Use Of Spatial Analysis Techniques Research Articles

Flood disaster mitigation measure recommendations based on flood hazard and risk assessment: a case study in Brebes Regency, North Coast of Java

The concern over floods in the northern coast of Java is more pronounced. The increasing frequency of flood events, material damage and death rates underscore the importance of integrated efforts in controlling floods there. Brebes Regency has good prospects for economic growth and infrastructure. However, because its location on the north coast of Java makes it vulnerable to flooding. Considering this, it is necessary to carry out a comprehensive evaluation of flood hazards and risk assessment in Brebes Regency, as part of efforts to mitigate flood disasters and encourage the development of Brebes Regency as a resilient city. Flood hazard and risk assessment involves the use of spatial analysis techniques, which include the process of examining the level of vulnerability, determining flood hazard zones, and creating a flood risk map. Vulnerability analysis incorporates land use, population density, and economic activities. Flood hazard zoning assessment were carried out using rainfall data, tidal water levels and topography. The findings of the risk assessment indicate that the northern region of Brebes Regency has a high level of risk of flooding. Moreover, these flood risk maps can serve as a valuable tool to inform and guide the development of flood mitigation strategies and infrastructure.

Evaluation of Physiochemical Factors in Saffia Nature Reserve, Southern Iraqi Marshes, using Geographic Information System Techniques

This survey was conducted during four seasons in nine stations representing the Saffia Nature Reserve (SNR) in Al-Hawizeh marsh, southern Iraq, which has 44 km . Physical and chemical parameters were monitored; including natural water quality parameters such 2 as water temperature, pH, electric conductivity and dissolved oxygen in addition to levels of nutrients from September 2019 to August 2020. The current study discussed the possible use of spatial analysis techniques to characterize the temporal and spatial distribution of water prediction properties employing geographic information systems (GIS) to determine water quality parameters at SNR. In general, all parameters are within the acceptable limit of freshwater for aquatic life except electric conductivity. The current survey could serve as a basis for more monitoring and restoration of the marshland environment. The use of a geographic information system in evaluating the water quality depends on the laboratory values of water samples and spatial analysis of these properties employing inverse interpolation of the weighted distance. It was possible to map water quality indicators along the study area for nine sites and six water quality indicators. The production of water quality maps will improve monitoring and enforcement of standards and regulations for better management and control. This study suggested continuous monitoring of the physical and chemical characteristics of water marshes and water bodies, and the study of factors affecting the increase in the concentration of elements and nutrients and comparing them with environmental determinants.

Restructuring of emergency services to COVID-19 in Brazil: a space-time analysis from February to August 2020.

To explore the reorganization of the health system in response to the COVID-19 pandemic. We conducted an ecological, descriptive-exploratory study with analysis of spatio-temporal clusters by epidemiological week in Brazilian municipalities. Secondary data sources were used, from the National Health Establishment Registry (April 2020) and on COVID-19 cases, February to August 2020. Intensive Care Units (ICU) availability was overlaid on incidence and mortality hot spots in order to assess expanded access in critical regions. Of the 5,570 municipalities analyzed, 54% were identified as incidence hot spots and 31% as mortality hot spots. Of the municipalities in incidence hot spots and with scarce access, 28% were covered by ICU expansion, while with regard to mortality 14% were covered. The opening of new beds was not able to provide broad coverage for critical regions and could be optimized with the use of spatial analysis techniques.

Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne

Understanding where, when, what type and why crashes are occurring can help determine the most appropriate initiatives to reduce road trauma. Spatial statistical analysis techniques are better suited to analysing crashes than traditional statistical techniques as they allow for spatial dependency and non-stationarity. For example, crashes tend to cluster at specific locations (spatial dependency) and vary from one location to another (non-stationarity). Several spatial statistical methods were used to examine crash clustering in metropolitan Melbourne, including Global Moran’s I statistic, Kernel Density Estimation and Getis-Ord Gi* statistic. The Global Moran’s I statistic identified statistically significant clustering on a global level. The Kernel Density Estimation method showed clustering but could not identify the statistical significance. The Getis-Ord Gi* method identified local crash clustering and found that 15.7 per cent of casualty crash locations in metropolitan Melbourne were statistically significant hot spots at the 95 per cent confidence level. The degree, location and extent of clustering was found to vary for different crash categories, with fatal crashes exhibiting the lowest level of clustering and bicycle crashes exhibiting the highest level of clustering. Temporal variations in clustering were also observed. Overlaying the results with land use and road classification data found that hot spot clusters were in areas with a higher proportion of commercial land use and with a higher proportion of arterial and sub-arterial roads. Further work should investigate network based hot spot analysis and explore the relationship between crash clusters and influencing factors using spatial techniques such as Geographically Weighted Regression.

Advances in mapping population and demographic characteristics at small-area levels.

Temporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health.In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys.We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.

COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling.

This study aimed to analyse the spatial-temporal distribution of COVID-19 mortality in Sergipe, Northeast, Brazil. It was an ecological study utilising spatiotemporal analysis techniques that included all deaths confirmed by COVID-19 in Sergipe, from 2 April to 14 June 2020. Mortality rates were calculated per 100 000 inhabitants and the temporal trends were analysed using a segmented log-linear model. For spatial analysis, the Kernel estimator was used and the crude mortality rates were smoothed by the empirical Bayesian method. The space-time prospective scan statistics applied the Poisson's probability distribution model. There were 391 COVID-19 registered deaths, with the majority among ⩾60 years old (62%) and males (53%). The most prevalent comorbidities were hypertension (40%), diabetes (31%) and cardiovascular disease (15%). An increasing mortality trend across the state was observed, with a higher increase in the countryside. An active spatiotemporal cluster of mortality comprising the metropolitan area and neighbouring cities was identified. The trend of COVID-19 mortality in Sergipe was increasing and the spatial distribution of deaths was heterogeneous with progression towards the countryside. Therefore, the use of spatial analysis techniques may contribute to surveillance and control of COVID-19 pandemic.

Spatial and temporal analysis of tuberculosis in an area of social inequality in Northeast Brazil

BackgroundTuberculosis is an infectious disease caused by Mycobacterium tuberculosis. It is a disease known worldwide for its vulnerability factors, magnitude and mortality. The objective of the study was to analyze the spatial and temporal dynamics of TB in the area of social inequality in northeast Brazil between the years 2001 and 2016.MethodsAn ecological time series study with the use of spatial analysis techniques was carried out from 2001 to 2016. The units of analysis were the 75 municipalities in the state of Sergipe. Data from the Notification of Injury Information System were used. For the construction of the maps, the cartographic base of the state of Sergipe, obtained at the Instituto Brasileiro de Geografia e Estatística, was used. Georeferenced data were analysed using TerraView 4.2.2 software (Instituto Nacional de Pesquisas Espaciais) and QGis 2.18.2 (Open Source Geospatial Foundation). Spatial analyses included the empirical Bayesian model and the global and local Moran indices. The time trend analyses were performed by the software Joinpoint Regression, Version 4.5.0.1, with the variables of sex, age, cure and abandonment.ResultsThere was an increasing trend of tuberculosis cases in patients under 20 years old and 20–39 years old, especially in males. Cured cases showed a decreasing trend, and cases of treatment withdrawal were stationary. A spatial dependence was observed in almost all analysed territories but with different concentrations. Significant spatial correlations with the formation of clusters in the southeast and northeast of the state were observed. The probability of illness among municipalities was determined not to occur in a random way.ConclusionThe identification of risk areas and priority groups can help health planning by refining the focus of attention to tuberculosis control. Understanding the epidemiological, spatial and temporal dynamics of tuberculosis can allow for improved targeting of strategies for disease prevention and control.

