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The Gorganrood watershed (GW) is experiencing considerable environmental change in the form of natural hazards and erosion, as well as deforestation, cultivation and development activities. As a result of this, different types of Land Cover/Land Use (LCLU) change are taking place on an intensive level in the area. This research study investigates the LCLU conditions upstream of this watershed for the years 1972, 1986, 2000 and 2014, using Landsat MSS, TM, ETM+ and OLI/TIRS images. LCLU maps for 1972, 1986, and 2000 were produced using pixel-based classification methods. For the 2014 LCLU map, Geographic Object-Based Image Analysis (GEOBIA) in combination with the data-mining capabilities of Gini and J48 machine-learning algorithms were used. The accuracy of the maps was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. The overall accuracy ranged from 89% to 95%, quantity disagreement from 2.1% to 6.6%, and allocation disagreement from 2.1% for 2014 to 2.7% for 2000. The results of this study indicate that a significant amount of change has occurred in the region, and that this has as a consequence affected ecosystem services and human activity. This knowledge of the LCLU status in the area will help managers and decision makers to develop plans and programs aimed at effectively managing the watershed into the future. [ABSTRACT FROM AUTHOR]

Vines and olives are two important and widespread traditional agricultural crops that are also connected to the Judeo–Christian–Muslim tradition. The goal of the research was to demonstrate the importance of using cartographical sources to obtain a more accurate and complete view of the past. To this end, the aims were: (1) to reconstruct the former agricultural land-use in three periods, 1873–1874, 1917, and 1943–1945; (2) to analyze the different spatial physical factors that could explain the spatial distribution of traditional agricultural landscapes; (3) to identify the changes which took place between the three reconstructed timestamps. The research employed different cartographic sources and the implemented analyses were conducted using GIS tools and methods. The results show that, in the past, the distribution of vines and olive groves greatly depended on several physical geographic factors (climate, slopes, direction). Nonetheless, human factors such as political instability, cultural and religious beliefs contributed as well. Moreover, this research showed how GIS has advanced historical geography research. Lastly, the research demonstrated that obtaining the most complete view of the past can be achieved by a combination of sources together with the use of GIS tools and methods. [ABSTRACT FROM AUTHOR]

There is no consensus on the importance of satellite images in the process of memorizing a route from a map image, especially if the route is displayed on the Internet using dynamic (animated) cartographic visualization. In modern dynamic maps built with JavaScript APIs, background layers can be easily altered by map users. The animation attracts people's attention better than static images, but it causes some perceptual problems. This study examined the influence of the number of turns on the effectiveness (correctness) and efficiency of memorizing the animated route on different cartographic backgrounds. The routes of three difficulty levels, based on satellite and road background, were compared. The results show that the satellite background was not a significant factor influencing the efficiency and effectiveness of route memorizing. Recordings of the eye movement confirmed this. The study reveals that there were intergroup differences in participants' visual behavior. Participants who described their spatial abilities as "very good" performed better (in terms of effectiveness and efficiency) in route memorizing tasks. For future research, there is a need to study route variability and its impact on participants' performance. Moreover, future studies should involve differences in route visualization (e.g., without and with ephemeral or permanent trail). [ABSTRACT FROM AUTHOR]

Providing long-term data about the evolution of railway networks in Europe may help us understand how European Union (EU) member states behave in the long-term, and how they can comply with present EU recommendations. This paper proposes a methodology for collecting data about railway stations, at the maximal extent of the French railway network, a century ago.The expected outcome is a geocoded dataset of French railway stations (gares), which: (a) links gares to each other, (b) links gares with French communes, the basic administrative level for statistical information. Present stations are well documented in public data, but thousands of past stations are sparsely recorded, not geocoded, and often ignored, except in volunteer geographic information (VGI), either collaboratively through Wikipedia or individually. VGI is very valuable in keeping track of that heritage, and remote sensing, including aerial photography is often the last chance to obtain precise locations. The approach is a series of steps: (1) meta-analysis of the public datasets, (2) three-steps fusion: measure-decision-combination, between public datasets, (3) computer-assisted geocoding for 'gares' where fusion fails, (4) integration of additional gares gathered from VGI, (5) automated quality control, indicating where quality is questionable. These five families of methods, form a comprehensive computer-assisted reconstruction process (CARP), which constitutes the core of this paper. The outcome is a reliable dataset—in geojson format under open license—encompassing (by January 2021) more than 10,700 items linked to about 7500 of the 35,500 communes of France: that is 60% more than recorded before. This work demonstrates: (a) it is possible to reconstruct transport data from the past, at a national scale; (b) the value of remote sensing and of VGI is considerable in completing public sources from an historical perspective; (c) data quality can be monitored all along the process and (d) the geocoded outcome is ready for a large variety of further studies with statistical data (demography, density, space coverage, CO2 simulation, environmental policies, etc.). [ABSTRACT FROM AUTHOR]

In this work, we present a novel approach to understand the quality of public transit system in resource constrained regions using user-generated contents. With growing urban population, it is getting difficult to manage travel demand in an effective way. This problem is more prevalent in developing cities due to lack of budget and proper surveillance system. Due to resource constraints, developing cities have limited infrastructure to monitor transport services. To improve the quality and patronage of public transit system, authorities often use manual travel surveys. But manual surveys often suffer from quality issues. For example, respondents may not provide all the detailed travel information in a manual travel survey. The survey may have sampling bias. Due to close-ended design (specific questions in the questionnaire), lots of relevant information may not be captured in a manual survey process. To address these issues, we investigated if user-generated contents, for example, Twitter data, can be used to understand service quality in Greater Mumbai in India, which can complement existing manual survey process. To do this, we assumed that, if a tweet is relevant to public transport system and contains negative sentiment, then that tweet expresses user's dissatisfaction towards the public transport service. Since most of the tweets do not have any explicit geolocation, we also presented a model that does not only extract users' dissatisfaction towards public transit system but also retrieves the spatial context of dissatisfaction and the potential causes that affect the service quality. It is observed that a Random Forest-based model outperforms other machine learning models, while yielding 0.97 precision and 0.88 F1-score. [ABSTRACT FROM AUTHOR]

The issues of urban sprawl are current in both global research as well as the sphere of activities by public authorities in developed and developing countries. Urban sprawl is a phenomenon that goes beyond the administrative boundaries of cities, which forces monitoring of the phenomenon on a wide territorial scale, i.e., regional and national. However, assessing the level of urban sprawl on such a scale still remains a research challenge in many countries. Poland is such an example, where there is a deficit in assessing the level of the phenomenon, its spatial specificity, as well as comparisons between other national urban areas. The presented research uses the urban morphology method to assess urban sprawl in Poland. The method assumes the use of square grids and building locations for the quantification of sprawl. Based on the 14 urban areas that aggregate 296 communes, it was pointed out that the level of urban sprawl in Poland is moderate. The results indicate that there is not a significant sprawl or compact structures. [ABSTRACT FROM AUTHOR]

