Spatial Analysis Approach Research Articles

Spatial Map of Geohelminths Infection in Agricultural Communities and its Contamination in Soil of Jatian Village, Jember Regency

Introduction: Geohelminth infections are a neglected global health problem. Data from the World Health Organization (WHO) in 2020 show that more than 24% of the world population is infected by geohelminths. The condition of the agricultural soil, which tends to be loose and moist, supports the development of the infective form of the geohelminth. This makes agricultural communities susceptible to geohelminth infections. This study aimed to determine a spatial map of geohelminth infection in agricultural communities and its contamination in the soil of Jatian Village, Jember Regency. Methods: This study used an observational analytical design and a spatial analysis approach. Sampling was performed using a random sampling method that included 43 samples. Data were obtained by examining soil and stool samples and the coordinates of the sampling locations. Data were analyzed using spatial analysis. Results and Discussion: The results of this study indicated that the prevalence of geohelminth infection in agricultural communities was 23.3%, and its contamination in the soil was 6.98%. Hookworms caused infections among respondents, and contamination in the ground was caused by Hookworms and Strongyloides stercoralis. Clustering analysis results showed that geohelminth infection formed two secondary clusters. Spatial autocorrelation and buffer analysis showed clustering of geohelminth infections within the buffer range (<100 m), indicating that the infection spreads more easily within the cluster. Conclusion: The spatial map showed the distribution pattern of clustered geohelminth infection cases and their contamination in soil within proximity, thereby increasing the risk of geohelminth transmission.

Counter-Mapping Maroon Cartographies

Formal spatial modeling and analytical approaches to maroon settlement, fugitivity, and warfare in the colonial-era Caribbean have tended to mine historical cartographic sources instrumentally to analyze the distributions and simulate processes driving marronage in St. Croix (Dunnavant 2021b; Ejstrud 2008; Norton and Espenshade, 2007). Through close-in analysis, we compare two Danish maps of St. Croix produced in 1750 and 1799 in relation to modern cartographic sources, to explore how cartographic forms and cartesian conventions (attempt to) elide blind spots in the colonial gaze. By modeling possible subject-oriented maroon movement on georeferenced colonial maps and contemporary LiDAR, we demonstrate how GIS can recover anti-colonial agency. Additionally, the practice of georeferencing itself is a critical site of analysis, revealing distortions suggestive of social and environmental conditions that limited colonial cartographers’ ability to map certain wilderness and contested landscapes that lay outside of their control.

Assessing nitrogen sources in Lake Erhai: A spatially explicit modelling approach

Due to the vulnerable ecosystem environments, plateau lakes in the southwest of China face high eutrophication risks. Excessive nitrogen input is an important driver of eutrophication in plateau lakes. However, it is lacked of accurate analysis of spatial and temporal distribution characteristics of nitrogen sources for plateau lakes, which restricts local water environment management. Taking a typical plateau lake, Erhai Lake, as an example, this study applied the MARINA (Model to Assess River Inputs of Nutrients to seAs)-Lakes model to 26 sub-basins of Erhai Lake to analyze the temporal (1998–2016) characteristics of nitrogen inputs and identify the main pollution sources in different sub-basins. The results showed that the most obvious decreasing trends in river export of nitrogen occurred in 2016. It decreased from 2.9 in 2010–2.3 Mg in 2016, which is associated with the series of water quality protection measures implemented. By distinguish nitrogen forms, we found that dissolved inorganic nitrogen was mainly from diffuse source (synthetic fertilizer) whereas point source (directly discharge of animal manure) was the dominant source for dissolved organic nitrogen. The highest river export of nitrogen was from Zhonghe sub-basin in the western areas with high population density and intensive agricultural system, and the lowest was from Mijuhe sub-basin in the northern areas. Tourism was the dominant source of river export of nitrogen in several sub-basins, including Zhonghe, Meitao, Haidongqin, Boluo, and Jinli. Our study developed an explicit spatial analysis approach to quantify river export of nitrogen by sources at sub-basin scale. Based on the primary sources of nitrogen within each sub-basin, we could divide Erhai Lake basin into different management zones. These results can not only help decision-makers formulate accurate and effective lake protection policies for Erhai Lake, but also provide new insights for other plateau lakes.

