Spatial Analysis Approach Research Articles

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.

Spatial modeling and analysis approach for ship-encounters dynamic spatial–temporal domain

One of the crucial issues at the forefront of smart ship development that urgently needs solutions is navigational situation safety awareness technology. The development of intelligent navigation safety technology is severely constrained by the issue of challenging mining and accurate measurement of ship encounter dynamic space-time variation law and unknown nonlinear spatial representation information. This work creatively offers a GIS spatial modeling and analysis method based on the dynamic space-time domain of ship encounters, based on the GIS spatial information platform and spatial analysis theory. Based on the ship's approaching rate, encounter hazard identification parameters, relative space-time position distribution, etc., combined with the normal distribution function of the spatial distance adjustment variable, the method successfully constructs a multi-time-space, multi-angle, and multi-state encounter space model. By efficiently reducing the ambiguity of the spatial distribution of hazards, it realizes the in-depth analysis of the spatial distribution features of potential hazards as well as the temporal and spatial trends in the dynamic process of ship navigation. To break through the technical problems of spatial analysis and perception of ship-encountered dangerous area sources. It offers a fresh approach for enhancing intelligent navigation's capacity for safety analysis of ship interactions.

Abstract 2369: Spatial organization of the tumour immune microenvironment (TIME) in primary and metastatic melanoma is associated with patient outcome

Abstract Introduction The complex spatial cellular interactions of immune cell populations within the melanoma TIME are associated with patient prognosis and immunotherapy response. Novel multiplex immunofluorescence (mIHC) and spatial analysis techniques enable comprehensive investigation of these interactions. This study aimed to develop an automated spatial analysis workflow for large patient cohorts and to determine immunospatial patterns associated with outcome in patients (pts) with primary melanoma as well as pts with stage III melanoma treated with adjuvant immunotherapy. Methods Two cohorts with available clinical & mIHC data were studied: one of primary melanoma pts (primary cohort, n=46), and another of baseline resected stage III melanoma pts who received adjuvant anti-PD-1 (stage III cohort, n=119). Melanoma tissue was stained for CD8+ T cells and the markers CD103, PD-1 and CD39 in both cohorts and the primary cohort was additionally stained for NK cells, B cells, and Langerhans cells (LCs). mIHC data was investigated using the SPIAT spatial analysis R package. Immune-melanoma and immune-immune spatial relationships were quantified throughout the entire tissue and analysed using neighborhood analysis and cell colocalization metrics including entropy and mixing interaction scores. Results In the primary melanoma cohort, 11 neighborhood metaclusters (NMCs) were identified from 1617 neighborhoods. NMCs with high B cells, CD39+CD103+ CD8+ T cells, or LCs were associated with improved outcomes in this cohort. Neighborhood composition varied significantly based on tissue localisation, with B cells, LCs and NK cells significantly increased in marginal and stromal neighborhoods (p<0.0001). Over 100 B cell aggregate (BCA) neighborhoods were identified predominantly at the tumor margins and adjacent stroma. These BCAs were likely early TLSs based on their localisation and immune composition. BCA composition was further compared based on patient outcomes, with those harboring increased CD103-PD-1+ CD8+ T cells associated with poor outcome. In-depth pairwise analysis of immune phenotypes with melanoma cells found that increased B cell-melanoma and CD103+PD-1- CD8+ T cell-melanoma interaction was associated with improved outcomes in the primary cohort. In the stage III cohort, increased intratumoral immune diversity was associated with reduced melanoma recurrence (p=0.0004). Conclusions In developing a cohort-wide TIME spatial analytic approach and workflow, we identified multiple immunospatial associations with patient outcome in primary and stage III metastatic melanoma. Intratumoral immune diversity and immune cell neighborhoods indicate beneficial anti-tumor immune functions contributing towards improved patient outcomes. Citation Format: Grace Heloise Attrill, Eva R. Shteinman, Xinyu Bai, Felix Marsh-Wakefield, Camelia Quek, Umaimainthan Palendira, Ismael A. Vergara, Georgina V. Long, Richard A. Scolyer, James S. Wilmott. Spatial organization of the tumour immune microenvironment (TIME) in primary and metastatic melanoma is associated with patient outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2369.

Species richness, extent and potential threats to mangroves of Sarangani Bay Protected Seascape, Philippines.