Probabilistic Landslide risk assessment - Application to Eastern Rif (Morocco)

The present study is consecrated to the probabilistic mapping of the landslide risk at the local scale of an area that belongs to Al Hoceima city in the western Rif of Morocco. The study focuses mainly on the spatial analysis of multi sources data by using an environment GIS (Geographic Information Systems), and the application of the bivariate probabilistic model to qualify the risk susceptibility. The employed methodology is based on three stages. First, the evaluation of landslide susceptibility (S) by the analysis model cited before. Second, the identification and the estimation of the potential consequences (C) for the existing issues. Finally, the landslide risk (R) is evaluated by combining the susceptibility and the potential consequences map. This study requires the use of spatial analysis techniques. It also refers to the risk maps scale, generally reduced and being inappropriate at the urban project area. The obtained risk map defines four risk intensities with a spatial resolution of two meters.

Spatial and spatiotemporal occurrence of human visceral leishmaniasis in Adamantina, State of São Paulo, Brazil.

Several municipalities of the Western region of the State of São Paulo have been affected by human visceral leishmaniasis (HVL), including the City of Adamantina, where the first autochthonous cases occurred in 2004. Therefore, this study aimed to describe the spatial and spatiotemporal occurrence of HVL in Adamantina. Secondary data regarding the occurrence of HVL in Adamantina between 2004 and 2011 were used. Incidence, mortality, and case fatality rates were calculated. We used local empirical Bayesian incidence rates to represent the occurrence of the disease in the census sector of the city. The existence of spatial and spatiotemporal clusters of cases was evaluated using scan statistics. In situ observation was performed to assess the socioeconomic and environmental characteristics of the areas with medium and high incidences. Adamantina reported cases in 70% of its census sectors. No differences were observed between sexes. The group aged 0-4 years presented the highest incidence and mortality rates, and the group aged 40-59 years presented the highest fatality rate. We detected a spatiotemporal cluster, which coincided with the commencement of the endemic in the city. The individuals most affected by the disease were children. The disease was present in areas with better and worse socioeconomic conditions. The use of spatial analysis techniques was important to achieve the study objectives.

A Spatial Analysis of Poverty in Kigali, Rwanda using indicators of household living standard

This study examines the poverty pattern occurring in Kigali through the use of spatial analysis techniques. It seeks to further decipher the underlying factors contributing to the emerging pattern of poverty. These kinds of information are useful to the Kigali administration as input into devising appropriate poverty reduction strategy for the city. Household living standard is examined using data from the Integrated Living Condition survey (EICV: Enquete Integrale sur les Conditions de vie des menages) conducted by the National Institute of Statistics of Rwanda in 2000-2001. It was meant to measure household expenditures, consumption and income, as well as demographic and socioeconomic characteristics of the population. Four poverty dimensions were employed in the analysis of urban poverty in Kigali, namely: expenditure, health, education, and services. The influence exerted by each dimension on the overall poverty level is examined. Several indices were computed such as the poverty headcount, poverty gap and a composite household living standard index. Looking at the example of the poverty headcount index, three distinct regions of poverty incident can be deciphered. In the first region with the highest poverty incident, the number of poor is between 32-78%. These are the extreme southern and north-western part of Kigali comprising of south Kicukiro, south Gikondo and south Butamwa districts and the north-western part of Gisozi District. The second region of medium poverty incident has between 12-32% of its population poor. These are the north of Gisozi, eastern part of Kanombe, northern portions of Kicukiro, Gikondo and Butamwa districts. The third region of low poverty incident has between 4-12% of its population poor. This region consists mainly of Kacyiru, Kanombe, Nyarugenge, the northern parts of Nyamirambo and Gikondo districts, and the south of Gisozi district. The poverty pattern shows a clear urban and rural dichotomy.

