Geospatial Features Research Articles

Database Construction and Application of Large-scale Geospatial Data

Abstract. Geospatial data refers to data that related to spatial location. It is the abstract representation of objects, events, or other characteristic information located on or near the surface of the Earth. It characterizes attribute features, spatial features, and associated information of geospatial features. Geospatial data commonly include various data types such as point, polyline, polygon, and so on. Geospatial database is the basic method to store and manage geospatial data. It is also the important component of geographic information systems (GIS). Geospatial database generally stores data in a specific structure in a medium, which can achieve efficient management of geospatial data. Users can add, modify, and delete data efficiently. Furthermore, query retrieval and statistical analysis can be quickly performed based on geospatial database. In this paper, distributed geospatial database is constructed by using database partition tables storage technology. Meanwhile, database construction software is developed to achieve the construction, management, and application of large-scale geospatial data. It can improve data import, query and retrieval efficiency significantly. The database application service system can directly connect to the database for displaying statistical analysis results. At the same time, geospatial data are processed to vector tiles, which stored in the tile database. Furthermore, standard online service is then published, which can be used for database management and application service system. It can be used for natural resource management work such as basic surveying, survey and monitoring, and so on.

Factors affecting Kaposi’s sarcoma-associated herpesvirus transmission in rural Ugandan households, a longitudinal study

BackgroundWe report the impact of HIV infection within a household on oral Kaposi’s sarcoma-associated herpesvirus (KSHV) shedding.MethodsWe enrolled 469 individuals from 90 households. Mouthwash rinse samples collected at three monthly visits were analyzed for KSHV DNA using quantitative polymerase chain reaction (qPCR). Generalized linear mixed effects logistic models were applied to analyze factors associated with KSHV ever shedding, and among shedders, always versus intermittent shedding. Linear mixed effects models were applied to models of KSHV viral loads. Intraclass correlation coefficients (ICCs) were calculated to assess the contribution of household-level factors to variations in shedding probabilities. Hotspot analyses of geospatial feature clusters were calculated using Getis-Ord Gi* statistic and visualized using inverse distance weighted interpolation.ResultsAnalyses included 340 KSHV seropositive individuals, aged 3 + years, with qPCR results from 89 households. Forty households had 1 + persons living with HIV (PLWH), while 49 had none. Among participants, 149(44%) were KSHV ever shedders. Of 140 who shed KSHV at two or more visits, 34(24%) were always shedders. Increasing number of KSHV seropositive household members was significantly associated with ever shedding [Odds ratio(OR) (95% Confidence Interval(95%CI)):1.14(1.03,1.26);p = 0.013]. Among KSHV shedders, a statistically significant age-related trend was identified with 10–19 years being more likely to be always shedders (type III test p = 0.039) and to have higher viral loads (type III test p = 0.027). In addition, higher viral loads were significantly associated with increasing number of household members [coefficient(95%CI):0.06(0.01,0.12);p = 0.042], increasing number of KSHV seropositive members [coefficient(95%CI):0.08(0.01,0.15);p = 0.021], and living in households with 1 + PLWH [coefficient(95%CI):0.51(0.04,0.98);p = 0.033]. Always shedders exhibited higher viral loads than intermittent shedders [coefficient(95%CI):1.62(1.19,2.05);p < 0.001], and viral loads increased with the number of visits where KSHV DNA was detected in saliva (type III test p < 0.001). Household-level factors attributed for 19% of the variability in KSHV shedding (ICC:0.191;p = 0.010). Geospatial analysis indicated overlapping hotspots of households with more KSHV seropositive individuals and KSHV shedders, distinct from areas where PLWH were clustered.DiscussionKSHV oral shedding is influenced by multiple factors at the individual, household, and regional levels. To mitigate ongoing KSHV transmission a comprehensive understanding of factors contributing to oral KSHV reactivation and transmission within households is needed.

Factors affecting Kaposi's sarcoma-associated herpesvirus transmission in rural Ugandan households, a longitudinal study.