A Framework for Assessing Nature-Based Urban Stormwater Management Solutions: A Preliminary Spatial Analysis Approach Applied to Southeast Serbia

The development of the stormwater management strategies, e.g., low-impact development (LID), water-sensitive urban design (WSUD), and sustainable drainage solutions (SUDS), was initiated in the mid-1980s as a set of engineering approaches and technologies to reduce the harmful effects of stormwater. Over time, all of them evolved in the holistic, multidisciplinary approaches and, today, they are increasingly viewed and implemented under the umbrella term “Nature-based Solutions” (NbS). The technical elements and measures of these NbS represent various technical solutions, implemented i.a., according to the suitability of the site to achieve their maximum efficiency. Currently, there are no standards or procedures for the application of NbS technologies in Serbia. To overpass this and encourage implementation, we carried out preliminary assessment of NbS elements suitability for application in eight urban settlements in the Region of Southern and Eastern Serbia. The assessment is based on publicly available data and performed according to the existing recommendations in the field of spatial planning and rainwater management for WSUD. The analyses were conducted by GIS tools that involved spatial analyses of various terrain characteristics and provided an insight into the criteria, i.e., constraints that are key to the placement of various technical elements, including bioretention, rain garden, and permeable pavement. Research findings point out that creation of the thematic maps with area suitability ratings for individual NbS stormwater elements might represent a good starting point for further investigation, planning, and design. The proposed framework for preliminary assessment is potentially useful for the countries and regions without regulations in the field of NbS for stormwater management.

Analysis of Spatiotemporal Patterns of Undernutrition among Children below Five Years of Age in Uganda

Background: This study aimed at examining the prevalence and variations in undernutrition among children below five years of age in Uganda while considering the influence of space and time factors. Various studies conducted in Uganda analyzed the undernutrition phenomenon among children below five years of age with a focus on the risk factors and spatial variations; however, no study has ever integrated the elements of time in examining the problem of undernutrition in Uganda. The approach of spatial and spatiotemporal analysis is essential in identifying cluster patterns, hotspots, trends, and emerging hotspots, which is crucial in making timely and location-specific interventions. Methods: Data from the six Uganda Demographic and Health Surveys spanning from 1990 to 2016 were used, with the main outcome variable being undernutrition among children below five years of age. A Composite Index of Anthropometric Failure was derived from the three undernutrition outcomes and subsequently used as a proxy of undernutrition in this study. All data that were relevant to this study were retrieved from the survey datasets and combined with the 2014 shape files of Uganda to enable spatial and spatiotemporal analysis. Spatial maps with the spatial distribution of the prevalence of undernutrition, both in space and time, were generated using ArcGIS Pro version 2.8. Moran’s I, an index of spatial autocorrelation, was used to test the hypothesis of no spatial autocorrelation, while the Getis–Ord (Gi*) statistic was used to examine hot and cold spot areas. Furthermore, space-time cubes were generated to establish the trend in undernutrition as well as to mirror its variations over time and across the country. Moreover, emerging hot spot analysis was done to help in identifying the patterns of undernutrition over time. Results: The national prevalence of undernutrition among children below five years of age was 31.96 percent, with significant spatial variations both in space across Uganda and in the time since 1989. The index of spatial autocorrelation (Moran’s I) confirmed spatial clustered patterns as opposed to random distributions of undernutrition prevalence. Four hot spot areas, namely, the Karamoja, the Sebei, the West Nile, and the Toro regions, were significantly evident. Most of the central parts of Uganda were identified as cold spot clusters, while most of Western Uganda, the Acholi, and the Lango regions had no statistically significant spatial patterns by the year 2016. The spatio-temporal analysis identified the Karamoja and Sebei regions as clusters of persistent, consecutive, and intensifying hot spots, West Nile region was identified as a sporadic hotspot area, while the Toro region was identified with both sporadic and emerging hotspots. In conclusions, undernutrition is a silent pandemic that calls for immediate and stringent measures. At 31.96 percent, the prevalence is still very high and unpleasant. To reduce the prevalence of undernutrition and to achieve SDG goal 2, policymakers, as well as implementers, should consider the spatial effects and spatial and spatiotemporal variations across the country and prioritize interventions to hot spot areas. This would ensure efficient, timely, and region-specific interventions.