Trajectory compression is an efficient way of removing noise and preserving key features in location-based applications. This paper focuses on the dynamic compression of trajectory in memory, where the compression accuracy of trajectory changes dynamically with the different application scenarios. Existing methods can achieve this by adjusting the compression parameters. However, the relationship between the parameters and compression accuracy of most of these algorithms is considerably complex and varies with different trajectories, which makes it difficult to provide reasonable accuracy. We propose a novel trajectory compression algorithm that is based on the finite element method, in which the trajectory is taken as an elastomer to compress as a whole by elasticity theory, and trajectory compression can be thought of as deformation under stress. The compression accuracy can be determined by the stress size that is applied to the elastomer. When compared with the existing methods, the experimental results show that our method can provide more stable, data-independent compression accuracy under the given stress parameters, and with reasonable performance. [ABSTRACT FROM AUTHOR]

In this article, we explore the relationship between cellular phone data and housing prices in Budapest, Hungary. We determine mobility indicators from one months of Call Detail Records (CDR) data, while the property price data are used to characterize the socioeconomic status at the Capital of Hungary. First, we validated the proposed methodology by comparing the Home and Work locations estimation and the commuting patterns derived from the cellular network dataset with reports of the national mini census. We investigated the statistical relationships between mobile phone indicators, such as Radius of Gyration, the distance between Home and Work locations or the Entropy of visited cells, and measures of economic status based on housing prices. Our findings show that the mobility correlates significantly with the socioeconomic status. We performed Principal Component Analysis (PCA) on combined vectors of mobility indicators in order to characterize the dependence of mobility habits on socioeconomic status. The results of the PCA investigation showed remarkable correlation of housing prices and mobility customs. [ABSTRACT FROM AUTHOR]

Mapping and understanding the differences in land cover and land use over time is an essential component of decision-making in sectors such as resource management, urban planning, and forest fire management, as well as in tracking of the impacts of climate change. Existing methods sometimes pose a barrier to the effective monitoring of changes in land cover and land use, since a threshold parameter is often needed and determined based on trial and error. This study aimed to develop an automatic and operational method for change detection on a large scale from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Super pixels were the basic unit of analysis instead of traditional individual pixels. T2 tests based on the feature vectors of temporal Normalized Difference Vegetation Index (NDVI) and land surface temperature were used for change detection. The developed method was applied to data over a predominantly vegetated area in northern Ontario, Canada spanning 120,000 sq. km from 2001–2016. The accuracies ranged between 78% and 88% for the NDVI-based test, from 74% to 86% for the LST-based test, and from 70% to 86% for the joint method compared with manual interpretation. Our proposed method for detecting land cover change provides a functional and viable alternative to existing methods of land cover change detection as it is reliable, repeatable, and free from uncertainty in establishing a threshold for change. [ABSTRACT FROM AUTHOR]

The article presents a method for determining and evaluating the threat to railway infrastructure from falling trees. The main objective was to identify the degree of threat according to three parameters: the height of tree stands, species composition, and vegetation health. Identification of the threat to individual railway sections would allow the creation of measures that could increase operational safety. Evaluation should be carried out at regular intervals to assess vegetation health during certain seasons. We, therefore, recommend the use of satellite images to provide a data source and regular data updates. The potential benefit is a reduction in railway infrastructure damage and an increase in traffic flow, especially during periods of exceptional weather conditions involving high wind, ice or heavy snow. The new method presented in the paper, based on data for railway infrastructure, law and other data sources in the Czech Republic, determines a Vegetation Threat Index for Railways (VTIR). We selected the Liberec Region as an example case study because of its large spread of wooded areas around railway lines and high incidence of railway traffic accidents. [ABSTRACT FROM AUTHOR]

In recent years, spatial multi-criteria decision analysis (MCDA) has been applied to different types of spatial problems, such as solar power site suitability. MCDA can be used to support the process of identifying suitable regions for solar energy projects. To the best of our knowledge, no study has addressed the problem of site evaluation for solar photovoltaic PV systems in the Riyadh region. Therefore, a spatial MCDA framework is proposed to perform a geospatial analysis of solar energy in the Riyadh region, which includes data collection, spatial analysis, a spatial decision support system and visualization. The methodology that was used to solve the site suitability problem is described. It involved the combination of a Geographical Information System (GIS) and the Analytic Hierarchy Process (GIS-AHP). The results show that the most suitable sites are in the north and northwest of the Riyadh region, with an area that represents 16,748 Km2 with an 80% suitability degree. In addition, it was proven that Afif is the largest suitable city. It has high solar radiation, at an average of 2.631687 MWh/m2/year, and low temperatures, at an average of 26.3 °C, as well as having flat areas with a slope under 5°. The results were validated using a sensitivity analysis model and also compared with those for ground-based stations. [ABSTRACT FROM AUTHOR]

The public's attitudes, emotions, and opinions reflect the state of society to a certain extent. Understanding the state and trends of public sentiment and effectively guiding the direction of sentiment are essential for maintaining social stability during disasters. Social media data have become the most effective resource for studying public sentiment. The TextBlob tool is used to calculate the sentiment value of tweets, and this research analyzed the public's sentiment state during Typhoon Haiyan, used the biterm topic model (BTM) to classify topics, explored the changing process of public discussion topics at different stages during the disaster, and analyzed the differences in people's discussion content under different sentiments. We also analyzed the spatial pattern of sentiment and quantitatively explored the influencing factors of the sentiment spatial differences. The results showed that the overall public sentiment during Typhoon Haiyan tended to be positive, that compared with positive tweets, negative tweets contained more serious disaster information and more urgent demand information, and that the number of tweets, population, and the proportion of the young and middle-aged populations were the dominant factors in the sentiment spatial differences. [ABSTRACT FROM AUTHOR]

Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses by comparing the modelled fluxes with the observed ones from eight sites equipped with eddy covariance stations which differ in terms of vegetation and climate type. Three potential ET methods (Penman-Monteith, Priestley-Taylor, and Blaney-Criddle models) and three actual ET models (the Advection-Aridity, the Granger and Gray, and the Antecedent Precipitation Index method) have been proposed. The findings show that the models performances differ from site to site and they depend on the vegetation and climate characteristics. Indeed, they show a wide range of error values ranging from 0.18 to 2.78. It has been not possible to identify a single model able to outperform the others in each biome, but in general, the Advection-Aridity approach seems to be the most accurate, especially when the model calibration in not carried out. It returns very low error values close to 0.38. When the calibration procedure is performed, the most accurate model is the Granger and Gray approach with minimum error of 0.13 but, at the same time, it is the most impacted by this process, and therefore, in a context of data scarcity, it results the less recommended for ET prediction. The performances of the investigated ET approaches have been furthermore tested in case of lack of measured data of soil heat fluxes and net radiation considering using empirical relationships based on meteorological data to derive these variables. Results show that, the use of empirical formulas to derive ET estimates increases the errors up to 200% with the consequent loss of model accuracy. [ABSTRACT FROM AUTHOR]

