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Spatial pattern analysis for water quality in free-surface constructed wetland

Mohammadpour R, Shaharuddin S, Chang CK, Zakaria NA, Ab Ghani A

Water Sci Technol, 2014 10 18;70(7):1161-7.
PMID: 25325539 DOI: 10.2166/wst.2014.343

Free-surface constructed wetlands are known as a low-energy green technique to highly decrease a wide range of pollutants in wastewater and stormwater before discharge into natural water. In this study, two spatial analyses, principal factor analysis and hierarchical cluster analysis (HACA), were employed to interpret the effect of wetland on the water quality variables (WQVs) and to classify the wetland into groups with similar characteristics. Eleven WQVs were collected at the 17 sampling stations twice a month for 13 months. All sampling stations were classified by HACA into three clusters, with high, moderate, and low pollution areas. To improve the water quality, the performance of Cluster-III (micropool) is more significant than Cluster-I and Cluster-II. Implications of this study include potential savings of time and cost for long-term data monitoring purposes in the free-constructed wetland.

Matched MeSH terms: Spatial Analysis
Fulltext Spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern, and its correlation with meteorological factors during the first to the second waves

Rendana M, Idris WMR, Abdul Rahim S

J Infect Public Health, 2021 Oct;14(10):1340-1348.
PMID: 34301503 DOI: 10.1016/j.jiph.2021.07.010

Currently, many countries all over the world are facing the second wave of COVID-19. Therefore, this study aims to analyze the spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern during the first to the second waves in the South Sumatra Province of Indonesia. This study used the geographical information system (GIS) software to map the spatial distribution of COVID-19 cases and epidemic spread rate. The spatial autocorrelation of the COVID-19 cases was carried out using Moran's I, while the Pearson correlation was used to examining the relationship between meteorological factors and the epidemic spread rate. Most infected areas and the direction of virus spread were predicted using wind rose analysis. The results revealed that the epidemic rapidly spread from August 1 to December 1, 2020. The highest epidemic spread rate was observed in the Palembang district and in its peripheral areas (dense urban areas), while the lowest spread rate was found in the eastern and southern parts of South Sumatra Province (remote areas). The spatial correlation characteristic of the epidemic distribution exhibited a negative correlation and random distribution. Air temperature, wind speed, and precipitation have contributed to a significant impact on the high epidemic spread rate in the second wave. In summary, this study offers new insight for arranging control and prevention strategies against the potential of second wave strike.

Matched MeSH terms: Spatial Analysis
Fulltext A review of property mass valuation models

Ebrahim Jahanshiri, Taher Buyong, Abdul Rashid Mohd. Shariff

Pertanika Journal of Science & Technology, 2011;19(3):23-30.
MyJurnal

Mass valuation of properties is important for purposes like property tax, price indices construction, and understanding market dynamics. There are several ways that the mass valuation can be carried out. This paper reviews the conventional MRA and several other advanced methods such as SAR, Kriging, GWR, and MWR. SAR and Kriging are good for modeling spatial dependence while GWR and MWR are good for modeling spatial heterogeneity. The difference between SAR and Kriging is the calculation of weights. Kriging weights are based on the spatial dependence or so called the semi-variogram analysis of the price data whereas the weights in SAR are based on the spatial contiguity between the sample data. MWR and GWR are special types of regression where study region is subdivided into local sections to increase the accuracy of prediction through neutralizing the heterogeneity of autocorrelations. MWR assigns equal weights for observations within a window while GWR uses distance decay functions. The merits and drawbacks of each method are discussed.

Matched MeSH terms: Spatial Analysis
Fulltext Spatial clustering of colorectal cancer in Malaysia

Syed Soffian SS, Mohammed Nawi A, Hod R, Abdul Maulud KN, Mohd Azmi AT, Hasim Hashim MH, et al.