Large dam candidate region identification from multi-source remote sensing images via a random forest and spatial analysis approach

ABSTRACTThe extraction of large dam candidate regions is critical for broad-scale efforts to rapidly detect large-area dams. The framework proposed in this paper attempts to combine random forest classification models and spatial analysis methods with large dam candidate area extraction methods for large-scale areas. First, we studied the combination of optical, microwave, texture, and topographic features of the dam and constructed a multisource remote sensing and topographic feature vector of the dam. Secondly, we constructed random forest classifiers in different study areas and evaluate their performance. Then we explored the geographic characteristics of the dams and their relationships with other features. Finally, we introduced the spatial analysis method to constrain the large dam candidate area. The proposed framework was tested in a total area of 968,533 km2 in five countries and achieved promising results, which constrained the candidate area to less than 1.06% of the total area. We calculated the completeness rate of large dams using the multi-source dam datasets. The framework achieved a completeness rate of more than 97.62%. Our results show that the entire framework is reliable for automated and fast large dam candidate area acquisition based on data from open remote sensing products.

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures.

Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular patterns by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering the spatial contribution. As such, none of the existing approaches has fully accounted for the simultaneous heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular organizations. Functional principal component analysis (FPCA) is applied to estimate FPC scores which are then served as predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, potentially adjusting for other confounders. Using a non-small cell lung cancer dataset (n = 153) as a case study, we found that the spatial heterogeneity in the TME cellular composition of CD14+ cells, CD19+ B cells, CD4+ and CD8+ T cells, and CK+ tumor cells, had a significant non-zero effect on the overall survival (p = 0.027). Furthermore, using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset (n = 33), our proposed method identified a significant impact of cellular interactions between tumor and immune cells on the overall survival (p = 0.046). In simulation studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.

Spatial linear discriminant analysis approaches for remote-sensing classification

Linear Discriminant Analysis (LDA) is a popular and simple classification tool that often outperforms more sophisticated modern machine learning techniques in remote sensing. We introduce a novel LDA method that uses spatial autocorrelation of all pixels of an object to be classified but also of other objects of the training set that are spatially close to improve classification performance. To simplify spatial modelling and model fitting, the methodology is applied to the transformed feature vectors. We term this method conditional spatial LDA. Much alike universal Kriging in geostatistical interpolation, the combined use of feature data and conditioning on labelled training data in conditional spatial LDA was best able to exploit the available geospatial data. The method is illustrated on a crop classification case study from the Aconcagua agricultural region in central Chile.

Coordinated economic, energy and environment development across China from 2001 to 2020

ABSTRACT With the development of China’s economy, the contradiction between energy, economy and ecological environment is becoming more and more prominent. However, the spatial and temporal evolution of the contradiction system of energy, economy and environment all over China is rarely focused on. This study assesses the provincial coordinated degree of the economy-energy-environment system over the period of 2001–2020 and analyzes their spatial and temporal evolution using principal component analysis (PCA), a coordination degree model, non-parametric Kernel Density Estimation (KDE), and spatial correlation analysis approaches. Results show that provincial coordination degrees from 2001 to 2020 vary between ‘barely balanced’ and ‘superiorly balanced’ with an overall improvement. However, the gap between high and low regional coordination has widen, while middle-level provinces gradually increase and are distributed more evenly. Limited spatial correlation of provincial coordination exists and the clustering level slightly increases over 2001–2020. Additionally, some less-developed regions, which are still staying at the ‘intermediately balanced’ state in 2020, call for attention.