In developing countries, crime is a serious problem that affects the operation and viability of firms. Offenses such as vandalism, robbery, and theft raise the operating costs of firms and imposes on them indirect costs. The literature on spatial analysis of crime is vast; however, relatively little research has addressed business crime, especially in developing countries' cities. Spatial and temporal analysis of crime concentration represents a basic input for the design and implementation of appropriate prevention and control strategies. This article explores the spatial concentration and stability of thefts committed against commercial establishments in the city of Mexicali, Mexico, from 2009 to 2011 using the Gini coefficient, Lorenz curve, and decile maps. Results revealed that thefts were highly concentrated in a small percentage of urban basic geostatistical areas. Moreover, a portion of these areas were classified as having the highest deciles of thefts (hot spots) and remained in this group throughout the period. In both cases, the relationship between crime and place was close to the 80/20 rule, or the Pareto principle. [ABSTRACT FROM AUTHOR]

Accessibility analyses are an essential step in the evaluation and planning of urban infrastructures such as transport or pipeline networks. However, these studies generally produce sharply defined lines (called isovarones) or areas (called isovarone areas) that represent the same or similar accessibility. Uncertainties in the input data are usually not taken into account. The aim of this contribution is, therefore, to set up a structured framework that describes the integration of uncertainty information for accessibility analyses. This framework takes uncertainties in the input data, in the processing step, in the target variables, and in the final visualization into account. Particular attention is paid, on the one hand, to the impact of the uncertainties in the target values, as these are key factors for reasoning and decision making. On the other hand, the visualization component is emphasized by applying a dichotomous classification of uncertainty visualization methods. This framework leads to a large set of possible combinations of uncertainty categories. Five selected examples that have been generated with a new software tool and that cover important combinations are presented and discussed. [ABSTRACT FROM AUTHOR]

Small object detection in very-high-resolution (VHR) optical remote sensing images is a fundamental but challenaging problem due to the latent complexities. To tackle this problem, the MdrlEcf model is proposed by modifying deep reinforcement learning (DRL) and extracting the efficient convolution feature. Firstly, an efficient attention network is constructed by introducing the local attention into the convolutional neural network. Combining the shallow low-level features with rich detail descriptions and high-level features with more semantic meanings effectively, efficient convolution features can be obtained. By this, the attention network can effectively enhance the ability to extract small target features and suppressing useless features. Secondly, the efficient feature map is sent to the region proposal network constructed by modified DRL. Using the modified reward function, this model can accumulate more rewards to conduct the search process, and potentially generate effective subsequent proposals and classification scores. It also can increase the effectiveness of object locations and classifications for small targets. Quantitative and qualitative experiments are conducted to verify the detection performance of different models. The results show that the proposed MdrlEcf can effectively and accurately locate and identify related small objects. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems. [ABSTRACT FROM AUTHOR]

Structurally describing the portrayal-related information by using a standalone Digital Cartographic Model on top of a Digital Landscape Model has been proved applicable and beneficial for 2D mapping but has not yet been applied to 3D cadastre. This study, therefore, evaluates the applicability of digital cartographic model and the corresponding visualization pipeline for 3D cadastre in the context of Chinese urban cadastre. This research starts by identifying the requirements and design features of 3D cadastre mapping through a literature review and interviews with users and cartographers. Addressing the limitations of the existing general-purpose models, this paper proposes an ad hoc 3D cadastre digital cartographic model. The main developments of the proposed model are the inclusion of 3D content modeling, the support of the compound 3D symbols, and the introduction of the semantic transformation. The proposed model is then embedded into three parts of the cadastre visualization pipeline: the symbolic rule design, graphic content creation, and scene dissemination. The empirical result of qualitative proof-of-concept user tests supports that the proposed visualization pipeline is applicable and yields promising visualization results. The digital cartographic model-based visualization pipeline is a novel 3D cadastre mapping paradigm that facilitates designing, producing, sharing, and administrating. [ABSTRACT FROM AUTHOR]

Flood occurrence is increasing due to the expansion of urbanization and extreme weather like hurricanes; hence, research on methods of inundation monitoring and mapping has increased to reduce the severe impacts of flood disasters. This research studies and compares two methods for inundation depth estimation using UAV images and topographic data. The methods consist of three main stages: (1) extracting flooded areas and create 2D inundation polygons using deep learning; (2) reconstructing 3D water surface using the polygons and topographic data; and (3) deriving a water depth map using the 3D reconstructed water surface and a pre-flood DEM. The two methods are different at reconstructing the 3D water surface (stage 2). The first method uses structure from motion (SfM) for creating a point cloud of the area from overlapping UAV images, and the water polygons resulted from stage 1 is applied for water point cloud classification. While the second method reconstructs the water surface by intersecting the water polygons and a pre-flood DEM created using the pre-flood LiDAR data. We evaluate the proposed methods for inundation depth mapping over the Town of Princeville during a flooding event during Hurricane Matthew. The methods are compared and validated using the USGS gauge water level data acquired during the flood event. The RMSEs for water depth using the SfM method and integrated method based on deep learning and DEM were 0.34m and 0.26m, respectively. [ABSTRACT FROM AUTHOR]

The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. However, the landslide related database and research are still inadequate in these landslide-prone areas. The main purpose of this study is: (1) to prepare the multi-temporal landslide inventory map using geospatial platforms in the data-scarce environment; (2) to evaluate the landslide susceptibility map using weights of evidence (WoE) method in the Geographical Information System (GIS) environment at the district level; and (3) to provide a comprehensive understanding of recent developments, gaps, and future directions related to landslide inventory, susceptibility mapping, and risk assessment in the Indian context. Firstly, 293 landslides polygon were manually digitized using the BHUVAN (Indian earth observation visualization) and Google Earth® from 2011 to 2013. Secondly, a total of 14 landslide causative factors viz. geology, geomorphology, soil type, soil depth, slope angle, slope aspect, relative relief, distance to faults, distance to thrusts, distance to lineaments, distance to streams, distance to roads, land use/cover, and altitude zones were selected based on the previous study. Then, the WoE method was applied to assign the weights for each class of causative factors to obtain a landslide susceptibility map. Afterward, the final landslide susceptibility map was divided into five susceptibility classes (very high, high, medium, low, and very low classes). Later, the validation of the landslide susceptibility map was checked against randomly selected landslides using IDRISI SELVA 17.0 software. Our study results show that medium to very high landslide susceptibilities had occurred in the non-forest areas, mainly scrubland, pastureland, and barren land. The results show that medium to very high landslide susceptibilities areas are in the upper catchment areas of the Mandakini river and adjacent to the National Highways (107 and 07). The results also show that landslide susceptibility is high in high relative relief areas and shallow soil, near thrusts and faults, and on southeast, south, and west-facing steep slopes. The WoE method achieved a prediction accuracy of 85.7%, indicating good accuracy of the model. Thus, this landslide susceptibility map could help the local governments in landslide hazard mitigation, land use planning, and landscape protection. [ABSTRACT FROM AUTHOR]