Geospat Health, 2023 May 25;18(1).
PMID: 37246545 DOI: 10.4081/gh.2023.1158

INTRODUCTION: The rise in colorectal cancer (CRC) incidence becomes a global concern. As geographical variations in the CRC incidence suggests the role of area-level determinants, the current study was designed to identify the spatial distribution pattern of CRC at the neighbourhood level in Malaysia.
METHOD: Newly diagnosed CRC cases between 2010 and 2016 in Malaysia were identified from the National Cancer Registry. Residential addresses were geocoded. Clustering analysis was subsequently performed to examine the spatial dependence between CRC cases. Differences in socio-demographic characteristics of individuals between the clusters were also compared. Identified clusters were categorized into urban and semi-rural areas based on the population background.
RESULT: Most of the 18 405 individuals included in the study were male (56%), aged between 60 and 69 years (30.3%) and only presented for care at stages 3 or 4 of the disease (71.3%). The states shown to have CRC clusters were Kedah, Penang, Perak, Selangor, Kuala Lumpur, Melaka, Johor, Kelantan, and Sarawak. The spatial autocorrelation detected a significant clustering pattern (Moran's Index 0.244, p< 0.01, Z score >2.58). CRC clusters in Penang, Selangor, Kuala Lumpur, Melaka, Johor, and Sarawak were in urbanized areas, while those in Kedah, Perak and Kelantan were in semi-rural areas.
CONCLUSION: The presence of several clusters in urbanized and semi-rural areas implied the role of ecological determinants at the neighbourhood level in Malaysia. Such findings could be used to guide the policymakers in resource allocation and cancer control.

Matched MeSH terms: Spatial Analysis
Spatial scaling effects on soil bacterial communities in Malaysian tropical forests

Tripathi BM, Lee-Cruz L, Kim M, Singh D, Go R, Shukor NA, et al.

Microb Ecol, 2014 Aug;68(2):247-58.
PMID: 24658414

Spatial scaling to some extent determines biodiversity patterns in larger organisms, but its role in microbial diversity patterns is much less understood. Some studies have shown that bacterial community similarity decreases with distance, whereas others do not support this. Here, we studied soil bacterial communities of tropical rainforest in Malaysia at two spatial scales: a local scale with samples spaced every 5 mover a 150-m transect, and a regional scale with samples 1 to 1,800 km apart. PCR-amplified soil DNA for the bacterial 16S rRNA gene targeting the V1–V3 region was pyrosequenced using Roche/454 GS FLX Titanium platform. A ranked partial Mantel test showed a weak correlation between spatial distance and whole bacterial community dissimilarity, but only at the local scale. In contrast, environmental distance was highly correlated with community dissimilarity at both spatial scales,stressing the greater role of environmental variables rather than spatial distance in determining bacterial community variation at different spatial scales. Soil pH was the only environmental parameter that significantly explained the variance in bacterial community at the local scale, whereas total nitrogen and elevation were additional important factors at the regional scale.We obtained similar results at both scales when only the most abundant OTUs were analyzed. A variance partitioning analysis showed that environmental variables contributed more to bacterial community variation than spatial distance at both scales. In total, our results support a strong influence of the environment in determining bacterial community composition in the rainforests of Malaysia. However, it is possible that the remaining spatial distance effect is due to some of the myriad of other environmental factors which were not considered here, rather than dispersal limitation.

Matched MeSH terms: Spatial Analysis
Spatial analysis and source profiling of beta-agonists and sulfonamides in Langat River basin, Malaysia

Sakai N, Mohd Yusof R, Sapar M, Yoneda M, Ali Mohd M

Sci Total Environ, 2016 Apr 01;548-549:43-50.
PMID: 26799806 DOI: 10.1016/j.scitotenv.2016.01.040

Beta-agonists and sulfonamides are widely used for treating both humans and livestock for bronchial and cardiac problems, infectious disease and even as growth promoters. There are concerns about their potential environmental impacts, such as producing drug resistance in bacteria. This study focused on their spatial distribution in surface water and the identification of pollution sources in the Langat River basin, which is one of the most urbanized watersheds in Malaysia. Fourteen beta-agonists and 12 sulfonamides were quantitatively analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). A geographic information system (GIS) was used to visualize catchment areas of the sampling points, and source profiling was conducted to identify the pollution sources based on a correlation between a daily pollutant load of the detected contaminant and an estimated density of human or livestock population in the catchment areas. As a result, 6 compounds (salbutamol, sulfadiazine, sulfapyridine, sulfamethazine, sulfadimethoxine and sulfamethoxazole) were widely detected in mid catchment areas towards estuary. The source profiling indicated that the pollution sources of salbutamol and sulfamethoxazole were from sewage, while sulfadiazine was from effluents of cattle, goat and sheep farms. Thus, this combination method of quantitative and spatial analysis clarified the spatial distribution of these drugs and assisted for identifying the pollution sources.

