Displaying publications 1 - 20 of 54 in total

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  1. 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
  2. Ebrahim Jahanshiri, Taher Buyong, Abdul Rashid Mohd. Shariff
    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
  3. Foong, Ng Set, Eng, Ch’ng Pei, Ming, Chew Yee, Shien, Ng Kok
    MyJurnal
    Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimized. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.
    Matched MeSH terms: Spatial Analysis
  4. 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*
  5. Heino J, Melo AS, Bini LM, Altermatt F, Al-Shami SA, Angeler DG, et al.
    Ecol Evol, 2015 Mar;5(6):1235-48.
    PMID: 25859329 DOI: 10.1002/ece3.1439
    The hypotheses that beta diversity should increase with decreasing latitude and increase with spatial extent of a region have rarely been tested based on a comparative analysis of multiple datasets, and no such study has focused on stream insects. We first assessed how well variability in beta diversity of stream insect metacommunities is predicted by insect group, latitude, spatial extent, altitudinal range, and dataset properties across multiple drainage basins throughout the world. Second, we assessed the relative roles of environmental and spatial factors in driving variation in assemblage composition within each drainage basin. Our analyses were based on a dataset of 95 stream insect metacommunities from 31 drainage basins distributed around the world. We used dissimilarity-based indices to quantify beta diversity for each metacommunity and, subsequently, regressed beta diversity on insect group, latitude, spatial extent, altitudinal range, and dataset properties (e.g., number of sites and percentage of presences). Within each metacommunity, we used a combination of spatial eigenfunction analyses and partial redundancy analysis to partition variation in assemblage structure into environmental, shared, spatial, and unexplained fractions. We found that dataset properties were more important predictors of beta diversity than ecological and geographical factors across multiple drainage basins. In the within-basin analyses, environmental and spatial variables were generally poor predictors of variation in assemblage composition. Our results revealed deviation from general biodiversity patterns because beta diversity did not show the expected decreasing trend with latitude. Our results also call for reconsideration of just how predictable stream assemblages are along ecological gradients, with implications for environmental assessment and conservation decisions. Our findings may also be applicable to other dynamic systems where predictability is low.
    Matched MeSH terms: Spatial Analysis
  6. Isahak A, Reza MIH, Siwar C, Ismail SM, Sulaiman N, Hanafi Z, et al.
    Jamba, 2018;10(1):501.
    PMID: 29955268 DOI: 10.4102/jamba.v10i1.501
    Shelter centres are important locations to safeguard people from helpless situations and are an integral part of disaster risk reduction (DRR), particularly for flood DRR. The establishment of shelter centres, and their design based on scientific assessment, is crucial. Yet, they are very much related to the geographic location, socio-economic conditions and the livelihoods of the affected communities. However, many parts of the developing world are still lagging behind in ensuring such scientific design. Considering the flood disaster in 2014 that affected the residents living along the Pahang River Basin, in this study we delineate the communities at risk and evaluate the existing shelter centres to determine how they reduce people's vulnerability to the risks associated with rural and urban landscapes. We used spatial analysis tools to delineate risk zones and to evaluate existing evacuation systems. A flood disaster risk map was produced to determine which communities are living with risks. Subsequently, the distribution of shelter centres examined whether they are able to support people living at the flood risk zones. These centres were also evaluated using a set of international guidelines for effective disaster shelters. This reveals that the number of shelter centres is not adequate. The designation and designing of shelter centres are not being done scientifically. The maps produced here have a lot of potential to support disaster management decisions, in particular site selection and the prioritisation of centres. The study concludes with a set of guidelines and recommendations for structural and non-structural measures, such as alternative livelihoods and the potential of ecotourism, which may improve the resilience among flood-affected communities; and the decision-making process for the overall flood DRR initiatives.
    Matched MeSH terms: Spatial Analysis
  7. Qazi HH, Mohammad AB, Ahmad H, Zulkifli MZ