In central and eastern European countries that joined the European Union (EU) in 2004 and 2007, there are strong connections between transport development and sustainable regional development. The needs for transport development in post-socialist countries are so high that it is difficult to finance them, and most governments have to choose between immediate and less urgent needs. The aim of this study was to determine the role played by sustainable transport growth in the improvement of regional cohesion and sustainable regional development with a use of geographical information systems' capabilities. The specific goals of the study were to: (i) determine the impact of EU subsidies on the regional transport development; (ii) identify threats to regional cohesion resulting from under-capitalised regions, (iii) examine geographical information systems' capabilities in periodical regional transport development monitoring. The analyses include evaluation of the regions' involvement in sustainable transport development as well as the presentation of direct and indirect results, such as road system improvement, with the use of GIS capabilities. It is particularly important to develop a system for monitoring the level of EU fund allocation with the use of GIS tools and the capabilities of geographic information systems. Such in-depth analysis would be useful for regional governments and decision-makers in creating and updating long term transport policies as well as monitoring long term regional development strategies. Both sustainable transport development and EU fund allocation can be monitored within the use of GIS tools, and such analyses should supplement the INSPIRE Geoportal scope. [ABSTRACT FROM AUTHOR]

The modifiable areal unit problem is of great importance in geographic science. The use of a specific zoning impacts the social and economic imbalances that can be generated in the deployment of services, facilities, and infrastructure. In this article, GIS is used together with simulation and optimization tools to analyse the effects of bus frequency changes in the levels of service and horizontal equity derived from different types of territorial zoning. The city of Palma (Balearic Islands, Spain) was chosen as a case study for the method, for which different geographical areas are used: neighbourhoods, census sections, cadastral blocks, and a 400 × 400 m mesh. The results show significant variations of the optimal frequencies obtained, depending on the type of zoning used. In general, smaller zonings show much higher sensitivity for the detection of imbalances between the population and bus service level. Likewise, orthogonal zonings also prove useful for identifying service and population concentration over other zonings. The use of large spatial units could lead to the misdiagnosis of needs and the implementation of actions that do not actually improve the level of service or the equity of the transport service. It is recommended to consider combining zonings of different sizes simultaneously, in order to accurately highlight imbalances and to argue for transport service improvements. [ABSTRACT FROM AUTHOR]

The reported lightning accidents that are available in the DesInventar database—which consist of 549 deaths, 498 injured people, 39 destroyed houses, and 741 damaged houses—were analyzed in terms of their geographical and temporal variation. The average lightning flash densities were calculated using zonal statistics using the geographic information system (GIS), referring to the respective raster maps generated based on Lightning Imaging Sensor (LIS) data from the Tropical Rainfall Measurement Mission (TRMM) Satellite. Hence, the variations of the lightning accidents—monthly and climate season-wise—in response to the lightning flash density were also reported. The calculated average lightning flash density in Sri Lanka is 8.26 flashes km−2 year−1, and the maximum average lightning flash density of 31.33 flashes km−2 year−1 is observed in April in a calendar year. April seems to be more vulnerable to lightning accidents, as the maximum number of deaths (150 deaths) and injuries (147 injuries) were recorded in this month. Most of the high-risk lightning accident regions that were identified in Sri Lanka are well known for agricultural activities, and those activities will eventually create the platform for lightning victims. In Sri Lanka, in a year, 12 people were killed and 11 people were injured, based on the reported accidents from 1974 to 2019. Conversely, a substantial increase in the number of deaths, injuries, and incidents of property damage has been observed in the last two decades (2000–2019). On average, for the period from 2000 to 2019, 18 people were killed and 16 people were injured per year. Furthermore, considering the population of the country, 0.56 people per million per year were killed, and 0.51 people per million per year were injured due to lightning accidents based on the reported accidents from 1974 to 2019. Moreover, for the 2000–2019 period, these estimated values are significantly higher; 0.86 people per million per year were killed, and 0.77 people per million per year were injured. [ABSTRACT FROM AUTHOR]

When generalizing a group of objects, displacement is an essential operation to resolve the conflicts arising between them due to enlargement of their symbol sizes and reduction of available map space. Although there are many displacement methods, most of them are rather complicated. Therefore, more practical methods are still needed. In this article, a new building displacement approach is proposed. For this purpose, buildings are grouped and zones are created for them in the blocks via Voronoi tessellation and buffering. Linear patterns are then detected through buffer analyses and the respective zones are narrowed to be able to preserve these patterns. After all the buildings are displaced inside their zones, grid points are generated and then weighted through kernel density estimation and buffer analyses to find suitable locations. Accordingly, the buildings are displaced toward the computed locations iteratively. The proposed approach directly enforces minimum distance and positional accuracy constraints while several indirect mechanisms are used for preserving spatial patterns and relationships. For the quality evaluation of the displacement, the angle, length and shape comparison measures are introduced, computed based on the (Delaunay) triangles or the azimuth comparison measure of the connection lines, generated for the buildings. The quality evaluation criteria are yielded according to the visual assessment of the displacement quality and the quantitative analysis of the measures. The findings demonstrate that the proposed approach is quite effective and practical for zonal building displacement. [ABSTRACT FROM AUTHOR]

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize a graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image; (2) region adjacency graph conversion; and (3) the graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements, such as walls, doors, and symbols, as well as spatial elements, such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data. [ABSTRACT FROM AUTHOR]

The construction and operation of air transport systems (ATS) needs huge investment, so its performance is of wide concern. The influences of social and economic factors in different regions must be considered when evaluating ATS performance. In this paper, a model combining data envelopment analysis, stochastic frontier analysis, and bootstrap technique is adopted to evaluate the 'real' performance of the air transport system in China. The evaluation results show the ATS performance in different regions. Social and economic factors are proved to pose influences on provincial ATS efficiency. Scale efficiency is the main factor that restricts the efficiency of China's ATS. Technological change has determined the trend of ATS total factor productivity. The research results may implicate that improvements can be gained by modifying airspace limitations and regulatory conditions that impose significant constraints on ATS. The importance of ATS technological development strategy and the legitimacy of air transport modernization policy are also supported. [ABSTRACT FROM AUTHOR]