We report the impact of HIV infection within a household on oral Kaposi's sarcoma-associated herpesvirus (KSHV) shedding. We enrolled 469 individuals from 90 households. Mouthwash rinse samples collected at three monthly visits, were analyzed for KSHV DNA using quantitative polymerase chain reaction (qPCR). Generalized linear mixed effects logistic models were applied to analyze factors associated with KSHV ever shedding, and among shedders, always versus intermittent shedding. Linear mixed effects models were applied to models of KSHV viral loads. Intraclass correlation coefficients (ICCs) were calculated to assess the contribution of household-level factors to variations in shedding probabilities. Hotspot analyses of geospatial feature clusters were calculated using Getis-Ord Gi* statistic and visualized using inverse distance weighted interpolation. Analyses included 340 KSHV seropositive individuals, aged 3 + years, with qPCR results from 89 households. Forty households had 1 + persons living with HIV (PLWH), while 49 had none. Among participants, 149(44%) were KSHV ever shedders. Of 140 who shed KSHV at two or more visits, 34(24%) were always shedders. Increasing number of KSHV seropositive household members was significantly associated with ever shedding [Odds ratio(OR) (95% Confidence Interval(95%CI)):1.14(1.03,1.26);p = 0.013]. Among KSHV shedders, a statistically significant age-related trend was identified with 10-19 years being more likely to be always shedders (type III test p = 0.039) and to have higher viral loads (type III test p = 0.027). In addition, higher viral loads were significantly associated with increasing number of household members [coefficient(95%CI):0.06(0.01,0.12);p = 0.042], increasing number of KSHV seropositive members [coefficient(95%CI):0.08(0.01,0.15);p = 0.021], and living in households with 1 + PLWH [coefficient(95%CI):0.51(0.04,0.98);p = 0.033]. Always shedders exhibited higher viral loads than intermittent shedders [coefficient(95%CI):1.62(1.19,2.05);p < 0.001], and viral loads increased with the number of visits where KSHV DNA was detected in saliva (type III test p < 0.001). Household-level factors attributed for 19% of the variability in KSHV shedding (ICC:0.191;p = 0.010). Geospatial analysis indicated overlapping hotspots of households with more KSHV seropositive individuals and KSHV shedders, distinct from areas where PLWH were clustered. KSHV oral shedding is influenced by multiple factors at the individual, household, and regional levels. To mitigate ongoing KSHV transmission a comprehensive understanding of factors contributing to oral KSHV reactivation and transmission within households is needed.

AidData’s Geospatial Global Chinese Development Finance Dataset

AidData’s Global Chinese Development Finance Dataset (Version 3.0) provides detailed information about more than 20,000 development projects across 165 low- and middle-income countries financed by 791 official sector Chinese donors and lenders from 2000 to 2021. In this study, we introduce a methodology for identifying the geospatial features of these projects. Our application of the methodology has resulted in the Geospatial Global Chinese Development Finance Dataset (Version 3.0), which captures the geospatial features of 9,405 projects across 148 low- and middle-income countries supported by Chinese grant and loan commitments worth more than USD 830 billion. The dataset provides details for 6,266 projects containing spatial definitions of roads, railways, power plants, transmission lines, buildings, and other precisely geocoded features. It identifies approximate and administrative-level locations for 3,139 additional projects. The methodology, dataset, and the code used to construct the dataset have been made publicly available to facilitate replication and future applications.

Optimizing Malaria Control: Granular and Cost-Effective Mosquito Habitat Index in Endemic Areas Through Satellite Imagery

Malaria, classified as a tropical disease under the Sustainable Development Goals (SDGs) indicator 3.3, remains a significant global health challenge. In this study, by taking advantage of multiple spectral composite indexes of multisource satellite imagery to capture various geospatial features relevant to the suitability of marsh mosquito habitat, we introduced the Mosquito Habitat Suitability Index (MHSI) to assess potential Anopheles mosquito breeding sites in terms of the vegetation density, water bodies, environment temperature, and humidity in any particular areas. The MHSI integrates the publicly accessible granular level of the normalized difference vegetation index, water index, land surface temperature, and moisture index from cost-effective low and medium-resolution optical satellite data. We focus on West Papua Province, Indonesia, known for diverse ecological conditions and varying malaria prevalence, as a case study area. From the built index, the risk zone map is then formed with the K-Means algorithm. One key finding is the elevated risk in Fakfak Regency, demanding particular attention, as its high-risk area represents 45% of its total. This research aids localized decision-making to combat malaria's unique challenges in West Papua Province which are relevant for implementation in other regions, contributing to SDG-aligned interventions for malaria eradication by 2030.