Matched MeSH terms: Spatial Analysis
Economic and efficiency based optimisation of water quality monitoring network for land use impact assessment

Camara M, Jamil NR, Abdullah AFB, Hashim RB, Aliyu AG

Sci Total Environ, 2020 May 30;737:139800.
PMID: 32526579 DOI: 10.1016/j.scitotenv.2020.139800

The evaluation of the importance of having accurate and representative stations in a network for river water quality monitoring is always a matter of concern. The minimal budget and time demands of water quality monitoring programme may appear very attractive, especially when dealing with large-scale river watersheds. This article proposes an improved methodology for optimising water quality monitoring network for present and forthcoming monitoring of water quality under a case study of the Selangor River watershed in Malaysia, where different monitoring networks are being used by water management authorities. Knowing that the lack of financial resources in developing countries like Malaysia is one of the reasons for inadequate monitoring network density, to identify an optimised network for cost-efficiency benefits in this study, a geo-statistical technique coupled Kendall's W was first applied to analyse the performance of each monitoring station in the existing networks under the monitored water quality parameters. Second, the present and future changes in non-point pollution sources were simulated using the integrated Cellular Automata and Markov chain model (CA-Markov). Third, Station Potential Pollution Score (SPPS) determined based on Analytic Hierarchy Process (AHP) was used to weight each station under the changes of non-point pollution sources for 2015, 2024, and 2033 prior to prioritisation. Finally, according to the Kendall's W test on kriging results, the weights of non-point sources from the AHP evaluation and fuzzy membership functions, six most efficient sampling stations were identified to build a robust network for the present and future monitoring of water quality status in the Selangor River watershed. This study proposes a useful approach to the pertinent agencies and management authority concerned to establish appropriate methods for developing an efficient water quality monitoring network for tropical rivers.

Matched MeSH terms: Spatial Analysis
Structural analysis of geothermal decline rate

Darwis S, Isnani, Ashat A

Sains Malaysiana, 2007;36:207-211.

The aim of semivariogram modeling is to infer the structure of spatial continuity of the measurements. Practical experiences show that semivariogram modeling is an important step in spatial interpolation. The usual empirical semivariogram is sensitive to extreme data and shows a noised pattern. Some robust empirical semivariogram was proposed. This paper reports the application of pairwise relative empirical semivariogram to Kamojang geothermal decline rate. Using the same data, the usual empirical semivariogram and pairwise semivariogram are compared. Comparative study shows that the empirical pairwise relative semivariogram is able to infer the structure of spatial continuity of the process.

Matched MeSH terms: Spatial Analysis
Fulltext Correlation analysis of air pollutant index levels and dengue cases across five different zones in Selangor, Malaysia

Thiruchelvam L, Dass SC, Zaki R, Yahya A, Asirvadam VS

Geospat Health, 2018 05 07;13(1):613.
PMID: 29772882 DOI: 10.4081/gh.2018.613

This study investigated the potential relationship between dengue cases and air quality - as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were -800.66, -796.22, and -790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.

Matched MeSH terms: Spatial Analysis
Fulltext Spatial autocorrelation in the study of neighbourhoods towards smart cities: empirical evidence from Kerman, Iran

Asra Hosseini

Scientific Research Journal, 2012;9(2):0-0.
MyJurnal

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature of urban areas. This study explored issue of measuring neighbourhoods through spatial autocorrelation method based on Moran’s I index in respect of achieving to best neighbourhoods’ model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% of neighbourhoods’ area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran’s index is associated with disproportional distribution of density and increasing in Moran’s I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people’s quality of life can be related to the way that neighbourhoods’ patterns are defined.

Matched MeSH terms: Spatial Analysis
Spatial assessment and source identification of heavy metals pollution in surface water using several chemometric techniques

Ismail A, Toriman ME, Juahir H, Zain SM, Habir NL, Retnam A, et al.

