Geospatial Method Research Articles

Spatial and Statistics for Profiling Risk Factors of Diseases: A Case Study of Tuberculosis in Malaysia

Understanding concepts of a proper disease transmission risk is not a straightforward process. In the context of tuberculosis (TB) dynamics, the concepts require the exploration of two meticulous criteria to produce an accurate epidemic modelling of the risk areas of the disease. The criteria include interpreting the biological transmission of the disease and applying multidisciplinary approaches. Spatial statistics were used to evaluate the preferences of risk factors in Shah Alam, Malaysia. GIS-multicriteria decision making (MCDM) method and logistic regression method were specifically integrated to select the local risk factors and seven influential factors were ranked accordingly i.e. human mobility, high risk group, socio-economic status (SES), population, type of house, distance of factory and urbanisation. Each has relative risk rate that affects the cases and the combination of them will even impact more on the overall risk concentration of TB. Human–based factors are identified as dominant effects to the risk than biophysical factors, for example, a location of TB risk will be increased by four times if individuals are living together with people who have TB disease for a particular time period. This geospatial method is expected to predict a better factor prediction in identifying hotspot areas of the disease.

Mapping Informal Settlements Using Geospatial Method

As one of the largest city in Indonesia, Bandung faces informal settlement, or “Permukiman Kumuh” problems. Based on the criteria of informal settlements used in KOTAKU Program, Bandung contains 8,7 percent of slums within it. During its development, these informal settlements experiences phase based on the development of the city, which are infancy, consolidation and saturation. Each developmental phase needs differing solutions. This study formulates the spatial development phases from the informal settlements in Bandung through Geospatial methods to give new perspective in settling informal settlement problems.

Comparison of geospatial and ground-based methods for determining postharvest dispersed woody residues

Postharvest woody residues are measured to estimate billable waste, bioenergy potential, fuel loadings, and carbon budgets. In fall 2014, a waste and residue survey (WRS) established twenty-nine 0.4 ha plots in the dispersed residue stratum on two cutblocks on Vancouver Island, British Columbia, and measured woody residue “logs” ≥ 10 cm inside-bark diameter and ≥ 20 cm in length. A line-intersect sampling (LIS), in spring 2015, measured all woody debris ≥ 10 cm diameter outside bark (DOB) on 18 plots. High-resolution (2 cm) photography was acquired in summer 2015, orthophotomosaics were prepared and analyzed for residue “logs” ≥ 10 cm DOB in 29 plots using semi-automated “log” delineation (SLD) and manual heads-up “log” digitization (MLD). After adjustment for bark thickness, SLD values were still higher than WRS values, due to inclusion of non-log pieces, though MLD values were not. LIS values were not different from WRS values once adjusted for bark thickness, transect overlaps, and decayed or non-log pieces excluded. The LIS and preharvest forest cover species composition differed from the WRS. While the SLD geospatial method can census ≥ 10 cm diameter residues in entire cutblocks, it was biased. Field-based methods may be required to correct SLD bias and measure species composition to determine bark thickness and wood densities to calculate biomass from residue volumes.

Participatory sensing-based geospatial localization of distant objects for disaster preparedness in urban built environments

Abstract Although the benefit of participatory sensing for collecting local data over large areas has long been recognized, it has not been widely used for various applications such as disaster preparation due to a lack of geospatial localization capability with respect to a distant object. In such applications, objects of interest need to be robustly localized and documented for supporting data-driven decision-making in site inspection and resource mobilization. However, participatory sensing is inappropriate to localize a distant object due to the absence of ranging sensors in citizens' mobile devices; thus, the localization accuracy varies to a large extent. To address this issue, this study presents a novel geospatial localization method for distant objects based on participatory sensing. The proposed geospatial localization process consists of multiple computational modules—a geographic coordinate conversion, mean-shift clustering, deep learning-based object detection, magnetic declination adjustment, line of sight equation formulation, and the Moore-Penrose generalized inverse method—to improve the localization accuracy in participatory sensing environments. The experiments were conducted in Houston and College Station in Texas to evaluate the proposed method, and the experimental results demonstrated a reasonable localization accuracy, recording the distance errors of 1.5 m to 27.8 m when the distance from observers to the objects of interest were 17 m to 296 m. The proposed method is expected to contribute to rapid data collection over large urban areas, thereby facilitating disaster preparedness that needs to identify locations of distant objects at risk.

A geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes.

Prehistoric peoples chose farming locations based on environmental conditions, such as soil moisture, which plays a crucial role in crop production. Ancestral Pueblo communities of the central Mesa Verde region became increasingly reliant on maize agriculture for their subsistence needs by AD 900. Prehistoric agriculturalists (e.g., Ancestral Pueblo farmers) were dependent on having sufficient soil moisture for successful plant growth. To better understand the quality of farmland in terms of soil moisture, this study develops a static geospatial soil moisture model, the Soil Moisture Proxy Model, which uses soil and topographic variables to estimate soil moisture potential across a watershed. The model is applied to the semi-arid region of the Goodman watershed in the central Mesa Verde region of southwestern Colorado. We evaluate the model by comparing the Goodman watershed output to two other watersheds and to soil moisture sensor values. The simple framework can be used in other regions of the world, where water is also an important limiting factor for farming. The general outcome of this research is an improved understanding of potential farmland and human-environmental relationships across the local landscape.

A Lightweight Collaborative GIS Data Editing Approach to Support Urban Planning

Collaborative geospatial data editing is different from other collaborative editing systems, such as textual editing, owing to its geospatial nature. This paper presents a version-based lightweight collaborative geospatial editing method for urban planning. This method extracts editing data and generates a version for collaborative editing, which reduces the data size and thus allows for a high feedback speed. A replication mechanism is engaged to replicate a version for the client to freely edit, which ensures constraint-free editing in collaboration. Based on this method, realizing the fact that heterogeneous geospatial data and non-professional users are involved, a lightweight architecture, integrating web services, and component technologies, was proposed. This architecture provides a unified data access interface and powerful editing ability and ensures a high feedback speed and constraint-free editing. The result of the application of the proposed approach in a practical project demonstrates the usability of collaborative geospatial editing in urban planning. While this approach has been designed for urban planning, it can be modified for use in other domains.

Geospatial Disaggregation of Population Data in Supporting SDG Assessments: A Case Study from Deqing County, China

Quantitative assessments and dynamic monitoring of indicators based on fine-scale population data are necessary to support the implementation of the United Nations (UN) 2030 Agenda and to comprehensively achieve its 17 Sustainable Development Goals (SDGs). However, most population data are collected by administrative units, and it is difficult to reflect true distribution and uniformity in space. To solve this problem, based on fine building information, a geospatial disaggregation method of population data for supporting SDG assessments is presented in this paper. First, Deqing County in China, which was divided into residential areas and nonresidential areas according to the idea of dasymetric mapping, was selected as the study area. Then, the town administrative areas were taken as control units, building area and number of floors were used as weighting factors to establish the disaggregation model, and population data with a resolution of 30 m in Deqing County in 2016 were obtained. After analyzing the statistical population of 160 villages and the disaggregation results, we found that the global average accuracy was 87.08%. Finally, by using the disaggregation population data, indicators 3.8.1, 4.a.1, and 9.1.1 were selected to conduct an accessibility analysis and a buffer analysis in a quantitative assessment of the SDGs. The results showed that the SDG measurement and assessment results based on the disaggregated population data were more accurate and effective than the results obtained using the traditional method.