Mangroves form one of the most vital tropical ecosystems that support many species and surrounding communities. The Sarangani Bay Protected Seascape (SBPS) in the south of Mindanao Islands in the Philippines is home to a large number of mangrove species, which have not been fully explored. We updated the list of true mangrove species for SBPS from 10 to 24 by integrating the results of our survey and other past mangrove assessments. A practical spatial analysis approach was used to estimate the current mangrove forest extent of SBPS at 514 ha, as compared to 479 ha and 332 ha in 1998 and 2016, respectively, from other independent reports. Mangrove cover was negatively related to built area, cropland, bare ground, rangeland and total human population, but positively related to the number of fishing boats and total tree cover. In addition, we identified other potential anthropogenic threats to mangroves and categorised them into forest clearing or deforestation, over-extraction and pollution. The benefits of mangrove cover expansion, adoption of mangrove-friendly aquaculture and revitalising degraded mangrove forests outweigh their constraints. Our work provided a locally relevant understanding of the potential causes of mangrove loss and the values of human actions in mangrove dynamics, which will contribute to reliable and informed decision-making for the conservation of mangrove species and restoration of mangrove forests in SBPS.

A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research.

With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on this topic, specifically when such models would be useful. This systematic review therefore aimed at reviewing health research published using joint spatial and spatiotemporal models. A systematic search of published studies that applied joint spatial and spatiotemporal models was performed using six electronic databases without geographic restriction. A search with the developed search terms yielded 4077 studies, from which 43 studies were included for the systematic review, including 15 studies focused on infectious diseases and 11 on cancer. Most of the studies (81.40%) were performed based on the Bayesian framework. Different joint spatial and spatiotemporal models were applied based on the nature of the data, population size, the incidence of outcomes, and assumptions. This review found that when the outcome is rare or the population is small, joint spatial and spatiotemporal models provide better performance by borrowing strength from related health outcomes which have a higher prevalence. A framework for the design, analysis, and reporting of such studies is also needed.

How does ICT literacy influence reading literacy score in Indonesia: first attempt using spatial analysis approach

PurposeThe aims of this research include (1) to identify the scores of reading literacy in 34 provinces and (2) to determine the impact of ICT literacy with other variables on reading literacy in Indonesia.Design/methodology/approachThematic maps and Spatial Autoregressive Regression were applied to 2019 AKSI Survey data.FindingsThe results showed that only D.I. Yogyakarta, DKI Jakarta and Kepulauan Riau have a high percentage of reading literacy scores in the excellent category. The ICT literacy and teachers' competency scores significantly affect the percentage of reading literacy. Meanwhile, the percentage of lack of learning materials and GRDP per capita has no significant effect.Originality/valuePreviously, the national exam has been used to determine the quality of education in Indonesia, but it is ineffective because it only measures cognitive aspects. In 2015, the Ministry of Education initiated the AKSI survey, which measures cognitive (reading, math and science literacy) and non-cognitive aspects, as an effort to improve the quality of education in Indonesia. Some literature states that reading literacy is the most basic indicator for determining the quality of education, but in Indonesia, it is the lowest achievement. To improve reading literacy scores, the government has to utilize technological advances through School Digitization. However, this should be supported by the ICT literacy of students. Presently, there is no study to evaluate the impact of ICT literacy on reading literacy, which is also affected by regional value differences.

Gully erosion model of the archaeological site of Eridu Ziggurat in the southern plateau of Iraq using RS-GIS technology

The metamorphic processes represented by the process of erosion of the grooves by rainwater is one of the most important processes that affect the structure of the archaeological hills, including the site of Zaqra Eridu. This worked on the activity of rain erosion operations during the rainy season of the year. The problem of the research is whether rain erosion leaves grooves that distort the facades or remnants of the Eridu ziggurat area, and can the severity and degree of erosion erosion be measured within the site of the study area using GIS technology, and is it possible to provide means of protection and maintenance of the archaeological site from the processes of gully erosion. The spatial analytical approach will be adopted and the mathematical quantitative approach based on the extrapolation and analysis of geographical data will be adopted, as well as the use of the descriptive approach. In order to achieve this goal, a field survey will be postponed to discover the site of the Eridu Ziggurat by observing and observing the grooves that the region was exposed to, preparing a digital spatial database for the region and building The digital elevation model, as well as building a gully erosion model, its degree and intensity for the location of the study area. GIS technology, in addition to providing a practical study and a spatial database, the results of which benefit from the General Authority for Antiquities of Dhi Qar Governorate. The study shows that the ziggurat of Arduwa is located within a range of a complex drainage network that reaches the sixth rank, and it is also guaranteed that it penetrates with a network of high density (340-580 m / km 2) according to the classification of the density of the river beds, and therefore it suffered from being cut by grooves that showed its buried features under the surface of the earth on one side Its outer walls were smashed on the other hand, that is, it is within the range of high erosion within the category (1501-2700) according to the degrees of erosion of Berkisma. This study seeks to preserve archaeological sites by following planning standards for their maintenance and restoration and exploitation of the attractions of archaeological tourism.