Mar Pollut Bull, 2016 May 15;106(1-2):292-300.
PMID: 27001716 DOI: 10.1016/j.marpolbul.2015.10.019

This study presents the determination of the spatial variation and source identification of heavy metal pollution in surface water along the Straits of Malacca using several chemometric techniques. Clustering and discrimination of heavy metal compounds in surface water into two groups (northern and southern regions) are observed according to level of concentrations via the application of chemometric techniques. Principal component analysis (PCA) demonstrates that Cu and Cr dominate the source apportionment in northern region with a total variance of 57.62% and is identified with mining and shipping activities. These are the major contamination contributors in the Straits. Land-based pollution originating from vehicular emission with a total variance of 59.43% is attributed to the high level of Pb concentration in the southern region. The results revealed that one state representing each cluster (northern and southern regions) is significant as the main location for investigating heavy metal concentration in the Straits of Malacca which would save monitoring cost and time.
CAPSULE: The monitoring of spatial variation and source of heavy metals pollution at the northern and southern regions of the Straits of Malacca, Malaysia, using chemometric analysis.

Matched MeSH terms: Spatial Analysis
Evaluation of the spatial risk factors for high incidence of dengue fever and dengue hemorrhagic fever using GIS application

Aziz Shafie

Sains Malaysiana, 2011;40:1179-1186.

In Malaysia, the incidence of Dengue Fever (DF) and Dengue Hemorrhagic Fever (DHF) have risen dramatically in the last twenty years. With the use of Geographical Information System an explanation for the spread and control of these diseases can be obtained. This study aims to develop a spatial modeling that can predict the risks for DF and DHF based on environmental factors such as physical surroundings, land use, rainfall, temperature and GIS application using logistic regression. A total of 16 variables were used in the process of spatial modeling development. At the significant level of 0.05, the results of logistic regression showed that only 10 out of 16 significant variables in the modeling process. The accuracy of the resulting model is 70.3%. A crucial feature of this study is a risk area map for incidence of DF and DHF in the study area. This study also highlights the application of spatial analysis in planning and implementing the process for the prevention and control activities of DF and DHF in Malaysia.

Matched MeSH terms: Spatial Analysis
Fulltext Environmental and socioeconomic impacts of pipeline transport interdiction in Niger Delta, Nigeria

Umar HA, Abdul Khanan MF, Ogbonnaya C, Shiru MS, Ahmad A, Baba AI

Heliyon, 2021 May;7(5):e06999.
PMID: 34027190 DOI: 10.1016/j.heliyon.2021.e06999

Over the years, pipelines have been the most economic medium for transporting crude oil to production and distribution facilities in the Niger Delta area of Nigeria. However, damages to the pipelines in this area by interdiction have hampered the continuous flow of crude oil to the facilities. Consequently, the revenue of the government dwindles, and the environment is severely degraded. This study assesses the economic and environmental impacts of pipeline interdiction in the Niger Delta region. Data from National oil spills detection and response agency, Nigeria is used to map spatial distribution of oil spills using Kernel Density Estimation with Geographic Information System. Literature was assessed to synthesize the historical, socioeconomic, and environmental impacts of oil spills and pipeline interdiction. Soil samples were collected from study area to determine the types of hydrocarbon pollutants and their concentrations in comparison with uncontaminated sites in the area. Results show that the range of concentrations of total petroleum hydrocarbon (TPH) for the impacted soil (IMP) was 17.27-58.36 mg/kg; remediated soil (RS) was 11.73-50.78 mg/kg which were higher than the concentrations of 0.68 mg/kg in the control samples (CS). Polycyclic aromatic hydrocarbons (PAH) concentrations were in the range of 0.43-77.54 mg/kg for IMP, 0.42-10.65 mg/kg for RS, against CS value of 0.49 mg/kg while BTEX ranged between 0.02 - 0.38 mg/kg for IMP, 0.01-2.7 for RS against CS value of 0.01. The values of the PAH were higher than the limits of the Department of Petroleum Resources, Nigeria. This study also revealed that pipeline interdiction has affected the livelihood of the inhabitants of the study area and the revenue of the Nigerian government. The major hotspots for oil spills in the Niger Delta region are Bayelsa, Rivers and Delta states.