    Sensors (Basel), 2016 Sep 15;16(9).
    PMID: 27649195 DOI: 10.3390/s16091505
    A D-shaped polarization-maintaining fiber (PMF) as fiber optic sensor for the simultaneous monitoring of strain and the surrounding temperature is presented. A mechanical end and edge polishing system with aluminum oxide polishing film is utilized to perform sequential polishing on one side (lengthwise) of the PMF in order to fabricate a D-shaped cross-section. Experimental results show that the proposed sensor has high sensitivity of 46 pm/µε and 130 pm/°C for strain and temperature, respectively, which is significantly higher than other recently reported work (mainly from 2013) related to fiber optic sensors. The easy fabrication method, high sensitivity, and good linearity make this sensing device applicable in various applications such as health monitoring and spatial analysis of engineering structures.
    Matched MeSH terms: Spatial Analysis
  8. Alyousifi Y, Ibrahim K, Kang W, Zin WZW
    Environ Monit Assess, 2020 Oct 21;192(11):719.
    PMID: 33083907 DOI: 10.1007/s10661-020-08666-8
    An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.
    Matched MeSH terms: Spatial Analysis
  9. 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
  10. Ullah S, Mohd Nor NH, Daud H, Zainuddin N, Gandapur MSJ, Ali I, et al.
    Geospat Health, 2021 05 05;16(1).
    PMID: 33969966 DOI: 10.4081/gh.2021.961
    Coronavirus disease 2019 (COVID-19) is the current worldwide pandemic as declared by the World Health Organization (WHO) in March 2020. Being part of the ongoing global pandemic, Malaysia has recorded a total of 8639 COVID-19 cases and 121 deaths as of 30th June 2020. This study aims to detect spatial clusters of COVID-19 in Malaysia using the Spatial Scan Statistic (SaTScan™) to guide control authorities on prioritizing locations for targeted interventions. The spatial analyses were conducted on a monthly basis at the state-level from March to September 2020. The results show that the most likely cluster of COVID-19 occurred in West Malaysia repeatedly from March to June, covering three counties (two federal territories and one neighbouring state) and moved to East Malaysia in July covering two other counties. The most likely cluster shows a tendency of having moved from the western part to the eastern part of the country. These results provide information that can be used for the evidence- based interventions to control the spread of COVID-19 in Malaysia.
    Matched MeSH terms: Spatial Analysis
  11. Zainol NFM, Zainuddin AH, Looi LJ, Aris AZ, Isa NM, Sefie A, et al.
    PMID: 34071804 DOI: 10.3390/ijerph18115733
    Rapid urbanization and industrial development in the Langat Basin has disturbed the groundwater quality. The populations' reliance on groundwater sources may induce possible risks to human health such as cancer and endocrine dysfunction. This study aims to determine the groundwater quality of an urbanized basin through 24 studied hydrochemical parameters from 45 groundwater samples obtained from 15 different sampling stations by employing integrated multivariate analysis. The abundance of the major ions was in the following order: bicarbonate (HCO3-) > chloride (Cl-) > sodium (Na+) > sulphate (SO42-) > calcium (Ca2+) > potassium (K+) > magnesium (Mg2+). Heavy metal dominance was in the following order: Fe > Mn > Zn > As > Hg > Pb > Ni > Cu > Cd > Se > Sr. Classification of the groundwater facies indicated that the studied groundwater belongs to the Na-Cl with saline water type and Na-HCO3 with mix water type characteristics. The saline water type characteristics are derived from agricultural activities, while the mixed water types occur from water-rock interaction. Multivariate analysis performance suggests that industrial, agricultural, and weathering activities have contributed to groundwater contamination. The study will help in the understanding of the groundwater quality issue and serve as a reference for other basins with similar characteristics.
    Matched MeSH terms: Spatial Analysis
  12. Soffian SSS, Nawi AM, Hod R, Chan HK, Hassan MRA
    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
  13. 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
  14. Masrani AS, Nik Husain NR, Musa KI, Yasin AS
    J Prev Med Public Health, 2022 Jan;55(1):80-87.
    PMID: 35135051 DOI: 10.3961/jpmph.21.461
    OBJECTIVES: Dengue remains hyperendemic in Malaysia despite extensive vector control activities. With dynamic changes in land use, urbanisation and population movement, periodic updates on dengue transmission patterns are crucial to ensure the implementation of effective control strategies. We sought to assess shifts in the trends and spatial patterns of dengue in Kelantan, a north-eastern state of Malaysia (5°15'N 102°0'E).