Analisis Sebaran Konversi Lahan Pertanian di Kecamatan Muara Bangkahulu Kota Bengkulu

This study aims to determine the pattern of land use change and the pattern of distribution of land conversion that occurred in Muara Bangkahulu District in the period 2010-2022. This study uses a quantitative method with a spatial analysis approach that utilizes Geographic Information System technology and surveys to conduct ground checks in the field on the interpretation of land conversion results from satellite imagery data. The analysis used is nearest neighbor analysis and descriptive analysis. The results showed that there were only two patterns of land change that occurred in Muara Bangkahulu District, namely oil palm plantation areas turning into residential areas and agricultural areas turning into residential areas. The high rate of change of land use into residential areas is due to the subsidized housing program in Muara Bangkahulu District. The pattern of distribution of land conversion that occurs can be seen through the pattern of distribution of settlements in Muara Bangkahulu District which is included in the random category with a value of T = 1.064934 meaning that land conversion occurs in each village and the change tends to be permanent because it has changed its function to become built-up land or settlements.

Geographic Accessibility to Primary Healthcare: Study Case Dengue Fever in Purwosari Sub-District, Gunungkidul Regency, Yogyakarta, Indonesia

Distribution and spatial analysis of public healthcare in Indonesia are rarely discussed. Most of the research tell about quantity and quality which do not mention the spatial aspect. This research purposes to know the relationship between health facilities selection and geographic conditions by combining medical facilites data, number of doctor and spatial analysis approach. The selected study areas are referral health facilities chosen by DHF (Dengue Hemorrhagic Fever) patients in Purwosari District, Gunungkidul Regency. Information was collected by interviews and field measurements (altitude and location plotting) and also secondary data collection (hospital accreditation data, number of doctors, and number of beds). GPS, Altimeter, ArcGIS, and SPSS used for data collection and analysis. The spatial analysis method in this research were kernel density, buffer, ruler, and altitude of each facility. Meanwhile, the medical analysis approach used Pearsons' correlation and multinomial logistic regression in SPSS. The results of the spatial analysis show that the DHF patients in Purwosari dominantly choose health facilities in Bantul Regency because of having closer distance, relatively short travel time, lower elevation, and many health facilities centers. At the same time, Puskesmas Purwosari (Sub-district level Health center) was only chosen as an early reference and a place for treating mild dengue patients. In addition, distance, duration of travel time, and the number of doctors had a strong correlation based on the Pearson's correlation. These three variables have the greatest linear relationship with the response variable. Meanwhile, some variables; elevation, number of doctors, accreditation, and number of beds were considered by patients in choosing a hospital based on multinomial logistic regression analysis.

Administrative Unit Proliferation Through Spatial Interaction Approach: Case Study of Lembang City Region

Administrative unit proliferation is a decision that needs to be taken based on several considerations based on research. The proposed Lembang City region is an area that will develop into a city in the next few years. For this reason, this research attempted to evaluate the proposed area’s ability to become an administrative city using various spatial aspects. The analytical approaches used in this study were: spatial interaction analysis, which helps to determine the regional economic center; thematic overview analysis, implementing village SDGs through a spatial analysis approach; and SWOT analysis to identify Lembang City region’s strengths, weaknesses, opportunities, and threats in qualitative terms. The study results show that the proposed Lembang City area has excellent potential as a city region. Through a spatial analysis approach supported by geographic information system tools, this research was conducted as a consideration in deciding to expand Lembang Regency may provide significant assistance to the government in its work related to this decision.