Latent Representation Learning for Geospatial Entities

Representation learning has been instrumental in the success of machine learning, offering compact and performant data representations for diverse downstream tasks. In the spatial domain, it has been pivotal in extracting latent patterns from various data types, including points, polylines, polygons, and networked structures. However, existing approaches often fall short of explicitly capturing both semantic and spatial information, relying on proxies and synthetic features. This paper presents GeoNN, a novel graph neural network-based model designed to learn spatially-aware embeddings for geospatial entities. GeoNN leverages edge features generated from geodesic functions, dynamically selecting relevant features based on relative locations. It introduces both transductive (GeoNN-T) and inductive (GeoNN-I) models, ensuring effective encoding of geospatial features and scalability with entity changes. Extensive experiments demonstrate GeoNN’s effectiveness in location-sensitive superpixel-based graphs and real-world points of interest, outperforming baselines across various evaluation measures.

Monitoring solutions for remote locations: A data gathering approach for remote nature-based solution sites

Nature-based solutions have gained popularity as an approach to tackling hydro-meteorological hazards (HMHs) in both urban and rural settings. Despite this popularity, challenges persist regarding the evidence base for their effectiveness and data scarcity at the feature or site scale. Flood modelling is a common approach to quantifying the effectiveness of NbS; however, the accuracy of these models heavily depends on the accuracy of the DEM, land cover, and hydraulic/hydrological data utilised. Remote and rural settings often face data scarcity due to the challenging nature of data collection, and insufficient funding for monitoring. Additionally, NbS features vary in size and scale, with many being small (<1 m in width), posing challenges for accurate representation in national LiDAR datasets. Technological advancements in remote sensing technologies, such as unmanned aerial vehicles, handheld LiDAR, and GPS-GNSS, offer opportunities to gather high-resolution, high-accuracy data in these challenging locations. This article proposes a methodological framework for collecting elevation data at remote NbS sites that can tackle areas affected by both sparse and dense vegetation cover. This approach proves valuable in both pre-NbS implementation, through facilitating NbS opportunity and environmental risk identification, and post-NbS implementation, through aiding in geo-spatial feature location, improving existing DEM data for flood modelling, and monitoring temporal changes.

An approach for traffic pattern recognition integration of ship AIS data and port geospatial features

ABSTRACT Recognition of ship traffic patterns can provide insights into the rules of navigation, maneuvering, and collision avoidance for ships at sea. This is essential for ensuring safe navigation at sea and improving navigational efficiency. With the popularization of the Automatic Identification System (AIS), numerous studies utilized ship trajectories to identify maritime traffic patterns. However, the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior, such as ship static and maritime geospatial features, resulting in insufficient precision in ship traffic pattern recognition. This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity (STPMTS), which considers ship static feature, dynamic feature, port geospatial feature, as well as semantic relationships between these features. First, A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements. Subsequently, to quantify the degree of similarity of ship trajectories, a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features. Furthermore, trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters. The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns. Finally, the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China, ranging from September to November 2016. Compared with other methods, the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features, dynamic features, and adherence to navigation rules. This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives.

Spatial heterogeneity of low flow hydrological alterations in response to climate and land use within the Upper Mississippi River basin

The Upper Mississippi River Basin (UMRB), located in the Midwestern U.S., is a nationally recognized region of concern for water quality and aquatic biological health. At 500,000 km2, geospatial characteristics and climate vary widely within the basin and thus, hydrological behavior and associated effects on freshwater ecosystems may also dramatically vary. So far, previous studies of long-term hydrological behavior in the UMRB have mostly considered changes for the basin rather than changes at finer watershed scales. This study systematically evaluated long term trends in indices of hydrological alteration (IHA) for 118 of 131 watersheds of the UMRB and compared them with 1) variabilities in geospatial features (land use, soil type, and topography), 2) land use changes, 3) climate, and 4) evapotranspiration trends using hierarchical clustering and correlation analysis. Compared to climate trends, land use and evapotranspiration were found to have stronger impacts on trends in low flow IHA indices in the watershed. The results demonstrate that while overall, low flow has been increasing in the basin, there is considerable variability in alterations in low flow across watersheds. Specifically, the results indicate that in forested or mixed-use watersheds, low flow has been decreasing while in agricultural and urban watersheds low flow is increasing. These results together with the literature on water quality in the UMRB, suggest both stressors, water quantity and water quality exist in the UMRB and vary by land use and evapotranspiration, implying that effective watershed management plans are local not regional and should be directed toward reducing the specific local stressors.