Improving pest management in pet food mills using accurate monitoring and spatial analysis

Preventing the presence of pests in the final product of a food company requires appropriate pest management throughout the different stages of production and in all areas of the facility. Sampling and monitoring pests over time and space is therefore very important for implementing a correct pest management programme. In this study, we present the results of monitoring a pet food mill with pheromone traps focussing on moth pests. The main objective was to evaluate if the use of spatial analysis techniques would help to improve pest management applied in response to the monitoring results. Pest management was based on the evaluation of structural aspects of the facility (physical barriers), chemical treatments and cleaning methods (removal of residues). The use of spatial analysis showed that the walls with closed double doors constituted the only effective barrier against pest dispersal. There was no particular direction of pest movement through the facilities. However, the spatial distribution of the pest had two aggregation areas, one constant in the production area and one, sporadically, in the warehouse, both related to the presence of refuges and food resources. The timing and locality of applications of chemical treatments needed adjustment. When cleaning methods were evaluated, hand sweeping was more effective than automatic sweeping. Residue accumulation was directly proportional to pest presence. Results provided by the application of spatial analysis to the monitoring data can help to implement improved pest management protocols in any facility.

The Use of Spatial Analysis Techniques in Mapping Potential Natural Hazard Areas: A Case Study of Taiwan

Abstract In 2005, the World Bank issued Natural Disaster Hotspots: A Global Risk Analysis, which indicted that Taiwan, may be the most vulnerable place to natural hazards. This paper aims at landslide points, land use data, socioeconomic indicators integrated with the mapping method to analyze the relation between spatial structural relationships and spatial patterns of disaggregate and aggregate levels in natural hazard areas. First, natural hazard locations are derived from geospatial data and coded into a GIS system. Second, the spatial analysis techniques includes different geospatial scale is derived from spatial autocorrelation analysis (SAA), space syntax. On the other hand, spatial analysis data such as digital terrain model (DTM) combination based on land use type relative significance to the process of landslide occurrence. Finally, this paper organizes a framework integrated with the land use plan and spatial analysis techniques (FILS). Results from 2006 showed that the natural hazard area hot spots include Taipei, Taichung, and Kaohsiung.

Role of Spatial Data in the Protection of Critical Infrastructure and Homeland Defense

This paper examines how the use of spatial analysis techniques and geographic information systems may assist in developing a more complete understanding of underlying solutions trends and properties in complex optimization problems. In the model, an attacker with multiple attractive targets and multiple entry points into the region attempts to find a complete network path offering the highest probability of non-detection by a defender who has allocated detection resources to reduce arc non-detection and who wishes to minimize this maximum attacker path (this problem is a direct variant of the shortest path network interdiction problem). This paper introduces spatial analysis techniques to analyze defender resource allocation and attacker path selection in two sample networks (Lancaster-Palmdale, California and Northridge, California). The resulting discussion begins to examine how network diversity and other problem parameters can impact observed defender/attacker solutions and illustrates the importance of this knowledge in the making and evaluating of governmental and public policy decisions.

The Use of Spatial Analysis Techniques in Defect and Nanostructure Studies

Spatial analysis techniques were used to investigate defects and nanostructures in the III-nitride system. Dislocations in GaN films were distributed nonrandomly, forming both short-scale and large-scale linear arrays aligned along $$ \langle 11\bar{2}0\rangle . $$ Both low-density InGaN/GaN quantum dots (QDs) and In droplets on the surface of InAlN films were in spatially random positions and showed no spatial autocorrelation, but high-density InGaN/GaN QDs showed a tendency toward short-range ordering and all features showed a nonrandom size distribution. The spatial arrangements of defects and nanostructures relate to their generation processes and may also affect device properties.