Matched MeSH terms: Spatial Analysis
Spatial distribution of fine and coarse particulate matter during a southwest monsoon in Peninsular Malaysia

Othman M, Latif MT, Jamhari AA, Abd Hamid HH, Uning R, Khan MF, et al.

Chemosphere, 2021 Jan;262:127767.
PMID: 32763576 DOI: 10.1016/j.chemosphere.2020.127767

This study aimed to determine the spatial distribution of PM2.5 and PM10 collected in four regions (North, Central, South and East Coast) of Peninsular Malaysia during the southwest monsoon. Concurrent measurements of PM2.5 and PM10 were performed using a high volume sampler (HVS) for 24 h (August to September 2018) collecting a total of 104 samples. All samples were then analysed for water soluble inorganic ions (WSII) using ion chromatography, trace metals using inductively coupled plasma-mass spectroscopy (ICP-MS) and polycyclic aromatic hydrocarbon (PAHs) using gas chromatography-mass spectroscopy (GC-MS). The results showed that the highest average PM2.5 concentration during the sampling campaign was in the North region (33.2 ± 5.3 μg m-3) while for PM10 the highest was in the Central region (38.6 ± 7.70 μg m-3). WSII recorded contributions of 22% for PM2.5 and 20% for PM10 mass, with SO42- the most abundant species with average concentrations of 1.83 ± 0.42 μg m-3 (PM2.5) and 2.19 ± 0.27 μg m-3 (PM10). Using a Positive Matrix Factorization (PMF) model, soil fertilizer (23%) was identified as the major source of PM2.5 while industrial activity (25%) was identified as the major source of PM10. Overall, the studied metals had hazard quotients (HQ) value of <1 indicating a very low risk of non-carcinogenic elements while the highest excess lifetime cancer risk (ELCR) was recorded for Cr VI in the South region with values of 8.4E-06 (PM2.5) and 6.6E-05 (PM10). The incremental lifetime cancer risk (ILCR) calculated from the PAH concentrations was within the acceptable range for all regions.

Matched MeSH terms: Spatial Analysis
Fulltext Association between Landscape Factors and Spatial Patterns of Plasmodium knowlesi Infections in Sabah, Malaysia

Fornace KM, Abidin TR, Alexander N, Brock P, Grigg MJ, Murphy A, et al.

Emerg Infect Dis, 2016 Feb;22(2):201-8.
PMID: 26812373 DOI: 10.3201/eid2202.150656

The zoonotic malaria species Plasmodium knowlesi has become the main cause of human malaria in Malaysian Borneo. Deforestation and associated environmental and population changes have been hypothesized as main drivers of this apparent emergence. We gathered village-level data for P. knowlesi incidence for the districts of Kudat and Kota Marudu in Sabah state, Malaysia, for 2008-2012. We adjusted malaria records from routine reporting systems to reflect the diagnostic uncertainty of microscopy for P. knowlesi. We also developed negative binomial spatial autoregressive models to assess potential associations between P. knowlesi incidence and environmental variables derived from satellite-based remote-sensing data. Marked spatial heterogeneity in P. knowlesi incidence was observed, and village-level numbers of P. knowlesi cases were positively associated with forest cover and historical forest loss in surrounding areas. These results suggest the likelihood that deforestation and associated environmental changes are key drivers in P. knowlesi transmission in these areas.

Matched MeSH terms: Spatial Analysis*
Spatial and statistical trend characteristics of rainfall erosivity (R) in upper catchment of Baram River, Borneo

Vijith H, Dodge-Wan D

Environ Monit Assess, 2019 Jul 13;191(8):494.
PMID: 31302794 DOI: 10.1007/s10661-019-7604-z