Sustainable cereal production: A spatial analytical approach using the Ghana living standards survey

Ghana as one of the countries south of the Sahara, depends solely on cereals as a major staple food. Ironically, Ghana's economy depends on large importation from the global north, particularly Asia, due to systemic production deficits. The probability of farming households producing enough cereals and the constraints to meeting domestic supply remains imperative. Therefore, the current research focussed on the Ghana Living Standard Survey seventh round (GLSS7) involving 15,045 cereal farmers nationwide. By estimating the probability of farming households producing cereals and the factors that constrain cereal production. Using random-effects regression models, the empirics show that farming households are expected to produce 5.87 tonnes of cereals annually. Specifically, farming households headed by males are expected to produce 6.01 tonnes of cereal crops in a year, 0.14 tonnes more than female-headed households. Non-poor households are expected to produce 6.82 tonnes of cereals in a year compared with an expected production of 6.29 tonnes by poor households. Cereal production is constrained by wealth status, gender, and age of household heads. Our findings attempt to inform and shape policy towards sustained cereal production in Ghana, and by implication countries in sub-Saharan Africa (SSA). The Ministry of Food and Agriculture (MoFA) in Ghana, should bring on board a structural policy that will address constraints related to gender, wealth, and age of household heads to enhance sustainable cereal production.

Идеи Вернадского в области биогеохиии и их развитие в ГЕОХИ РАН (краткий очерк)

The article analyses evolution of Vernadsky’s ideas in the field of biogeochemistry, and implementation of these ideas through the Biogeochemical laboratory, and, later – in the laboratory’s successor GEOKHI RAS. The analysis demonstrates high value of Vernadsky’s ideas for basic researches in geochemical ecology and perspective of spatial analysis approach for identification, prevention, and elimination of endemic diseases of geochemical nature.

Does Geographical Heterogeneity Influence Urban Quality of Life? A Case of a Densely Populated Indian City

Rapid population expansion and urbanization have created enormous difficulties in maintaining the urban quality of life (UQoL) in Indian cities. Quantifying the UQoL is a complex task, due to its multidimensionality. This study empirically attempts to measure the degree of UQoL and tries to understand the geographical pattern, association, and heterogeneity of UQoL in the densely populated city of Siliguri in India. Spatial heterogeneity, in this context, is the diversity in how well people can live in various geographic units, such as neighborhoods or wards, within a city. This article evaluates the heterogeneity in UQoL using thirty-one indicators composed of seven criteria. Principal component analysis and average aggregation techniques were employed to construct the dimensional and synthetic UQoL indexes, which mapped the degree of spatial inequality among forty-seven wards. The findings revealed that Siliguri’s overall UQoL is alarmingly poor, with apparent geographic discrepancies. To further examine the spatial patterns and clusters of UQoL, an exploratory spatial data analysis approach was applied. The spatial autocorrelation using Moran’s I demonstrates a substantial clustering pattern of UQoL and other domains within the city’s neighboring units. The UQoL assessment provides evidence to assist in spatial planning and the development of sustainable urban policies.

A Context-based Approach for Neighbourhood Life Circle Delineation and Internal Spatial Utilization Analysis Based on GIS and GPS Tracking Data

A Context-based Approach for Neighbourhood Life Circle Delineation and Internal Spatial Utilization Analysis Based on GIS and GPS Tracking Data

Occurrence, Source, and Mobilization of Iron, Manganese, and Arsenic Pollution in Shallow Aquifer

This investigation is aimed at exploring the mobilization and sources of Fe, Mn, and As in aquifers through geochemical characterization, spatial analysis, and statistical approaches. Analytical results showed that the average concentrations of Fe (10.17 mg/L) and Mn (3.6 mg/L) crossed the guideline value but not As in all samples. In this study, the elevated levels of dissolved organic carbon, salinity, total hardness, and log10 pCO2; the lower value of dissolved oxygen; and some oxidizing groups of samples influence the reductive environment of Fe and Mn weathering. The investigation illustrated that the source of both metals on the upper platform is consolidated silty soil and aquifer sediment, whereas, in the river valley, they originate from Fe and Mn-bearing nodules in the rich clay layer and tidal mud. In addition, the frequent fluctuation of river flow, excess water mining, and an unbalanced recharge/discharge ratio are the causes of higher concentrations of both metals. The correlation matrix and factor analysis of Fe and Mn with redox-sensitive water variables confirmed that a favourable reductive situation fosters the Fe and Mn release process. Studies have also shown the significant possibility of forming a metal complex with organic and inorganic substances. The higher dissolution of Fe and Mn minerals causes As to be adsorbed on them, leading to less release of As into groundwater. This investigation provides a basis for the rational use of groundwater and the defense of public health in zones with high Fe and Mn contents.