Analysing the spatial configuration of urban bus networks based on the geospatial network analysis method

In urban areas, the spatial configuration of urban elements is vital to the sustainable development of cities. Due to the inherent spatial and networked characteristics of UBNs, research on the spatial configuration of UBNs requires a comprehensive approach involving the integration of spatial and network analysis methods. We propose a methodological framework for geospatial network analysis that combines spatial and network analysis to analyse the spatial configuration of UBNs. This framework is based on the construction of an urban bus spatial network (UBSN) in which the network nodes are spatial division units of urban space with more realistic geographical significance. Then, the framework analyses the spatial configuration of the UBN from the identification of macroscopic statistical characteristics to reveal the explicit roles played by microscopic elements such as individual nodes and to further identify the mesoscopic structural organization. The importance of nodes has spatial heterogeneity and directional imbalance. The community detection results demonstrate the UBSN presents obvious spatial agglomeration features, and the overlapping nodes are mostly distributed in the suburbs. With a novel viewpoint and detailed description of the spatial configuration of UBNs, this study provides meaningful insights for policy makers and planners seeking to optimize traffic infrastructure planning.

A bird’s eye view of my village – Developing participatory geospatial methodology for local level land use planning in the Southern Highlands of Tanzania

Despite the large number of participatory mapping and participatory geographical information system (PGIS) applications developed since the 1990s, few studies have utilized participatory mapping in formal planning processes. Evidence is needed regarding their practical applicability in rural land use planning in the Global South and their effectiveness in decision-making in formal planning processes. In this paper, we present participatory mapping and planning methodology that we have co-developed for official village land use planning processes in Tanzania and assess the method’s influence on spatial data quality as well as deliberation and spatial understanding and learning among the participants. We describe 11 literature-based criteria for integrating participatory mapping into spatial planning processes and use them in our assessment. The assessment includes analysis of village land use plan (VLUP) maps and observations as well as interviews and group discussions with participants and facilitators of the planning process. We show that the participatory mapping method with georeferenced images is a powerful tool to capture local spatial knowledge from a wide range of stakeholders and increase the quality of and confidence in spatial planning. As a visual aid, the georeferenced image supports deliberation and detailed examination of the landscape, enhancing spatial understanding and learning about the village landscape. Apart from generating local spatial data, we show that the participatory geospatial method supports the decision-making capacity of participants, which is important for the effectiveness of the method in formal land use planning processes.

Research on the geospatial correction method of water extracting products considering the characteristics of time points

Research on the geospatial correction method of water extracting products considering the characteristics of time points

Spatial analysis and heritage conservation: Leveraging 3-D data and GIS for monitoring earthen architecture

Abstract This paper discusses new advances in heritage site monitoring using a geo-spatial method for assessing the state of preservation of earthen architecture overtime as a preventive conservation measure. The proposed method leverages a comprehensive (quantitative–qualitative) approach that gathers multi-temporal data including environmental information collected by means of environmental loggers, qualitative vulnerability assessment of mud-brick walls, and surface change detection information obtained by comparing terrestrial laser scanning point cloud capturing the decay of building's wall features over time. Producing a detailed spatial understanding of the conservation issues that affect mud-brick walls in large earthen sites, this method can be used by conservators to rapidly identify which buildings require immediate intervention and lay the basis for future evaluation of the conservation actions undertaken. To test the effectiveness of the proposed geospatial model in producing a comprehensive view of the environmental risk and pattern of decay that affect mudbrick structures, this paper presents analyses and results obtained in a six-year study at Catalhoyuk, Turkey. Our results corroborate the effectiveness of the proposed method and prove that it can be successfully employed to create preventive conservation measures at other earthen sites inside and outside the Near East.