The upper catchment region of the Baram River in Sarawak (Malaysian Borneo) is undergoing severe land degradation due to soil erosion. Heavy rainfall with high erosive power has led to a number of soil erosion hotspots. The goal of the present study is to generate an understanding about the spatial characteristics of seasonal and annual rainfall erosivity (R), which not only control sediment delivery from the region but also determine the quantity of material potentially eroded. Mean annual rainfall and rainfall erosivity range from 2170 to 5167 mm and 1632 to 5319 MJ mm ha-1 h-1 year-1, respectively. Seasonal rainfall and rainfall erosivity range from 848 to 1872 mm and 558 to 1883 MJ mm ha-1 h-1 year-1 for the southwest (SW) monsoon, 902 to 2200 mm and 664 to 2793 MJ mm ha-1h-1year-1 for the northeast (NE) monsoon and 400 to 933 mm and 331 to 1075 MJ mm ha-1 h-1 year-1 during the inter-monsoon (IM) period. Linear regression, Spearman's Rho and Mann Kendall tests were applied. Considering the regional mean rainfall erosivity in the study area, all the methods show an overall non-significant decreasing trend (- 9.34, - 0.25 and - 0.30 MJ mm ha-1 h-1 year-1, respectively for linear regression, Spearman's Rho and Mann Kendall tests). However, during SW monsoon and IM periods, rainfall erosivity showed a non-significant decreasing trend (- 25.45, - 0.52, - 0.40, and - 8.86, - 1.07, - 0.77 MJ mm ha-1 h-1 year-1, respectively) whereas in NE, monsoon season erosivity showed a non-significant increasing trend (14.90, 1.59 and 1.60 MJ mm ha-1 h-1 year-1, respectively). The mean erosivity density ranges from 0.77 to 1.38 MJ ha-1 h-1 year-1 and shows decreasing trend. Spatial distribution pattern of erosivity density indicates significantly higher occurrence of erosive rainfall in the lower elevation portion of the study area. The spatial pattern of mean rainfall erosivity trends (linear, Spearman's Rho and Mann Kendall) suggests that the study area can be divided into two zones with increasing rainfall erosivity trends in the northern zone and decreasing trends in the southern zone. These results can be used to plan conservation measures to reduce sediment delivery from localized soil erosion hotspots.

Matched MeSH terms: Spatial Analysis
A practical illustration of spatial smoothing methods for disconnected regions with INLA: spatial survey on overweight and obesity in Malaysia

Mohamad MS, Abdul Maulud KN, Faes C

Int J Health Geogr, 2023 Jun 21;22(1):14.
PMID: 37344913 DOI: 10.1186/s12942-023-00336-5

BACKGROUND: National prevalence could mask subnational heterogeneity in disease occurrence, and disease mapping is an important tool to illustrate the spatial pattern of disease. However, there is limited information on techniques for the specification of conditional autoregressive models in disease mapping involving disconnected regions. This study explores available techniques for producing district-level prevalence estimates for disconnected regions, using as an example childhood overweight in Malaysia, which consists of the Peninsular and Borneo regions separated by the South China Sea. We used data from Malaysia National Health and Morbidity Survey conducted in 2015. We adopted Bayesian hierarchical modelling using the integrated nested Laplace approximation (INLA) program in R-software to model the spatial distribution of overweight among 6301 children aged 5-17 years across 144 districts located in two disconnected regions. We illustrate different types of spatial models for prevalence mapping across disconnected regions, taking into account the survey design and adjusting for district-level demographic and socioeconomic covariates.
RESULTS: The spatial model with split random effects and a common intercept has the lowest Deviance and Watanabe Information Criteria. There was evidence of a spatial pattern in the prevalence of childhood overweight across districts. An increasing trend in smoothed prevalence of overweight was observed when moving from the east to the west of the Peninsular and Borneo regions. The proportion of Bumiputera ethnicity in the district had a significant negative association with childhood overweight: the higher the proportion of Bumiputera ethnicity in the district, the lower the prevalence of childhood overweight.
CONCLUSION: This study illustrates different available techniques for mapping prevalence across districts in disconnected regions using survey data. These techniques can be utilized to produce reliable subnational estimates for any areas that comprise of disconnected regions. Through the example, we learned that the best-fit model was the one that considered the separate variations of the individual regions. We discovered that the occurrence of childhood overweight in Malaysia followed a spatial pattern with an east-west gradient trend, and we identified districts with high prevalence of overweight. This information could help policy makers in making informed decisions for targeted public health interventions in high-risk areas.

Matched MeSH terms: Spatial Analysis
Fulltext Environmental and spatial risk factors for the larval habitats of Plasmodium knowlesi vectors in Sabah, Malaysian Borneo

Byrne I, Aure W, Manin BO, Vythilingam I, Ferguson HM, Drakeley CJ, et al.