Геополитическая регионализация Балтики: содержание и историческая динамика

The article discusses a theoretical framework for investigating regionalisation and geopolitical regionalisation, employing the activity-geospatial approach. The main theoretical foci of this study are system-forming, or region-building, socio-geo-adaptation and geopolitical relations. The article examines various types of transboundary and transnational geopolitical regionalisation as manifestos of geopolitical relations. These types are categorised based on scale, functional area, historical and geographical characteristics, quality, legal status and geospatial features, placing particular emphasis on the Baltic region. An essential aspect of studying a region involves identifying and defining its spatial boundaries. Since determining the exact limits of the Baltic region remains problematic, this article examines various approaches to address this issue, highlighting their strengths and weaknesses, particularly in the context of geopolitical analysis. The concluding part of the article explores several centuries of the evolution of the Baltic Sea region, divided into historical geopolitical stages. It is highlighted that the geopolitical essence of the Baltic region was changing radically over time. Particular attention is paid to the current state of the Baltic regional geopolitical entity, which is classified as a conflict-ridden or confrontational geopolitical region in the ‘Eurasian arc of instability’ interpreted as a geopolitical macroregion.

Методичні підходи до аналізу рівня територіального розвитку в Україні

The article examines methodological approaches to the analysis of the level of territorial development in Ukraine. The main approaches to the analysis of the level of territorial development, which are contained in official documents and serving as bases for development of methodological recommendations, are allocated, in particular: 1) methods of preparing a socioeconomic analysis of community development; 2) development of profiles of communities; 3) methods of formation and implementation of forecast and program documents of socioeconomic development of the united territorial community; 4) methodology for taking into account the Sustainable Development Goals in the development strategies of territorial communities. The territory as an object of development is considered from various aspects: economic, socio-cultural, ecological, and political. The concept of «territory» is usually associated with spatial characteristics and is considered as a limited part of the land surface with a certain area, geographical location and other spatial features. However, in the case of interpreting the territory as an object of development, this concept should be considered as a separate, identified by geospatial features and administrative subordination territory with the appropriate structure, conditions, properties and characteristics. The main principles on which methodological approaches to the analysis of the level of territorial development should be based are allocated, i. e.: consistency; integration; focus on the needs of the local community; public participation; scientificity; validity and objectivity; sustainability; rational use of resources; monitoring and evaluation. It is emphasized that in Ukraine there is currently no single methodological approach to the analysis of the level of territorial development, and the existing methods turned out to be imperfect and require revision. In particular, only certain methodologies of analyzing the level of territorial development are in use, as well as certain methods of taking into account the Sustainable Development Goals in the development strategies of territorial communities, although this does not comply with the principles of consistency, sustainability, and integration. The necessity of developing a unified methodological approach to the analysis of the level of territorial development in Ukraine, which would take into account a comprehensive analysis of the current state of territorial communities and the compliance of their development with the Sustainable Development Goals, is substantiated.

Social media insights into spatio-temporal emotional responses to COVID-19 crisis

The Coronavirus pandemic has presented multifaceted challenges in urban emotional well-being and mental health management. Our study presents a spatio-temporal sentiment mining (STSM) framework to address these challenges, focusing on the space-time geography and environmental psychology. This framework analyzes the distribution and trends of 6 categories of public sentiments in Shanghai during the COVID-19 crisis, considering the potential urban spatial influencing factors. The research specifically draws on social media data temporally coinciding with the spread of COVID-19 and the pre-trained language model RoBERTa-wwm-ext to classify public sentiment, in order to characterize the distribution and trends of dominant urban sentiment under the influence of epidemic at different phases. The interactions between urban geospatial features and sentiments are further modelled and explained using LightGBM algorithm and SHapley Additive exPlanations (SHAP) technique. The experimental findings reveal the subtle yet dynamic impact of the urban environment on the long-term spatial variation and trends of public sentiment under the epidemic, with green spaces and socio-economic status emerging as significant factors. Regions with higher permanent population consumption demonstrated more positive sentiments, underscoring the significance of socio-economic factors in urban planning and public health policy. This research offers the most extensive analysis to date on the influence of urban characteristics on public sentiment during Shanghai's epidemic life cycle also lays the groundwork for applying the STSM framework in future crises beyond COVID-19.