Geospatial Method to Map Potential Risk of Stormwater Thermal Pollution

AbstractThermal pollution is a growing concern as communities strive to protect and improve the nation's waters. The purpose of this study was to develop and analyze a geospatial model that predicts the relative risk of contribution of thermal energy to surface runoff throughout an urban watershed. A geospatial‐based solution could serve as a screening method or planning element, prior to or instead of the development of a more complex simulation of mass‐load transport of thermal energy. The study's theoretical methodology integrated the thermal potential of land cover and thermal decay due to overland travel to inlets. The resulting thermal pollution potential (TPP) index value was then assigned to individual model grid cells for comparison. Analysis of results from application to a case study watershed in Blacksburg, Virginia, indicated the computational methodology is not very sensitive to changes in spatial resolution. The thermal decay constant used in the analysis was varied, and results indicated a low sensitivity to selection of this parameter. A comparison between methods of watershed delineation also indicated use of infrastructure‐corrected watershed delineation yields better results than derivation from automated techniques. The geospatial method presented may be used to reproduce a TPP risk map to prioritize thermal pollution mitigation efforts.

Migratory flyways in Far-East Asia and spatial-temporal relationship to HPAI H5N6 outbreaks in South Korea

Event Abstract Back to Event Migratory flyways in Far-East Asia and spatial-temporal relationship to HPAI H5N6 outbreaks in South Korea Seong-Keun Hong1*, Kwang-Nyeong Lee1, Hachung Yoon1, Dae-Sung Yoo1, Ilseob Lee1 and Han-Mo Son1 1 Animal and Plant Quarantine Agency (South Korea), Republic of Korea Since 2016, 365 cases of highly pathogenic avian influenza virus (HPAIv) outbreaks by the Asian-origin H5N6 lineage of HPAIv have reported in South Korea and resulted in serious socio-economical losses. In this study, we demonstrated that the risk of HPAIv H5N6 in domestic poultry is related to wild birds. We expanded previous geospatial risk analysis method to East Asia including South Korea and integrated the analysis method with migration data of satellite-tracked wild birds along the far-east Asian flyway. We caught wild migratory birds around Lake Khanka (southeast area) collaborate with Russian researchers and attached a location tracker. From April to November 2018, we studied movement of 21 wild birds which carrying location tracker: 4 mallards, 2 spot-billed ducks, and 15 great egrets. The great egrets showed highest tracking success rate. During the migration, they stopped at several stopover sites (Russia to Japan; 1 mallard, 1 spot-billed duck, 1 great egret), and stayed for several weeks at those sites. We also demonstrated that migratory pathway of wild birds is correlated with HPAI H5N6 outbreaks, poultry density and latitude in East Asian Flyways. Spatial and statistical analysis was performed in ArcGIS 10.4.1(ESRI) and R 3.5.3. In addition, satellite-based movement patterns of wild birds showed that wild birds followed the East Asian flyway along the coast to breeding areas in eastern Russia. Taken together, our results suggest that it is important to analyze the tracking records of the movement of wild birds in various ways for making inference on the relationship between the migration of wild birds and outbreaks of HPAIv. Acknowledgements For field assistance with wild bird studies, we acknowledge the corperation of dedicated staff at Russian institutions, including Igor Katin and Ivan Tinuov(NSCMB FEB RAS). Additional support for tracking system was also provided by Korea Ecology Environment Research Institute, South Korea, including Han-Soo Lee. Any use of product or firm names is for descriptive purposes and does not imply endorsement by the South Korea Government.

Clustering Geospatial Data for Multiple Reference Points

Data clustering plays a significant role in geospatial data management and analytics. In this light, we propose and study a novel geospatial data clustering method for multiple reference points. Given a set Q of geospatial data points, a candidate set O of reference points, and a threshold k, each data point q will be matched to its closest reference point o. The multi-reference clustering (MRC) method finds a subset A (A ⊆ O ∧ |A| ≤ k) reference points from O which define the minimum global travel distance (Σ P∀q∈Q,o∈A d(q, o)), hence the data are grouped into |A| clusters. We believe that the MRC method may benefit a lot of applications including geospatial data clustering, data classification, and data analytics in general. The MRC problem is challenging due to its high computation complexity, and there exist Σ i=1 k C |O| i = Σ i=1 k (|O|!/i!(|O|-i)! possibilities for subset A. Because the exact solution cannot be computed in real time, we develop a heuristic method to select subset A from O efficiently. The experimental results show that the accuracy of A is very close to the optimal solution. In addition, we also develop a set of optimization techniques to further enhance the efficiency. Finally, we conduct extensive experiments to study the efficiency and accuracy of the heuristic method.