Integrated GIS and multivariate statistical approach for spatial and temporal variability analysis for lake water quality index

It is critical to monitor water quality to keep water bodies ecologically healthy and facilitate the sustainable development of Kenyir Lake. Water quality differs temporally and spatially and is affected by several factors. Typically, water quality inspection systems are cost- and labour-intensive depending on water quality indicator count and sampling frequency. Optimising the frequency and location of water quality sampling is crucial. This study focused on collecting water samples from 22 locations in Kenyir Lake during different seasons (normal, dry, and wet). The study aimed to assess the spatial and temporal variations in the water quality of Kenyir Lake based on multivariate statistical methods. In this study, the following water quality parameters were selected for analysis: temperature, dissolved oxygen (DO), pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), and ammoniacal nitrogen (NH3-N). In addition, a water quality index was also calculated. GIS software was used to assess water quality data, and various multivariate statistical methods like cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) were employed. The outcome shows minor spatial differences concerning Kenyir Lake; however, the temporal variations were noteworthy during this study duration. Cluster analysis divided the locations into 3 clusters with TSS being key parameter affecting the spatial differences in water quality. Stepwise discriminant analysis based on three parameters, pH, temperature, and TSS, produced the associated classification matrix that correctly estimated 69.7% of the input. NH3-N and TSS were found to be the two critical aspects that affect water quality during dry, wet, or normal climatic conditions.

Towards a movement-friendly city: lessons from activity scans of five neighbourhoods in Antwerp, Belgium

ABSTRACT This paper describes the results of a research project in Antwerp, Belgium, aimed at developing a spatial analysis approach (‘activity scan’) assessing how the urban fabric on a neighbourhood scale can encourage physical activity and active transport. By outlining a set of recurring spatial challenges and describing the developed innovative toolkit, the research responds to the necessity for design-oriented insights on how to promote active travel and instigate concrete interventions. By critically analysing this co-creative research trajectory, this article emphasizes the need for an interdisciplinary dialogue on what contributes to a movement-friendly urban environment.

Civic engagement and socio-economic proximity in urban areas

ABSTRACT Diversity boosts innovation and creativity in urban contexts, but it can also undermine civicness by negatively impacting individuals’ trust of other citizens, also hampering economic and institutional performance. By employing a spatial analysis approach using geocoded data from 5776 residents in three major urban Italian areas, we explore whether sharing geographical space in a context of socio-economic difference (in terms of income and education) affects individuals’ choices to be civically engaged. The evidence reveals that the geographical proximity of an individual to others with different socio-economic characteristics decreases their civic engagement. A key challenge when designing urban policies is to reconcile the positive effects of diversity as it influences different economic–societal aspects, with the associated social tensions.

Undernutrition among the children below five years of age in Uganda: a spatial analysis approach

BackgroundUndernutrition is a health condition caused by a lack of enough food intake, not having enough of the right combination of food nutrients, or the body’s failure to utilize the food eaten resulting in either, stunting, being underweight, or wasting. Globally, undernutrition affects more than 149 million under-five children, while in Uganda about 3 in every 10 children suffer from undernutrition. Undernutrition and its risk factors among under-five children in Uganda were unevenly distributed across the country and a study that focused on spatial distribution was prudent to examine the nature of the problem and salient factors associated with it. The current study addressed the issues of spatial heterogeneity of undernutrition and its determinants with the goal to identify hot spots and advise policymakers on the best actions to be taken to address the problem.MethodsData were obtained from the 2016 Uganda Demographic and Health Survey. Prevalence rates and percentages of risk factors were combined with the Uganda district shape file to allow spatial analysis. Moran’s I, Getis-Ord (GI*), and Geographically Weighted Regressions were respectively used to establish the local, global, and geographically weighted regressions across the country. Stata 15 and ArcGIS 10. 7 soft wares were used.ResultsThe results indicate that undernutrition in Uganda shows varies spatially across regions. Evidence of hot spots exists in the Karamoja and Arua regions, cold spot areas exist around the central part of the country while the greatest part of Western Uganda, Northern, and Eastern were not significant.ConclusionThe study reveals that a variation in the distribution of undernutrition throughout the country. Significant spatial patterns associated with undernutrition as identified through the hotspot and cold spot analysis do exist in Uganda. Programs targeting to reduce the undernutrition of under-five children in Uganda should consider the spatial distribution of undernutrition and its determinants whereby priority should be given to hotspot areas. The spatial intensity of undernutrition and its determinants indicate that focus should be tailored to meet the local needs as opposed to a holistic national approach.