Current State and Features of Tourism Development in Czechia

Tourism is a significant contributor to the social and economic growth of countries and regions. As such, developing tourism is a crucial element in enhancing a country's economic efficiency. In Europe, the Czech Republic stands as one of the most alluring and well-established countries. Thanks to its abundant natural, recreational, historical, and cultural resources, it has fostered a fiercely competitive tourism industry. The commonness of historical, natural, social, and economic developmental conditions between Czechia and Ukraine establishes similar prerequisites for tourism development. An analysis of Czech tourism development, including identification of the country's tourism traits and trends, enables the adoption of Czech tourism organization experience and practical application in Ukraine. The present article examines the current state of tourism development in Czechia. The analyses the development of the tourist infrastructure in the country. It examines the dynamics of the collective means of tourist accommodation in the Czech Republic. The analysis focuses on the dynamics of tourist arrivals to the Czech Republic, characterizing the structure of tourist flows. The geospatial features of tourist flows are considered, and the dynamics of the volume of income from foreign tourism are analysed. Tourism plays a significant role in the economics of the Czech Republic. The contribution of tourism to the country's GDP remains below its pre-COVID-19 levels. However, in 2022, the tourism industry of the country started to recuperate from the pandemic. As compared to 2021, the number of tourists escalated by 8 million in 2022. Majority of the tourists arrived in the Czech Republic for recreational and cultural purposes, as well as to meet their relatives and for business agendas. The Czech Republic received the majority of its foreign tourists from Germany, Slovakia, and Poland. The domestic tourist share in the country's overall tourist flow is 62,3 %, while the foreign tourist share is 37,7 %. The country is currently investing significantly in transport infrastructure by modernizing its railway network and expanding the capacity of its airports. The provision and standard of accommodation play a vital role in the appeal of tourist destinations, and the Czech Republic has seen a consistent rise in the quantity of lodging options. An appreciable increase in the number of shared lodging facilities was observed in the Czech Republic over the period of 2020–2022. South Bohemian, Hradec Královе and South Moravian regions boast the highest number of accommodation facilities, while Pardubice and Vysočina regions have the lowest. The city of Prague, along with South Bohemian and South Moravian regions, is home to the largest number of rooms in collective accommodation facilities. The Czech Republic boasts a thriving restaurant industry that has seen a steady increase in the number of establishments. The country's tourism is characterized by uneven geographic distribution. Notably, the largest number of tourist arrivals are recorded in Prague, South Moravian, Hradec Královе, and South Bohemian regions. According to the analysis, the regions adjacent to Prague also exhibited a significant appeal to tourists. This trend might be clarified by tourists who visit Prague tend to extend their routes to neighbouring regions, particularly in Central Bohemian, Liberec, and Hradec Královе regions. Conversely, regions situated far from the capital draw a substantial number of visitors. This could suggest the existence of exceptional tourism services that entice travellers. The Czech Republic has high tourism densities, with Prague having the highest observed density due to the influx of tourists into a relatively small area. Karlovy Vary, Liberec, and Hradec Královе regions also have high tourism densities. In 2022, foreign tourism generated revenues of 1 billion USD for the country. However, due to the coronavirus pandemic, foreign tourism revenues decreased by 62,5 % in 2020. The Czech Republic is known for its conventional health and wellness, cultural and educational tourism. Keywords: tourism, tourist arrivals, collective accommodation, tourists, Czechia.

Predicting the transmission trend of respiratory viruses in new regions via geospatial similarity learning

The outbreak and spread of COVID-19 remind us again of the devastating attack that human-to-human transmitted respiratory infectious diseases (H-HRIDs) bring to global economics and public health. Predicting the trend of H-HRIDs yields important suggestions for making response strategies. Existing methods predict the future trend of H-HRIDs based on the prophase epidemiological data. However, it is also crucial to predict the potential outbreak trend of H-HRIDs in regions without prophase epidemiological data based on those with epidemiological data available. This prediction can provide constructive pharmaceutical strategies and non-pharmaceutical interventions such as pre-allocating the limited preventions to regions with different potential trends, especially at the early stage of an H-HRID. In this paper, transform the problem of predicting the early-stage trend of an H-HRID in new regions without prophase epidemiological data into the problem of learning regions’ geospatial features via a Contrastive learning-based Hierarchical Graph Convolutional Neural Network (CHGCN). Specifically, CHGCN first generates training data from regions with epidemiological data available via a Relaxed Graph Edit Distance (RGED) algorithm. CHGCN then uses a constructive learning schema to learn region embeddings, where regions with similar geospatial features and H-HRID trends exhibit similar embeddings. Once trained, CHGCN searches for the region with epidemiological data available that has the highest embedding similarity to the given query region without prophase epidemiological data, so as to achieve early-stage H-HRID trend prediction for the query region. Experimental results on COVID-19 demonstrate that the proposed CHGCN achieves state-of-the-art performance in predicting the early-stage trend of H-HRIDs, compared with the baselines.

