regression with the integration of machine
           learning for spatial prediction

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      Abstract: Abstract Conventional methods of machine learning have been widely used to generate spatial prediction models because such methods can adaptively learn the mapping relationships among spatial data with limited prior knowledge. However, the direct application of these methods to build a global model without considering spatial heterogeneity cannot accurately describe the local relationships among spatial variables, which might lead to inaccurate predictions. To avoid these shortcomings, we have presented a unified framework for handling spatial heterogeneity by incorporating the geographically weighted scheme into machine learning methods. The proposed framework has the potential to extend the existing models of machine learning for analysing heterogeneous spatial data. Furthermore, geographically weighted support vector regression (GWSVR) has been introduced as an implementation of the proposed framework. Experimental studies on environmental datasets were used to test the ability of model predictions. The results show that the mean absolute percentage error, normalized mean square error, and relative error percentage of the GWSVR model are 0.436, 0.903, and 0.558, respectively, when analysing soil metal chromium (Cr) concentrations and 0.221, 0.287, and 0.206, respectively, when predicting PM2.5 concentrations; these values are lower than those obtained using support vector regression, geographically weighted regression (GWR), and GWR-kriging models. These case studies have proved the validity and feasibility of the proposed framework.
      PubDate: 2022-07-03
      DOI: 10.1007/s10109-022-00387-5
       
 
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