HEALTH FACILITIES LOCATIONAL ANALYSIS IN MAKURDI LOCAL GOVERNMENT AREA OF BENUE STATE-NIGERIA USING GIS APPLICATION

The most important indices of defining general welfare and quality of life of people in the world are the physical and mental health of individuals. This study attempts to examine GIS solution in the spatial analysis of health facilities in Makurdi local government area of Benue State, through mapping of the existing health facilities located in the study area, their spatial distribution pattern, identify areas with marginal services and propose a model for the future development as well examine the availability and accessibility of the existing health facilities, e.g (road network, travel time, distance covered and queuing time). The” Nearest neighbour analysis” Geospatial statistical method was employed to determine the spatial distribution pattern of the existing health facilities in other to determine the area of limited service and accessibility to healthcare facilities. The findings indicated that the healthcare facilities are randomly distributed; Fiidi ward was discovered to have limited service as regards the healthcare facilities in the study area. In line with the findings, recommendations were made on the use of GIS to build a concept that can be followed and replicated in divergent social-economic and political contexts. Conclusively, the study had shown that GIS is a vital tool in healthcare facility analysis and also in live application issues and thus should be encouraged in Nigeria for national economic transformation and sustainable development.
 
 keywords: Health, GIS, Mapping, Location

MsRi-CCF: Multi-Scale and Rotation-Insensitive Convolutional Channel Features for Geospatial Object Detection

Geospatial object detection is a fundamental but challenging problem in the remote sensing community. Although deep learning has shown its power in extracting discriminative features, there is still room for improvement in its detection performance, particularly for objects with large ranges of variations in scale and direction. To this end, a novel approach, entitled multi-scale and rotation-insensitive convolutional channel features (MsRi-CCF), is proposed for geospatial object detection by integrating robust low-level feature generation, classifier generation with outlier removal, and detection with a power law. The low-level feature generation step consists of rotation-insensitive and multi-scale convolutional channel features, which were obtained by learning a regularized convolutional neural network (CNN) and integrating multi-scaled convolutional feature maps, followed by the fine-tuning of high-level connections in the CNN, respectively. Then, these generated features were fed into AdaBoost (chosen due to its lower computation and storage costs) with outlier removal to construct an object detection framework that facilitates robust classifier training. In the test phase, we adopted a log-space sampling approach instead of fine-scale sampling by using the fast feature pyramid strategy based on a computable power law. Extensive experimental results demonstrate that compared with several state-of-the-art baselines, the proposed MsRi-CCF approach yields better detection results, with 90.19% precision with the satellite dataset and 81.44% average precision with the NWPU VHR-10 datasets. Importantly, MsRi-CCF incurs no additional computational cost, which is only 0.92 s and 0.7 s per test image on the two datasets. Furthermore, we determined that most previous methods fail to gain an acceptable detection performance, particularly when they face several obstacles, such as deformations in objects (e.g., rotation, illumination, and scaling). Yet, these factors are effectively addressed by MsRi-CCF, yielding a robust geospatial object detection method.