Education for out-of-school children: Unpacking driving factors of vulnerability via digital-learning footprint data in East Africa

Introduction &amp; BackgroundApproximately 617 million children and adolescents worldwide do not possess the foundational skills to live healthy and productive lives. Sub-Saharan Africa is profoundly affected, resulting in social and financial dependency and raising vulnerability to forced marriage, female genital mutilation, and mental health issues. Contextual factors are considered critical in this crisis, yet have received little attention due to a currently insurmountable deficit in traditional census and survey data. Digital footprint data offers a potential route to filling this information gap - particularly in education- and how a learner’s environment can impact digital learning and future well-being.&#x0D; To address the crisis, the ‘Global Learning’ XPRIZE competition challenged teams to develop software empowering marginalised out-of-school children to learn literacy and numeracy skills. Five finalist teams tested their technology with 2041 children using handheld tablets in 172 remote villages in Tanzania.&#x0D; Objectives &amp; ApproachOur study examined factors that can predict improvements in learning outcomes, building on the digital footprint data collected from the children participating in the device intervention in the form of app usage and locational and activity data. Additional geospatial features were engineered based on village coordinates, distance to local amenities, services and transport, variables serving as additional potential indicators of isolation and connectedness. These data were linked with child-level factors, including household composition and literacy levels.&#x0D; After comparative assessment of machine learning regression models, tree-based models (XGB, RF) were used to establish the optimal predictive performance for literacy and numeracy. Variable importance using SHAP was used to determine which specific contextual variables should be considered before deploying digital interventions to support education and well-being.&#x0D; Relevance to Digital FootprintsUtilising digital footprint data to quantify the influence of geospatial and contextual data in digital interventions can offer comprehensive insights to understand and address factors impacting learning outcomes in this context.&#x0D; ResultsPrior school attendance, home reading environments and high familial literacy were found to be predictors of higher learning outcomes after a technology-based learning intervention. Environments featuring an unemployed caregiver and few siblings were surprisingly consistent positive predictors, suggesting accompanying and focussed caregiver support as valuable for effective development via digital interventions. Proximity to police stations and health centres were revealed as key predictors, indicating the importance of social and physical connectedness in positive learning environments.&#x0D; Conclusions &amp; ImplicationsTargeted improvement of EdTech provision with out-of-school learners promises to help the development of learning outcomes in remote villages and reduce vulnerabilities to correlated social and health issues. However, positive outcomes can only be achieved when appropriate supporting environments surround interventions.

De‐peripheralization and rural land capitalization: An empirical study of a public–private partnership in Zhaoqing, China

AbstractClassical urban geography theories determine the distribution of population, production and economic activities on geospatial features. Under this assumption, peripheral regions are rarely prosperous. Depopulation, weak industrial and employment structure and inefficient and fragmented land use form a negative feedback loop. Within the context of economic revitalization of peripheral area, this paper introduces an innovative Chinese practice on de‐peripheralization on the basis of two months of empirical research on the ‘two‐stage land demutualization reform’ in Zhaoqing, a city located at the peripheries of the Pearl River Delta (PRD). The reform successfully bonds the local stakeholders, stimulates the economy and resolves the land value capturing issue in urbanization. Local farmers in Zhaoqing founded an investment company with agricultural fields demutualized into stock shares and collaborated with the local government in improving local infrastructures and promoting economic investment in the secondary/tertiary sector. An in‐depth profile of the public–private partnership mechanism is collected through semi‐structured interviews conducted with locals. The results of the policy evaluation show that the rural land capitalization and inclusive design of the partnership regime removed the institutional barriers to socio‐economic development and brought attraction to the underprivileged area. The proposed joint development and value sharing policy framework contribute to the literature of institutional renovation for economic de‐peripheralization, especially for practices in land readjustment and zoning, resident resettlement, local employment provision and value capturing.