Relationship between Urban Environmental Components and Dengue Prevalence in Dhaka City-An Approach of Spatial Analysis of Satellite Remote Sensing, Hydro-Climatic, and Census Dengue Data.

Dengue fever is a tropical viral disease mostly spread by the Aedes aegypti mosquito across the globe. Each year, millions of people have dengue fever, and many die as a result. Since 2002, the severity of dengue in Bangladesh has increased, and in 2019, it reached its worst level ever. This research used satellite imagery to determine the spatial relationship between urban environmental components (UEC) and dengue incidence in Dhaka in 2019. Land surface temperature (LST), urban heat-island (UHI), land-use-land-cover (LULC), population census, and dengue patient data were evaluated. On the other hand, the temporal association between dengue and 2019 UEC data for Dhaka city, such as precipitation, relative humidity, and temperature, were explored. The calculation indicates that the LST in the research region varies between 21.59 and 33.33 degrees Celsius. Multiple UHIs are present within the city, with LST values ranging from 27 to 32 degrees Celsius. In 2019, these UHIs had a higher incidence of dengue. NDVI values between 0.18 and 1 indicate the presence of vegetation and plants, and the NDWI identifies waterbodies with values between 0 and 1. About 2.51%, 2.66%, 12.81%, and 82% of the city is comprised of water, bare ground, vegetation, and settlement, respectively. The kernel density estimate of dengue data reveals that the majority of dengue cases were concentrated in the city's north edge, south, north-west, and center. The dengue risk map was created by combining all of these spatial outputs (LST, UHI, LULC, population density, and dengue data) and revealed that UHIs of Dhaka are places with high ground temperature and lesser vegetation, waterbodies, and dense urban characteristics, with the highest incidence of dengue. The average yearly temperature in 2019 was 25.26 degrees Celsius. May was the warmest month, with an average monthly temperature of 28.83 degrees Celsius. The monsoon and post-monsoon seasons (middle of March to middle of September) of 2019 sustained higher ambient temperatures (>26 °C), greater relative humidity (>80%), and at least 150 mm of precipitation. The study reveals that dengue transmits faster under climatological circumstances characterized by higher temperatures, relative humidity, and precipitation.

Integration of Moran's I, geographically weighted regression (GWR), and ordinary least square (OLS) models in spatiotemporal modeling of COVID-19 outbreak in Qom and Mazandaran Provinces, Iran.

Globally, the COVID-19 pandemic is a top-level public health concern. This paper attempts to identify the COVID-19 pandemic in Qom and Mazandaran provinces, Iran using spatial analysis approaches. This study was based on secondary data of confirmed cases and deaths from February 3, 2020, to late October 2021, in two Qom and Mazandaran provinces from hospitals and the website of the National Institute of Health. In this paper, three geographical models in ArcGIS 10.8.1 were utilized to analyze and evaluate COVID-19, including geographic weight regression (GWR), ordinary least squares (OLS), and spatial autocorrelation (Moran I). The results from this study indicate that the rate of scattering of confirmed cases for Qom province for the period was 44.25%, while the rate of dispersal of the deaths was 4.34%. Based on the GWR and OLS model, Moran's statistics demonstrated that confirmed cases, deaths, and recovered followed a clustering pattern during the study period. Moran's Z-score for all three indicators of confirmed cases, deaths, and recovered was confirmed to be greater than 2.5 (95% confidence level) for both GWR and OLS models. The spatial distribution of indicators of confirmed cases, deaths, and recovered based on the GWR model has been more scattered in the northwestern and southwestern cities of Qom province. Whereas the spatial distribution of the recoveries of the COVID-19 pandemic in Qom province was 61.7%, the central regions of this province had the highest spread of recoveries. The spatial spread of the COVID-19 pandemic from February 3, 2020, to October 2021 in Mazandaran province was 35.57%, of which 2.61% died, according to information published by the COVID-19 pandemic headquarters. Most confirmed cases and deaths are scattered in the north of this province. The ordinary least squares model results showed that the spatial dispersion of recovered people from the COVID-19 pandemic is more significant in the central and southern regions of Mazandaran province. The Z-score for the deaths Index is more significant than 14.314. The results obtained from this study and the information published by the National Headquarters for the fight against the COVID-19 pandemic showed that tourism and pilgrimages are possible factors for the spatial distribution of the COVID-19 pandemic in Qom and Mazandaran provinces. The spatial information obtained from these modeling approaches could provide general insights to authorities and researchers for further targeted investigations and policies in similar circumcises.