Due to their multiple sources and structures, big spatial data require adapted tools to be efficiently collected, summarized and analyzed. For this purpose, data are archived in data warehouses and explored by spatial online analytical processing (SOLAP) through dynamic maps, charts and tables. Data are thus converted in data cubes characterized by a multidimensional structure on which exploration is based. However, multiple sources often lead to several data cubes defined by heterogeneous dimensions. In particular, dimensions definition can change depending on analyzed scale, territory and time. In order to consider these three issues specific to geographic analysis, this research proposes an original data cube metamodel defined in unified modeling language (UML). Based on concepts like common dimension levels and metadimensions, the metamodel can instantiate constellations of heterogeneous data cubes allowing SOLAP to perform multiscale, multi-territory and time analysis. Afterwards, the metamodel is implemented in a relational data warehouse and validated by an operational tool designed for a social economy case study. This tool, called "Racines", gathers and compares multidimensional data about social economy business in Belgium and France through interactive cross-border maps, charts and reports. Thanks to the metamodel, users remain independent from IT specialists regarding data exploration and integration. [ABSTRACT FROM AUTHOR]

Interest in and use of 3D visualizations for analysis and communication of flooding risks has been increasing. At the same time, an ecosystem of 3D user interfaces has also been emerging. Together, they offer exciting potential opportunities for flood visualization. In order to understand how we turn potential into real value, we need to develop better understandings of technical workflows, capabilities of the resulting systems, their usability, and implications for practice. Starting with existing geospatial datasets, we develop single user and collaborative visualization prototypes that leverage capabilities of the state-of-the art HoloLens 2 mixed reality system. By using the 3D displays, positional tracking, spatial mapping, and hand- and eye-tracking, we seek to unpack the capabilities of these tools for meaningful spatial data practice. We reflect on the user experience, hardware performance, and usability of these tools and discuss the implications of these technologies for flood risk management, and broader spatial planning practice. [ABSTRACT FROM AUTHOR]

Analysis of long-term rainfall trends provides a wealth of information on effective crop planning and water resource management, and a better understanding of climate variability over time. This study reveals the spatial variability of rainfall trends in Sri Lanka from 1989 to 2019 as an indication of climate change. The exclusivity of the study is the use of rainfall data that provide spatial variability instead of the traditional location-based approach. Henceforth, daily rainfall data available at Climate Hazards Group InfraRed Precipitation corrected with stations (CHIRPS) data were used for this study. The geographic information system (GIS) is used to perform spatial data analysis on both vector and raster data. Sen's slope estimator and the Mann–Kendall (M–K) test are used to investigate the trends in annual and seasonal rainfall throughout all districts and climatic zones of Sri Lanka. The most important thing reflected in this study is that there has been a significant increase in annual rainfall from 1989 to 2019 in all climatic zones (wet, dry, intermediate, and Semi-arid) of Sri Lanka. The maximum increase is recorded in the wet zone and the minimum increase is in the semi-arid zone. There could be an increased risk of floods in the southern and western provinces in the future, whereas areas in the eastern and southeastern districts may face severe droughts during the northeastern monsoon. It is advisable to introduce effective drought and flood management and preparedness measures to reduce the respective hazard risk levels. [ABSTRACT FROM AUTHOR]

Solar radiation may be shielded by the terrain relief before reaching the Martian surface, especially over some rugged terrains. Yet, to date, no comprehensive studies on the spatial structure of shielded astronomical solar radiation (SASR) and the possible sunshine duration (PSD) on Mars have been conducted by previous researchers. Previous studies generally ignored the influences of the terrain on the SASR and PSD, which resulted in a corresponding unexplored field on SASR. The purpose of this paper is to study the Martian spatial-temporal structure of SASR and the PSD under terrain influences. In this paper, the theory of Earth's SASR, the previous Martian SASR model and the theory of planetary science were combined to propose the SASR model that can be applied to Mars. Then, with the spectrum method theory of geography, we defined two new concepts of spectrums to explore the spatial-temporal distribution of SASR and PSD in different Martian landforms. We found SASR and PSD on Mars were significantly influenced by terrain relief and latitude and showed sufficient regularity, which can be concluded as a gradual attenuation with terrain relief and a regularity of latitude anisotropy. The latitude anisotropy feature is a manifestation of the terrain shielding effect. With the latitude varying, SASR and PSD at different temporal scale generally showed different features with those of Earth, which may be attributed to the imbalanced seasons caused by Martian moving orbits and velocity. Compared to PSD, SASR showed more regular variation under terrain relief and was more influenced by the terrain relief which revealed that SASR is more sensitive to terrain relief than PSD. Additionally, the critical area is a quantitative index to reflect the stable spatial structure of SASR and PSD in different landforms and may be viewed as the minimum test region of sample areas. The corresponding result of the experiments herein indicated that either spectrum can effectively depict the spatial-temporal distribution of SASR and PSD on Mars under terrain relief and deepen the understanding of the variation of SASR and PSD influences by terrain. The critical area of either spectrum can be employed to explore and determine the stable spatial structure of SASR and PSD in different landforms. The proposed Martian SASR model and the new spectral method theory shed new light on revealing the spatial-temporal structure of SASR and PSD under terrain influences on Mars. [ABSTRACT FROM AUTHOR]

It is acknowledged that various types of thematic maps emphasize different aspects of mapped phenomena and thus support different map users' tasks. To provide empirical evidence, a user study with 366 participants was carried out comparing three map types showing the same input data. The aim of the study is to compare the effect of using choropleth, graduated symbols, and isoline maps to solve basic map user tasks. Three metrics were examined: two performance metrics (answer accuracy and time) and one subjective metric (difficulty). The results showed that the performance metrics differed between the analyzed map types, and better performances were recorded using the choropleth map. It was also proven that map users find the most commonly applied type of the map, choropleth map, as the easiest. In addition, the subjective metric matched the performance metrics. We conclude with the statement that the choropleth map can be a sufficient solution for solving various tasks. However, it should be remembered that making this type of map correctly may seem easy, but it is not. Moreover, we believe that the richness of thematic cartography should not be abandoned, and work should not be limited to one favorable map type only. [ABSTRACT FROM AUTHOR]

Tidal flats (non-vegetated area) are soft-sediment habitats that are alternately submerged and exposed to the air by changeable tidal levels. The tidal flat dynamics research mainly utilizes the cell-level comparisons between the consecutive snapshots, but the in-depth study requires more detailed information of the dynamic activities. To better track, represent, and analyze tidal flats' dynamic activities, this study proposes an integrated approach of a three-level Geographic Information Science (GIS) framework and a graph model. In the three-level GIS framework, the adjacent cells are assembled as the objects, and the objects on different time steps are linked as lifecycles by tracking the predecessor–successor relationships. Furthermore, eleven events are defined to describe the dynamic activities throughout the lifecycles. The graph model provides a better way to represent the lifecycles, and graph operators are utilized to facilitate the event analysis. The integrated approach is applied to tidal flats' dynamic activities in the southwest tip of Florida Peninsula from 1984 to 2018. The results suggest that the integrated approach provides an effective way to track, represent, and analyze the dynamic activities of tidal flats, and it offers a novel perspective to examine other dynamic geographic phenomena with large spatiotemporal scales. [ABSTRACT FROM AUTHOR]