Use of spatial analysis tools in the epidemiological surveillance of American visceral leishmaniasis, Araçatuba, São Paulo, Brazil, 1998-1999

The control of American visceral leishmaniasis (AVL) is based on combating the vector and eliminating the domestic reservoir of the focus area - defined as 200 meters around human or canine cases. This paper discusses the use of spatial analysis techniques in the epidemiological surveillance of AVL in Araçatuba, São Paulo State, in order to propose a model for territorial epidemiological surveillance, reformulating current control strategies. The results showed that AVL transmission was not homogeneous; human cases were more frequent in areas with higher canine prevalence rates. Vector dispersion appeared to be restricted to a few houses, although it was not possible to model the vector density. In order to study the vector distribution and correlated covariates, a field study based on house sampling is being conducted. The results will aid the development of new spatial analysis tools and possibly redefine protocols and routines for the control of this endemic disease in urban areas.

Spatial analysis of Aedes aegypti larval distribution in the Ilha do Governador neighborhood of Rio de Janeiro, Brazil

This study aims to help expand knowledge on Aedes aegypti in Rio de Janeiro, based on spatial analysis of the mosquito's breeding sites in the neighborhood of Ilha do Governador, Rio de Janeiro, from June 1992 to July 1994. Use of spatial analysis techniques is proposed for vector surveillance and control. Information obtained from the number of dwellings per block that were used for the analysis. A smoothing method, a Gaussian Kernel, was used in the spatial distribution analysis. Breaks in National Health Foundation intervention activities were observed between cycles 4 and 5 and 5 and 6, respectively, followed by increases in vector density and vector-positive areas. Permanently positive areas, like slums and a military compound, display the persistence of favorable environmental conditions for oviposition and larval growth, indicating flaws in the vector control program. Although Kernel is an exploratory analytical method involving subjective interpretation, it provides easy and fast visualization of sites subject to different degrees of risk, unaffected by existing political and administrative territorial apportionment.

Putting risk in its place: methodological considerations for investigating extreme event health risk

Health is affected by the places in which people live, work and interact, yet many epidemiological studies overlook the characteristics of places and instead focus solely on the people who inhabit them. Place-based investigations of disparities in health outcomes are concerned with the healthiness of places and not merely the healthiness of the populations in these places. A place-based approach has been used within medical geography and medical sociology, typically in the study of health differentials associated with long-term, cumulative exposures to a wide range of environmental variables. This approach has rarely been extended, however, to health research that looks at the effects of extreme events (such as industrial accidents or hurricanes). The purpose of this paper is to incorporate a place-based framework into extreme event health research. The paper first discusses methodological considerations for a place-based approach and then illustrates the use of spatial analysis techniques as the first step in identifying place-based risk factors in mortality associated with heat waves. The study centers on St. Louis, Missouri, a city where heat waves are frequent and heat-related mortality is high. The results show that heat-related mortality rates during the most severe heat waves were generally higher in the warmer, less stable and more disadvantaged areas of St. Louis and lower in the cooler and more affluent parts of the city. During the milder years analyzed, there was little evidence of a relationship between place-based characteristics and the distribution of heat-related mortality. These findings about extreme event mortality risk would not have been evident from a population-based analysis. Ongoing dialog between epidemiologists and social scientists can help to bring place into the arena of extreme event research and to increase understanding of the role of place in risk.

Long-period Love wave overtone data in North America and the Pacific Ocean: new evidence for upper mantle anisotropy

Two Tonga-trench earthquakes recorded in North America on WWSSN long-period instruments have been processed by making use of spatial analysis techniques to retrieve the phase velocities of Love wave modes. Dispersion data up to the third overtone at periods longer than 30 s have been obtained for two broad areas: the Pacific Ocean and North America. It is found that the observed Love wave phase velocities are not in agreement with the values predicted from the previously published models inferred from Rayleigh wave data. Phase velocity discrepancies as large as 3–4% in the Pacific Ocean and 2% in North America are observed up to the second overtone. Preliminary direct computations, which have been performed in the framework of transversely isotropic models with the axis of symmetry oriented vertically, suggest that the cause for this departure from isotropy probably extends to depths > 200 km.