Cyclists’ exposure to air pollution, noise, and greenery: a population-level spatial analysis approach

Urban travel exposes people to a range of environmental qualities with significant health and wellbeing impacts. Nevertheless, the understanding of travel-related environmental exposure has remained limited. Here, we present a novel approach for population-level assessment of multiple environmental exposure for active travel. It enables analyses of (1) urban scale exposure variation, (2) alternative routes’ potential to improve exposure levels per exposure type, and (3) by combining multiple exposures. We demonstrate the approach’s feasibility by analysing cyclists’ air pollution, noise, and greenery exposure in Helsinki, Finland. We apply an in-house developed route-planning and exposure assessment software and integrate to the analysis 3.1 million cycling trips from the local bike-sharing system. We show that especially noise exposure from cycling exceeds healthy thresholds, but that cyclists can influence their exposure by route choice. The proposed approach enables planners and individual citizens to identify (un)healthy travel environments from the exposure perspective, and to compare areas in respect to how well their environmental quality supports active travel. Transferable open tools and data further support the implementation of the approach in other cities.

COVID-19 during the first pandemic wave in Valle de México and México City: A spatial analysis approach in small areas

The first cases of the COVID-19 disease in Mexico came from abroad in February 2020. Communitarian propagation accelerated the infection in the big metropolitan areas of Mexico, such as Valle de México Metropolitan Zone (VMMZ), were located the biggest people concentration in the country. In this study, we evaluate the spatial distribution of the positive cases and deaths in VMMZ at municipality level through a spatial econometric model that include socio demographic and economic variables, besides we explore the active cases in Ciudad de México at neighborhood level. We found significant spatial effects, most notably in positive cases, that could help to explain the stage of the disease, in both levels municipality and neighborhood. The model shed light to observe how the COVID19 hits harder at the municipalities more densely populated and where the urbanization process was deeper, compared with those peripheral, nevertheless, worst living conditions also exhibit a positive relationship, in both positive cases and deaths.

The impact of traditional gold mining on land use changes and vegetation index in Mandor Subwatershed, West Kalimantan

Traditional gold mining activities altered the environmental structure of the Mandor Subwatershed significantly. The expansion of critical land in the Mandor Subwatershed causes flooding due to the lack of water catchment areas. The purpose of this study was to identify the impact of traditional gold mining on land use change in the Mandor Subwatershed. The research was conducted with a spatial analysis approach using Landsat multitemporal images from 2002, 2013, and 2022, followed by a field survey. A comparison of the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) methods was used to determine the changing process of vegetation density. The accuracy of vegetation index analysis indicated that the EVI method was more accurate for identifying vegetation density than the NDVI method. Land use change from 2002 to 2022 was dominated by an increase in the land area devoted to mining and oil palm plantations. The impact of this traditional gold mining has led to significant deforestation and land degradation over the past 20 years in the Mandor Subwatershed. This affects the condition of the surrounding environment as well as human health.

The effect of sanitation risk on toddler stunting incident with geographically weighted regression approach in Malang Regency, Indonesia

Stunting is a fundamental problem in Indonesia's human development. The risk factors for stunting are very complex, including inadequate environmental sanitation. This study aims to analyze and map the influence of spatially correlated sanitation risk on the incidence of stunting toddlers in Malang Regency, Indonesia. Data collection was carried out by interviewing respondents using questionnaires, counting stunting toddlers, and observing environmental sanitation objects. Determination of respondents was done purposively with the following stages. First, at the village level, 75 sites were selected based on the following criteria: population density; poverty level; the presence of streams, drainage or irrigation canals; and flood vulnerability. Furthermore, in each village, 40 respondents were selected based on the following criteria: women aged between 18 to 65 years, came from poor families and had a child (or children). When the number of respondents who were willing to participate is less than 40 people in each village, they were replaced by other respondents based on recommendations from village officials. A total of 3000 respondents have participated in this study. The selected respondents include wives, dependent married women or widows. The spatial analysis approach was carried out using the Geographically Weighted Regression (GWR) method. Based on the results of the analysis of the spatial effect of the GWR model, the Sanitation Risk Factors that significantly affected the incidence of stunting in Malang Regency were; Environmental Drainage (greywater), Solid Waste, and Domestic Wastewater (black water). Clean and Healthy Lifestyles and Water Sources have less effect on sanitation risk. This study recommends government and public interventions to improve environmental sanitation including integrated waste management, improvement of drainage infrastructure, construction of standardized toilets, and carrying out health promotion efforts with education to increase public awareness.