Forecasting the motion of surrounding vehicles is necessary for an autonomous driving system applied in complex traffic. Trajectory prediction helps vehicles make more sensible decisions, which provides vehicles with foresight. However, traditional models consider the trajectory prediction as a simple sequence prediction task. The ignorance of inter-vehicle interaction and environment influence degrades these models in real-world datasets. To address this issue, we propose a novel Dynamic and Static Context-aware Attention Network named DSCAN in this paper. The DSCAN utilizes an attention mechanism to dynamically decide which surrounding vehicles are more important at the moment. We also equip the DSCAN with a constraint network to consider the static environment information. We conducted a series of experiments on a real-world dataset, and the experimental results demonstrated the effectiveness of our model. Moreover, the present study suggests that the attention mechanism and static constraints enhance the prediction results. [ABSTRACT FROM AUTHOR]

Aquaculture production in Tanzania has increased in recent years, responding to an increased demand for fish, but the scale and productivity of smallholder aquaculture remains below the level needed to support significant sector growth in Tanzania. This study assesses, through geospatial analyses, the suitability for freshwater pond farming of Oreochromis niloticus and Clarias gariepinus in Tanzania, by assessing the geographical distribution of seven criteria (water availability, water temperature, soil texture, terrain slope, availability of farm inputs, potential farm-gate sales, and access to local markets) identified as important for fish pond farming. The criteria were developed and standardized from 15 sub-criteria, which were classified into a four-level suitability scale based on physical scores. The individual weights of the different criteria in the overall GIS suitability assessment were determined through a multi-criteria evaluation. The final results were validated and compared through field observations, interviews with 89 rural and 11 urban aquaculture farmers, and a questionnaire survey with 16 regional fisheries officers. Our results indicate that there is a good potential for aquaculture in Tanzania. Almost 60% of Tanzania is assessed as being suitable and 40% as moderately suitable for small-scale subsistence pond farming, which is the dominating fish farming practice currently. The corresponding figures for medium-scale commercial farming, which many regions expect to be the dominating farming method within ten-years, were 52% and 47% respectively. The availability of water was the most limiting factor for fish pond farming, which was confirmed by both farmers and regional fisheries officers, and assessed as being "suitable" in only 28% of the country. The availability of farm-gate sales and local markets were "moderate suitable" to "suitable" and were seen as a constraint for commercial farms in rural areas. The availability of farm inputs (agriculture waste and manure) was overall good (26% very suitable and 32% suitable), but high-quality fish feed was seen as a constraint to aquaculture development, both by farmers and regional fisheries officers. Soil, terrain, and water temperature conditions were assessed as good, especially at low altitudes and in regions close to the sea and south of Lake Victoria. [ABSTRACT FROM AUTHOR]

Public bike-sharing is eco-friendly, connects excellently with other transportation modes, and provides a means of mobility that is highly suitable in the current era of climate change. This study proposes a methodology for inferring the bike trip purpose based on bike-share and point-of-interest (POI) data. Because the purpose of a trip involves decision-making, its inference necessitates an understanding of the spatiotemporal complexity of human activities. Thus, the spatiotemporal features affecting bike trips were selected from the bike-share data, and the land uses at the origin and destination of the trips were extracted from the POI data. During POI type embedding, the data were augmented considering the geographical distance between the POIs and the number of bike rentals at each bike station. We further developed a ground truth data construction method that uses temporal mobile and POI data. The inference model was built using machine learning and applied to experiments involving bike stations in Seocho-gu, Seoul, Korea. The experimental results revealed that optimal performance was achieved with the use of decision tree algorithms, as demonstrated by a 78.95% overall accuracy and 66.43% F1-score. The proposed method contributes to a better understanding of the causes of movement within cities. [ABSTRACT FROM AUTHOR]

Urban Growth Boundary (UGB) is a growth management policy that designates specific areas where growth should be concentrated in order to avoid urban sprawl. The objective of such a boundary is to protect agricultural land, open spaces and the natural environment, as well as to use existing infrastructure and public services more efficiently. Due to the inherent heterogeneity and complexity of settlements, UGBs in Germany are currently created manually by experts. Therefore, every dataset is linked to a specific area, investigation period and dedicated use. Clearly, up-to-date, homogeneous, meaningful and cost-efficient delineations created automatically are needed to avoid this reliance on manually or semi-automatically generated delineations. Here, we present an aggregative method to produce UGBs using building footprints and generally available topographic data as inputs. It was applied to study areas in Frankfurt/Main, the Hanover region and rural Brandenburg while taking full account of Germany's planning and legal framework for spatial development. Our method is able to compensate for most of the weaknesses of available UGB data and to significantly raise the accuracy of UGBs in Germany. Therefore, it represents a valuable tool for generating basic data for future studies. Application elsewhere is also conceivable by regionalising the employed parameters. [ABSTRACT FROM AUTHOR]

With rapid population growth, there is an increasing demand for vertical use of space. The wide spread of complex and high-rise buildings, as well as the increasing number of infrastructure above or underground, requires new methods for efficient management of land property. 3D cadastre has, thus, become a necessity for land administration. However, the success of 3D cadastral systems relies on the definition of legal and institutional frameworks and requires implementing performant technical solutions. The potential of BIM and 3D GIS in this field has been demonstrated by several authors. However, cadastral development is strongly related to the national context of each country in terms of laws, institutions, etc. In this paper, an integrated approach based on BIM and 3D GIS for the implementation of a 3D cadastre in Morocco is presented. This approach demonstrates the relevance of such integration for the efficient management of cadastral information. First, a Conceptual Data Model (CDM) based on an extension of CityGML, was proposed for the management of cadastral information in Morocco. Then, a BIM modeling process was developed according to the model's specifications and then translated to CityGML format. After that, a 3D Geodatabase was implemented in ArcGIS based on the proposed CDM. Our method was applied to a case of co-ownership building, showing several difficulties and limits in terms of 2D representation. The results show several advantages in terms of representation and management of 3D cadastral objects. In addition, some improvements are proposed to extend the concept of co-owner share to a volumetric calculation. [ABSTRACT FROM AUTHOR]