Implementing a Digital Twin of an Underground Utility Tunnel for Geospatial Feature Extraction Using a Multimodal Image Sensor

The increasing interest in digital twin technology, the digitalization of worn-out social overhead capital (SOC), and disaster management services has augmented the usage of 3D spatial models and information to manage infrastructure. In this study, a digital twin of a subterranean utility tunnel was created, and spatial objects were identified using inbuilt image sensors. The novelty lies in the development of a unique algorithm that breaks down the structure of the utility tunnel into points, lines, and planes, identifying objects using a multimodal image sensor that incorporates light detection and ranging (LiDAR) technology. The three main conclusions of this study are the following: First, a digital twin of the utility tunnel was constructed using building information modeling integrated with a geographic information system (BIM-GIS). Second, a method for extracting spatial objects was defined. Third, image-sensor-based segmentation and a random sample consensus (RANSAC) were applied. In this process, the supplementary algorithm for extracting and updating 3D spatial objects was analyzed and improved. The developed algorithm was tested using point cloud data, showing easier object classification with more precise LiDAR data.

Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts.

Despite a recent global decrease in suicide rates, death by suicide has increased in the United States. It is therefore imperative to identify the risk factors associated with suicide attempts to combat this growing epidemic. In this study, we aim to identify potential risk factors of suicide attempt using geospatial features in an Artificial intelligence framework. We use iterative Random Forest, an explainable artificial intelligence method, to predict suicide attempts using data from the Million Veteran Program. This cohort incorporated 405,540 patients with 391,409 controls and 14,131 attempts. Our predictive model incorporates multiple climatic features at ZIP-code-level geospatial resolution. We additionally consider demographic features from the American Community Survey as well as the number of firearms and alcohol vendors per 10,000 people to assess the contributions of proximal environment, access to means, and restraint decrease to suicide attempts. In total 1,784 features were included in the predictive model. Our results show that geographic areas with higher concentrations of married males living with spouses are predictive of lower rates of suicide attempts, whereas geographic areas where males are more likely to live alone and to rent housing are predictive of higher rates of suicide attempts. We also identified climatic features that were associated with suicide attempt risk by age group. Additionally, we observed that firearms and alcohol vendors were associated with increased risk for suicide attempts irrespective of the age group examined, but that their effects were small in comparison to the top features. Taken together, our findings highlight the importance of social determinants and environmental factors in understanding suicide risk among veterans.

Feature selection for a continental-scale geospatial model of environmental sound levels.

Modeling environmental sound levels over continental scales is difficult due to the variety of geospatial environments. Moreover, current continental-scale models depend upon machine learning and therefore face additional challenges due to limited acoustic training data. In previous work, an ensemble of machine learning models was used to predict environmental sound levels in the contiguous United States using a training set composed of 51 geospatial layers (downselected from 120) and acoustic data from 496 geographic sites from Pedersen, Transtrum, Gee, Lympany, James, and Salton [JASA Express Lett. 1(12), 122401 (2021)]. In this paper, the downselection process, which is based on factors such as data quality and inter-feature correlations, is described in further detail. To investigate additional dimensionality reduction, four different feature selection methods are applied to the 51 layers. Leave-one-out median absolute deviation cross-validation errors suggest that the number of geospatial features can be reduced to 15 without significant degradation of the model's predictive error. However, ensemble predictions demonstrate that feature selection results are sensitive to variations in details of the problem formulation and, therefore, should elicit some skepticism. These results suggest that more sophisticated dimensionality reduction techniques are necessary for problems with limited training data and different training and testing distributions.

Landslide Hazard Assessment in Highway Areas of Guangxi Using Remote Sensing Data and a Pre-Trained XGBoost Model

This study presents a novel method for assessing landslide hazards along highways using remote sensing and machine learning. We extract geospatial features such as slope, aspect, and rainfall over Guangxi, China, and apply an extreme gradient boosting model pre-trained on contiguous United States datasets. The model produces susceptibility maps that indicate landslide probability at different scales. However, the lack of accurate data on historical landslides in Guangxi challenges the model evaluation and comparison between regions. To overcome this, we calibrate the model to fit the local conditions in Guangxi. The calibrated model agrees with the observed landslide locations, implying its capability to capture regional variations in landslide mechanisms. We apply the model at a 30 m resolution along the Heba Expressway and validate it against reports from July 2021 to March 2022. The model correctly predicts five of seven landslide events in this period with a reasonable alarm rate. This framework has the potential for large-scale landslide risk management by informing transportation planning and infrastructure maintenance decisions. More data on landslide timing and human disturbance events may improve the model’s accuracy across diverse geographical areas and terrains.