Urbana morfologija u Britaniji - vreme za preispitivanje i regrupisanje?

This article reviews the contribution of the work of the Urban Morphology Research Group (UMRG) to British urban morphological research. The group, led by Jeremy Whitehand, provided a focus for British research in urban morphology grounded in the work of M.R.G Conzen and the historico-geographical approach. The article reviews four core strands to this work: definition of the historico-geographical approach, morphological regions, the processes and people shaping urban landscapes and linking research and practice. The article also provides an overview of other areas of research into urban form within Britain beyond the UMRG, from scholars working in disciplines such as geography, architecture, and urban design. Two broad areas of work are focused on, namely spatial analytical and configurational approaches and British urban geographical traditions. In conclusion, the article reflects on the future for British urban morphology following the loss of Whitehand as its long-standing figurehead and champion, suggesting that is it time to form a new network to replace the now-dormant UMRG to ensure the continued vibrancy and visibility of urban morphological research in Britain.

ANALYSIS OF THE STATE OF PUBLIC TRANSPORT IN ALMATY

Public transport today is again gaining relevance as a means of transportation in connection with the personal cars that have flooded urban spaces. The city of Almaty is no exception and since the beginning of the last decade has taken a course to organize pilot projects to create a priority for the movement of public transport. The difficulty of implementing such innovations is the public, accustomed to crossing long distances by private vehicles, and in most cases city streets are loaded from nearby agglomerations. The emergence of such a trend is directly related to the expansion of the city in breadth, i.e. from east to west, because the natural uniqueness of the urban area in the south is limited by the mountain ranges of the Trans-Ili Alatau. This paper is presented taking into account the existing initial data for the study of public transport issues through the introduction of the GTFS scientific methodology, which can give a new angle of view on the current situation with the organization of bus and trolleybus routes. This paper focuses on studying the potential of public transport in Almaty for consistent growth, because with the help of the restructuring of route networks and the creation of a priority traffic network, there is a chance to achieve an increase in capacity and an increase in the number of users. The aim of this article is to provide information about the current state of the public transport network and to discuss the potential of geographic information systems (GIS) within the urban space, which are guided by spatial analysis approaches related to the processing of General Transit Feed Specification data (GTFS), since statistical data are based on providing a complete picture of the existing transport network, and afterwards can become the basis for subsequent optimization of public transport traffic. This paper creates new perspectives for future development of public transport and restructuring of the understanding how to create public network according to necessity in Almaty city.

Exploring the Spatial Autocorrelation in Soil Moisture Networks: Analysis of the Bias from Upscaling the Texas Soil Observation Network (TxSON)

Microwave remote sensing such as soil moisture active passive (SMAP) can provide soil moisture data for agricultural and hydrological studies. However, the scales between station-measured and satellite-measured products are quite different, as stations measure on a point scale while satellites have a much larger footprint (e.g., 9 km). Consequently, the validation for soil moisture products, especially inter-comparison between these two types of observations, is quite a challenge. Spatial autocorrelation among the stations could be a contribution of bias, which impacts the dense soil moisture networks when compared with satellite soil moisture products. To examine the effects of spatial autocorrelation to soil moisture upscaling models, this study proposes a spatial analysis approach for soil moisture ground observation upscaling and Thiessen polygon-based block kriging (TBP kriging) and compares the results with three other methods typically used in the current literature: arithmetic average, Thiessen polygon, and Gaussian-weighted average. Using the Texas Soil Observation Network (TxSON) as ground observation, this methodology detects spatial autocorrelation in the distribution of the stations that exist in dense soil moisture networks and improved the spatial modeling accuracy when carrying out upscaling tasks. The study concluded that through TBP kriging the minimum root-mean-square deviation (RMSD) is given where spatial autocorrelation takes place in the soil moisture stations. Through TBP kriging, the station-measured and satellite-measured soil moisture products are more comparable.