Infrastructures play an important role in urbanization and economic activities but are vulnerable. Due to unavailability of accurate subsurface infrastructure maps, ensuring the sustainability and resilience often are poorly recognized. In the current paper a 3D topographical predictive model using distributed geospatial data incorporated with evolutionary gene expression programming (GEP) was developed and applied on a concrete-face rockfill dam (CFRD) in Guilan province- northern to generate spatial variation of the subsurface bedrock topography. The compared proficiency of the GEP model with geostatistical ordinary kriging (OK) using different analytical indexes showed 82.53% accuracy performance and 9.61% improvement in precisely labeled data. The achievements imply that the retrieved GEP model efficiently can provide accurate enough prediction and consequently meliorate the visualization insights linking the natural and engineering concerns. Accordingly, the generated subsurface bedrock model dedicates great information on stability of structures and hydrogeological properties, thus adopting appropriate foundations. [ABSTRACT FROM AUTHOR]

Urban functional regions are essential information in parsing urban spatial structure. The rapid and accurate identification of urban functional regions is important for improving urban planning and management. Thanks to its low cost and fast data update characteristics, the Point of Interest (POI) is one of the most common types of open access data. It mainly identifies urban functional regions by analyzing the potential correlation between POI data and the regions. Even though this is an important manifestation of the functional region, the spatial correlation between regions is rarely considered in previous studies. In order to extract the spatial semantic information among regions, a new model, called the Block2vec, is proposed by using the idea of the Skip-gram framework. The Block2vec model maps the spatial correlation between the POIs, as well as the regions, to a high-dimensional vector, in which classification of urban functional regions can be better performed. The results from cluster analysis showed that the high-dimensional vector extracted can well distinguish the regions with different functions. The random forests classification result (Overall accuracy = 0.7186, Kappa = 0.6429) illustrated the effectiveness of the proposed method. This study also verified the potential of the sentence embedding model in the semantic information extraction of POIs. [ABSTRACT FROM AUTHOR]

With the exponential growth of numerous drone operations ranging from infrastructure monitoring to even package delivery services, the integration of UAS in the smart city transportation systems is an actual task that requires radically new, sustainable (safe, secure, with minimum environmental impact and life cycle cost) solutions. The primary objective of this proposed option is the definition of routes as desired and commanded trajectories and their autonomous execution. The airspace structure and fixed routes are given in the global GPS reference system with supporting GIS mapping. The concept application requires a series of further studies and solutions as drone trajectory (or corridor) following by an autonomous trajectory tracking control system, coupled with autonomous conflict detection, resolution, safe drone following, and formation flight options. The second part of the paper introduces such possible models and shows some results of their verification tests. Drones will be connected with the agency, designed trajectories to support them with factual information on trajectories and corridors. While the agency will use trajectory elements to design fixed or desired trajectories, drones may use the conventional GPS, infrared, acoustic, and visual sensors for positioning and advanced navigation. The accuracy can be improved by unique markers integrated into the infrastructure. [ABSTRACT FROM AUTHOR]

Multifractal theory provides a reliable method for the scientific quantification of the geomorphological features of basins. However, most of the existing research has investigated small and medium-sized basins rather than complex and large basins. In this study, the Yellow River Basin and its sub-basins were selected as the research areas, and the generalized fractal dimension and multifractal spectrum were computed and analyzed with a multifractal technique based on the slope distribution probability. The results showed that the Yellow River Basin and its sub-basins exhibit clear multifractal characteristics, which indicates that the multifractal theory can be applied well to the analysis of large-scale basin geomorphological features. We also concluded that the region with the most uneven terrain is the Yellow River Downstream Basin with the "overhanging river", followed by the Weihe River Basin, the Yellow River Mainstream Basin, and the Fenhe River Basin. Multifractal analysis can reflect the geomorphological feature information of the basins comprehensively with the generalized fractal dimension and the multifractal spectrum. There is a strong correlation between some common topographic parameters and multifractal parameters, and the correlation coefficients between them are greater than 0.8. The results provide a scientific basis for analyzing the geomorphic characteristics of large-scale basins and for the further research of the morphogenesis of the forms. [ABSTRACT FROM AUTHOR]

Standardized delimiting of local climate zones (LCZ) will be better applicable to the urban adaptation to climate change when the ecohydrological properties of LCZ units are known. Therefore, the properties of LCZ units based on the methodology of ecohydrological zoning of the urban landscape, which was created in GIS as a basis for planning blue-green infrastructure of cities in the Czech Republic, are presented in the paper. The goal of this study is to compare approaches and results of our own ecohydrological zonation and standardized LCZ delimiting in the city of Pilsen. Both methodological approaches differ in input data, resolution details and parameters used. The results showed that the areas of the individual LCZ classes show different levels of ecohydrological qualities. Internal heterogeneity of LCZ classes demonstrated by variance of ecohydrological parameters' values can be partly explained by different techniques and data sources for delimitation of both zonations, but by different sets of delimitation criteria. The discussion is held on the importance of terrain slope for supplementing the LCZ classification. A case study can be a stimulus for further development of holistic urban zoning methodologies that would take into account both climatological and ecohydrological conditions. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing interest in urban geomorphology both for its applications in terms of landscape planning, and its historical, cultural, and scientific interest. Due to recent urban growth, the identification of landforms in cities is difficult, particularly in Mediterranean and central European cities, characterized by more than 1000 years of urban stratification. By comparing and overlapping 19th-century cartography and modern topography from remote sensing data, this research aims to assess the morphological evolution of the city of Genoa (Liguria, NW Italy). The analysis focuses on a highly detailed 1:2'000 scale map produced by Eng. Ignazio Porro in the mid-19th century. The methodology, developed in QGIS, was applied on five case studies of both hillside and valley floor areas of the city of Genoa. Through map overlay and digitalization of elevation data and contour lines, it was possible to identify with great accuracy the most significant morphological transformations that have occurred in the city since the mid-19th century. In addition, the results were validated by direct observation and by drills data of the regional database. The results allowed the identification and quantification of the main anthropic landforms. The paper suggests that the same methodology can be applied to other historical urban contexts characterized by urban and architectural stratification. [ABSTRACT FROM AUTHOR]