Finding space for offshore wind to support net zero: A methodology to assess spatial constraints and future scenarios, illustrated by a UK case study

Government and commercial forecasts indicate global ambitions for 2000 GW of installed offshore wind (OW) by 2050 to meet the targets of the Paris Agreement. Set against a current global installed capacity of 56 GW offshore wind, which has taken over 30 years to achieve, the challenge of installing 35 times that capacity in the coming 30 years is considerable. Aside from the issue of the necessary pace of OW installation, another challenge is scale: Where in the ocean could and should this new infrastructure be placed? This paper presents a spatial analysis approach for assessing the location of future offshore wind that collates and integrates, metocean, geoscience, ecological and anthropogenic features and intersects with engineering requirements. A new contribution to the field is made through calibration in relation to current ocean ‘crowdedness’, which leads to a suitability ranking of new sea regions. A case study is presented to illustrate the workflow and methodology of this approach based on the United Kingdom (UK)-Economic Exclusive Zone (EEZ). The UK has been selected as an exemplar due to its well-developed offshore wind sector, having the greatest installed capacity globally until the close of 2021, and as a region with ambitious and legislated offshore wind targets to meet net zero. The modelling and analysis quantify the need to eliminate the water depth barrier through floating OW technology, open up new sea regions and the associated port and grid infrastructure, as well as to assess the potential impact of increased utilisation of ocean space for OW.

Urban void as an urban catalyst bridging the gap between the community

Due to the increase in population, there are fewer open spaces creating urban voids in cities nowadays. However, the opportunities made available to people by urban voids adequately indicate the necessity and significance of urban open spaces. The lack of proper planning and a good perception of the place led to the formation of lost spaces. These lost spaces are found everywhere and are treated as waste spaces. By reclaiming these urban voids, we can change them into public gathering spaces, pocket parks, or any user-friendly space admired by the users. The objective of the study is to determine why these waste spaces are called urban voids and are not utilized to their full potential. The identified area for the case study is near Railway Colony in Multan. The selected area is L-shaped and connected with the base of a flyover, which creates a huge void. The spatial methodology, which is based on identifying the physical conditions and surveys, which depend on user ratings, was the foundation of the research methodology. Users assigned ratings for the space performance, which was determined by the spatial analysis approach and differed in terms of space structure and attributes. A total of 280 participants (118 females and 162 men) rated the technical, functional, and aesthetic performance of the spaces they occupy using a Likert attitude scale, with 70 individuals from each of the identified connected urban voids under the flyover. The results showed that space performance affects user satisfaction and the space usage level after design interventions.

Geospatial Analysis of Malnutrition among Under-Five Children: A Scoping Review

AbstractNutritional status is one of the important factors that indicate children's proper development and growth. The geospatial analytic approach is useful in describing and analyzing the characteristics, depth, and coverage of the malnutrition burden among under-five children. This current scoping review was performed to systematically map the spatial analytical techniques and approaches applied in nutrition among under-five children. An organized online database search was conducted to identify articles published between 1995 and 2021 on under-five nutrition and spatial statistic in PubMed, Science Direct, Scopus, and Web of Science. A total of 80 distinct articles were identified, of which 34 articles were used for the final review. A spatial statistical correlation was mainly used (n = 15), followed by Bayesian spatial modelling (n = 7), Global Moran's technique (n = 9), and Getis-Ord (n = 3). Nine studies in India concerning spatial analysis and undernutrition were conducted based on a national-level demographic health survey. There is a need for future spatial studies related to nutrition and under-five children at the sub-national level in India.