Sci Rep, 2021 Jun 03;11(1):11810.
PMID: 34083582 DOI: 10.1038/s41598-021-90893-1

Land-use changes, such as deforestation and agriculture, can influence mosquito vector populations and malaria transmission. These land-use changes have been linked to increased incidence in human cases of the zoonotic malaria Plasmodium knowlesi in Sabah, Malaysian Borneo. This study investigates whether these associations are partially driven by fine-scale land-use changes creating more favourable aquatic breeding habitats for P. knowlesi anopheline vectors. Using aerial remote sensing data, we developed a sampling frame representative of all land use types within a major focus of P. knowlesi transmission. From 2015 to 2016 monthly longitudinal surveys of larval habitats were collected in randomly selected areas stratified by land use type. Additional remote sensing data on environmental variables, land cover and landscape configuration were assembled for the study site. Risk factor analyses were performed over multiple spatial scales to determine associations between environmental and spatial variables and anopheline larval presence. Habitat fragmentation (300 m), aspect (350 m), distance to rubber plantations (100 m) and Culex larval presence were identified as risk factors for Anopheles breeding. Additionally, models were fit to determine the presence of potential larval habitats within the areas surveyed and used to generate a time-series of monthly predictive maps. These results indicate that land-use change and topography influence the suitability of larval habitats, and may partially explain the link between P. knowlesi incidence and deforestation. The predictive maps, and identification of the spatial scales at which risk factors are most influential may aid spatio-temporally targeted vector control interventions.

Matched MeSH terms: Spatial Analysis
Fulltext Self-Adaptive Filtering Approach for Improved Indoor Localization of a Mobile Node with Zigbee-Based RSSI and Odometry

Loganathan A, Ahmad NS, Goh P

Sensors (Basel), 2019 Nov 01;19(21).
PMID: 31683837 DOI: 10.3390/s19214748

This study presents a new technique to improve the indoor localization of a mobile node by utilizing a Zigbee-based received-signal-strength indicator (RSSI) and odometry. As both methods suffer from their own limitations, this work contributes to a novel methodological framework in which coordinates of the mobile node can more accurately be predicted by improving the path-loss propagation model and optimizing the weighting parameter for each localization technique via a convex search. A self-adaptive filtering approach is also proposed which autonomously optimizes the weighting parameter during the target node's translational and rotational motions, thus resulting in an efficient localization scheme with less computational effort. Several real-time experiments consisting of four different trajectories with different number of straight paths and curves were carried out to validate the proposed methods. Both temporal and spatial analyses demonstrate that when odometry data and RSSI values are available, the proposed methods provide significant improvements on localization performance over existing approaches.

Matched MeSH terms: Spatial Analysis
Fulltext Area-Level Determinants in Colorectal Cancer Spatial Clustering Studies: A Systematic Review

Soffian SSS, Nawi AM, Hod R, Chan HK, Hassan MRA

Int J Environ Res Public Health, 2021 10 06;18(19).
PMID: 34639786 DOI: 10.3390/ijerph181910486

The increasing pattern of colorectal cancer (CRC) in specific geographic region, compounded by interaction of multifactorial determinants, showed the tendency to cluster. The review aimed to identify and synthesize available evidence on clustering patterns of CRC incidence, specifically related to the associated determinants. Articles were systematically searched from four databases, Scopus, Web of Science, PubMed, and EBSCOHost. The approach for identification of the final articles follows PRISMA guidelines. Selected full-text articles were published between 2016 and 2021 of English language and spatial studies focusing on CRC cluster identification. Articles of systematic reviews, conference proceedings, book chapters, and reports were excluded. Of the final 12 articles, data on the spatial statistics used and associated factors were extracted. Identified factors linked with CRC cluster were further classified into ecology (health care accessibility, urbanicity, dirty streets, tree coverage), biology (age, sex, ethnicity, overweight and obesity, daily consumption of milk and fruit), and social determinants (median income level, smoking status, health cost, employment status, housing violations, and domestic violence). Future spatial studies that incorporate physical environment related to CRC cluster and the potential interaction between the ecology, biology and social determinants are warranted to provide more insights to the complex mechanism of CRC cluster pattern.

Matched MeSH terms: Spatial Analysis

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