Mapping landscape-level hydrological connectivity of headwater wetlands to downstream waters: A geospatial modeling approach - Part 1

Headwater wetlands affect ecosystem integrity of downstream waters; however, many wetlands – particularly geographically isolated wetlands (GIWs) – continue to be at risk. A significant portion of US federal policy is based on the jurisdictional status of wetlands, which is partly determined by the relationship between wetlands and downstream waters, including the cumulative impact of wetlands on those waters. We present a novel multi-phase geospatial modeling method to help elucidate hydrological relationship between GIWs and downstream waters at the landscape scale. The presented approach in this study used inundation maps derived from time series remotely sensed data between 1985 and 2010, weather and hydrological records, and ancillary geospatial data including information from the US Fish and Wildlife Service National Wetlands Inventory (NWI). The study site was a headwater catchment (292 km2) of the Choptank River Basin, located in the Mid-Atlantic region of USA, which contained a large number of Delmarva bays. The results showed inundation extent within GIWs varied, in aggregate, in response to weather variability (r = 0.58; p-value = 0.05), and was well correlated with streamflow (r = 0.81; p-value < 0.01) and base flow (r = 0.57; p-value < 0.1) conditions. The relationship between inundation patterns and stream discharge also varied with NWI hydrologic modifiers. The GIWs with water regime characterized by longer durations of flooding exhibited stronger correlations with stream discharge, but those GIWs with shorter durations of flooding were less correlated with stream discharge. This analysis suggests the mutual reliance (i.e., connection) of wetlands and streams on groundwater. GIWs appeared to function in aggregate, and it is likely that the combined effect of these wetlands significantly influenced the functioning of downstream waters.

DEFINING GEOSPATIAL DATA FUSION METHODS BASED ON TOPOLOGICAL RELATIONSHIPS

Abstract. Currently, geospatial datasets are produced in various models and formats in accordance with the spatial scale of the real world such as ground/ surface/underground or indoor/outdoor. The location-based services application also uses the optimal data model and format for each purpose. Therefore, there are various geospatial dataset for representing features of the same space. Various geospatial data on same object cause problems with the financial problems and the suitability of the data. In the paper, we reviewed how to integrate existing geospatial data to utilize geospatial data constructed in different models and formats. There are four main ways to fuse existing geospatial information. The existing geospatial data fusion methods consist of a method through geometry data conversion, a method through the aspect of visualization, a method based on attribute data, and a method using topological relationships. Based on this review, we defined a geospatial data fusion method on topological relationships, which is a method considering topological relationship between geospatial objects. In this method, the topological relationship of objects uses the basic concept of IndoorGML.

Revisiting Hit-and-Run Crashes: A Geo-Spatial Modeling Method

Hit-and-run crashes often delay emergency response and may result in increasing/secondary harms/damages to the victims in the crash. This study revisited hit-and-run crashes using a geo-spatial modeling approach, specifically, Geographically Weighted Regression (GWR), to explore geo-referenced crash data. The data cover motor vehicle crashes ( N = 138,529) in Southeast Michigan including 20,813 hit-and-run crashes in 2015. This study presented the results from both traditional regression and GWR models. GWR model results can be mapped in space, and the maps offer visual insights about the spatially varying correlates of hit-and-run crashes that are not available from previous studies. Results from traditional binary logit model are generally consistent with findings in previous studies. For example, hit-and-run is more likely to occur on weekends or during nighttime (especially without street lights on). Driving under impairment (DUI) seems to increase the likelihood of hit-and-run. GWR models also uncovered spatially varying correlates of hit-and-run. For example, DUI crashes in the northwest of the Detroit metropolitan area are associated with an even greater hit-and-run likelihood than those in other parts in this area. In addition, the local socio-economic factors are included in the analysis. Results show that hit-and-run is more likely to occur in census tracts with a higher unemployment rate, a lower household income, a smaller portion of college-educated population, and a greater population density. The study demonstrates a way of making sense of geo-referenced traffic safety data. The geo-spatial modeling method is useful for prioritizing specific geographic regions/corridors for safety improvement countermeasures, and outperforms traditional modeling techniques.

Integrasi Kaedah Geospatial dan Pemodelan Hidrodinamik untuk Mengkaji Impak Kenaikan Aras Laut Terhadap Kawasan Pantai

Integrasi Kaedah Geospatial dan Pemodelan Hidrodinamik untuk Mengkaji Impak Kenaikan Aras Laut Terhadap Kawasan Pantai