Black soil is fertile, abundant with organic matter (OM) and is exceptional for farming. The black soil zone in northeast China is the third-largest black soil zone globally and produces a quarter of China's commodity grain. However, the soil organic matter (SOM) in this zone is declining, and the quality of cultivated land is falling off rapidly due to overexploitation and unsustainable management practices. To help develop an integrated protection strategy for black soil, this study aimed to identify the primary factors contributing to SOM degradation. The geographic detector, which can detect both linear and nonlinear relationships and the interactions based on spatial heterogeneous patterns, was used to quantitatively analyze the natural and anthropogenic factors affecting SOM concentration in northeast China. In descending order, the nine factors affecting SOM are temperature, gross domestic product (GDP), elevation, population, soil type, precipitation, soil erosion, land use, and geomorphology. The influence of all factors is significant, and the interaction of any two factors enhances their impact. The SOM concentration decreases with increased temperature, population, soil erosion, elevation and terrain undulation. SOM rises with increased precipitation, initially decreases with increasing GDP but then increases, and varies by soil type and land use. Conclusions about detailed impacts are presented in this paper. For example, wind erosion has a more significant effect than water erosion, and irrigated land has a lower SOM content than dry land. Based on the study results, protection measures, including conservation tillage, farmland shelterbelts, cross-slope ridges, terraces, and rainfed farming are recommended. The conversion of high-quality farmland to non-farm uses should be prohibited. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess and visualize the Quality of Life provided by urban space as a place of residence. The proposed methodology, after its theoretical documentation, is implemented in Athens Metropolitan Area, Greece. For the evaluation of Urban Quality of Life, a complex index is constructed by using multicriteria analysis. For this purpose, Quality of Life controlling factors such as built space, natural, socioeconomic, and cultural environment, infrastructure and services, and the quality of housing were analyzed within a GIS environment. The mapping of this index led to the identification of areas with different levels of Quality of Life. The results of the research can lead to more effective decision making regarding the planning of targeted actions and the distribution of financial resources to improve the Quality of Life of the residents in urban areas. [ABSTRACT FROM AUTHOR]

Land-use change is a typical geographic evolutionary process characterized by spatial heterogeneity. As such, the driving factors, conversion rules, and rate of change vary for different regions around the world. However, most cellular automata (CA) models use the same transition rules for all cells in the model space when simulating land-use change. Thus, spatial heterogeneity change is ignored in the model, which means that these models are prone to over- or under simulation, resulting in a large deviation from reality. An effective means of accounting for the influence of spatial heterogeneity on the quality of the CA model is to establish a partitioned model based on cellular space partitioning. This study established a partitioned, dual-constrained CA model using the area-weighted frequency of land-use change (AWFLUC) to capture its spatial heterogeneity. This model was used to simulate the land-use evolution of the Dianchi Lake watershed. First, the CA space was divided into subzones using a dual-constrained spatial clustering method. Second, an artificial neural network (ANN) was used to automatically acquire conversion rules to construct an ANN-CA model of land-use change. Finally, land-use changes were simulated using the ANN-CA model based on data from 2006 to 2016, and model reliability was validated. The experimental results showed that compared with the non-partitioned CA model, the partitioned counterpart was able to improve the accuracy of land-use change simulation significantly. Furthermore, AWFLUC is an important indicator of the spatial heterogeneity of land-use change. The shapes of the division spaces were more similar to reality and the simulation accuracy was higher when AWFLUC was considered as a land-use change characteristic. [ABSTRACT FROM AUTHOR]

In a post-earthquake scenario, the semantic enrichment of 3D building models with seismic damage is crucial from the perspective of disaster management. This paper aims to present the methodology and the results for the Level of Detail 3 (LOD3) building modelling (after an earthquake) with the enrichment of the semantics of the seismic damage based on the European Macroseismic Scale (EMS-98). The study area is the Vrisa traditional settlement on the island of Lesvos, Greece, which was affected by a devastating earthquake of Mw = 6.3 on 12 June 2017. The applied methodology consists of the following steps: (a) unmanned aircraft systems (UAS) nadir and oblique images are acquired and photogrammetrically processed for 3D point cloud generation, (b) 3D building models are created based on 3D point clouds and (c) 3D building models are transformed into a LOD3 City Geography Markup Language (CityGML) standard with enriched semantics of the related seismic damage of every part of the building (walls, roof, etc.). The results show that in following this methodology, CityGML LOD3 models can be generated and enriched with buildings' seismic damage. These models can assist in the decision-making process during the recovery phase of a settlement as well as be the basis for its monitoring over time. Finally, these models can contribute to the estimation of the reconstruction cost of the buildings. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the spread of COVID-19. This paper uses geotagged tweets data to reveal the spatiotemporal human mobility patterns during this COVID-19 pandemic in New York City. With New York City open data, human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces. This study further compares human mobility patterns by land use types based on an open social media platform (Twitter) and the human mobility patterns revealed by Google Community Mobility Report cell phone location, indicating that in some applications, open-access social media data can generate similar results to private data. The results of this study can be further used for human mobility analysis and the battle against COVID-19. [ABSTRACT FROM AUTHOR]

Road networks are the main source of mortality for many species. Amphibians, which are in global decline, are the most road-killed fauna group, due to their activity patterns and preferred habitats. Many different methodologies have been applied in modeling the relationship between environment and road-kills events, such as logistic regression. Here, we compared the performance of five regression techniques to relate amphibians' road-kill frequency to environmental variables. For this, we surveyed three country roads in northern Portugal in search of road-killed amphibians. To explain the presence of road-kills, we selected a set of environmental variables important for the presence of amphibians and the occurrence of road-kills. We compared the performances of five modeling techniques: (i) generalized linear models, (ii) generalized additive models, (iii) random forest, (iv) boosted regression trees, and (v) geographically weighted regression. The boosted regression trees and geographically weighted regression techniques performed the best, with a percentage of deviance explained between 61.8% and 76.6% and between 55.3% and 66.7%, respectively. Moreover, the geographically weighted regression showed a great advantage over the other techniques, as it allows mapping local parameter coefficients as well as local model performance (pseudo-R2). The results suggest that geographically weighted regression is a useful tool for road-kill modeling, as well as to better visualize and map the spatial variability of the models. [ABSTRACT FROM AUTHOR]

Islands as peripheral and ultra-peripheral are typically highlighted as ecologically sensitive areas to human activities due to the tremendous biological diversity of beings and the future possibility of habitat loss. In this regard, the comprehension of the land occupation dynamics and trends in the ultra-peripheral territories is crucial to attempt long-lasting regional sustainability, as is the island region's case. Therefore, the present article aims to analyze the trends and dynamics of the land-use changes on the European Archipelagos of the Macaronesia Region over the last three decades, using the CORINE (Coordination of Information on the Environment) data. Some of the obtained results show that about 3.4% of the Azores' surface is characterized mainly by discontinuous urban fabric, representing 67% of the total urban fabric of the Azores over the last thirty years. Additionally, in Madeira Archipelago, the land is mainly occupied by forest and semi-natural areas, representing almost three-thirds of the territory. A similar scenario is verified in the Canary Islands, where forests and semi-natural areas represent approximately three-quarters of the territory. Once more, this study shows the relevance of the island areas' unique character, which should be preserved and protected. Therefore, the priorities must be defined and established management strategies that are significant for the well-being of these highly valued areas. Moreover, the study showed that notable changes had occurred in the period 1990–2018 in this landscape. Hence there is a need for appropriate measures to mitigate these negative impacts on the environment. [ABSTRACT FROM AUTHOR]