    METHODS: This study incorporated data from the national dengue monitoring system (eDengue system). Confirmed dengue cases registered in Kelantan with disease onset between January 1, 2016 and December 31, 2018 were included in the study. Yearly changes in dengue incidence were mapped by using ArcGIS. Hotspot analysis was performed using Getis-Ord Gi to track changes in the trends of dengue spatial clustering.

    RESULTS: A total of 10 645 dengue cases were recorded in Kelantan between 2016 and 2018, with an average of 10 dengue cases reported daily (standard deviation, 11.02). Areas with persistently high dengue incidence were seen mainly in the coastal region for the 3-year period. However, the hotspots shifted over time with a gradual dispersion of hotspots to their adjacent districts.

    CONCLUSIONS: A notable shift in the spatial patterns of dengue was observed. We were able to glimpse the shift of dengue from an urban to peri-urban disease with the possible effect of a state-wide population movement that affects dengue transmission.

    Matched MeSH terms: Spatial Analysis
  15. Cheong YL, Leitão PJ, Lakes T
    Spat Spatiotemporal Epidemiol, 2014 Jul;10:75-84.
    PMID: 25113593 DOI: 10.1016/j.sste.2014.05.002
    The transmission of dengue disease is influenced by complex interactions among vector, host and virus. Land use such as water bodies or certain agricultural practices have been identified as likely risk factors for dengue because of the provision of suitable habitats for the vector. Many studies have focused on the land use factors of dengue vector abundance in small areas but have not yet studied the relationship between land use factors and dengue cases for large regions. This study aims to clarify if land use factors other than human settlements, e.g. different types of agricultural land use, water bodies and forest are associated with reported dengue cases from 2008 to 2010 in the state of Selangor, Malaysia. From the correlative relationship, we aim to generate a prediction risk map. We used Boosted Regression Trees (BRT) to account for nonlinearities and interactions between the factors with high predictive accuracies. Our model with a cross-validated performance score (Area Under the Receiver Operator Characteristic Curve, ROC AUC) of 0.81 showed that the most important land use factors are human settlements (model importance of 39.2%), followed by water bodies (16.1%), mixed horticulture (8.7%), open land (7.5%) and neglected grassland (6.7%). A risk map after 100 model runs with a cross-validated ROC AUC mean of 0.81 (±0.001 s.d.) is presented. Our findings may be an important asset for improving surveillance and control interventions for dengue.
    Matched MeSH terms: Spatial Analysis*
  16. 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
  17. Asra Hosseini
    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
  18. Syerrina Zakaria, Nuzlinda Abdul Rahman
    MyJurnal
    The objective of this study was to explore the geographic distribution and temporal patterns of violent crime cases in Peninsular Malaysia by using the tools and techniques for spatial analysis. This study will also provide a general picture of violent crime patterns in Malaysia. The unit of analysis is district and the violent crime data from the year 2000 until 2009 were used in this study. In order to obtain the optimum number of components of crime in the space-time period, the space-time Normal Mixture Models were used. Based on the results of this model, the mapping of the crime occurrences was made. This map displays the spatial distribution of crime occurrence in 82 districts of Peninsular Malaysia. From this analysis, more violent crimes were shown to have occurred in developed states such as Selangor, Wilayah Persekutuan Kuala Lumpur and Johor. The findings of this study could be used by policy makers or responsible agencies to take any relevant actions in terms of crime prevention, human resource allocation and law enforcement so as to overcome this important issue in future.
    Matched MeSH terms: Spatial Analysis
  19. Abd Majid N, Rainis R, Sahani M, Mohamed AF, Abdul Ghani Aziz SA, Muhamad Nazi N
    Geospat Health, 2021 03 11;16(1).
    PMID: 33706498 DOI: 10.4081/gh.2021.915
    In recent decades, dengue outbreaks have become increasingly common around the developing countries, including Malaysia. Thus, it is essential for rural as well as urbanised livelihood to understand the distribution pattern of this infection. The objective of this study is to determine the trend of dengue cases reported from the year 2014 to 2018 and the spatial pattern for this spread. Spatial statistical analyses conducted found that the distribution pattern and spatial mean centre for dengue cases were clustered in the eastern part of the Bangi region. Directional distribution observed that the elongated polygon of dengue cluster stretched from the Northeast to the Southwest of Bangi District. The standard distance observed for dengue cases was smallest in the year 2014 (0.017 m), and largest in 2016 (0.019 m), whereas in the year 2015, 2017 and 2018, it measured 0.018 m. The average nearest neighbour analysis also displayed clustered patterns for dengue cases in the Bangi District. The three spatial statistical analyses (spatial mean centre, standard distance and directional distribution) findings illustrate that the dengue cases from the year 2014 to 2018 are clustered in the Northeast to the Southwest of the study region.
    Matched MeSH terms: Spatial Analysis
  20. Abu Bakar MA, Samat N, Yaacob NS
    Geospat Health, 2021 10 19;16(2).
    PMID: 34672180 DOI: 10.4081/gh.2021.987
    Cerebral palsy (CP) is one of the most common causes of disability in childhood, leading to functional limitations and poor nutritional status. Families with CP children face challenges in providing proper care. Thus, accessibility of CP patients to health facilities is important to ensure that they can maintain regular visits to health facilities for proper treatment and care. The current study aimed to map the spatial distribution of CP in Johor, Malaysia and measure the accessibility of CP patients to nearby hospitals, health clinics and community-based rehabilitation centres. The study is based on CP cases in 2017 obtained from the Department of Social Welfare, Malaysia and analysed using the average nearest neighbour, buffer analysis and Kernel Density Estimation. Results indicate that there is generally good access to health care services for many of the CP children in Johor, but for 25% of those living more than 10 km away from the health clinics or community-based rehabilitation centres, regular visits can be a problem. This information should be used for targeted intervention and planning for health care strategies. Furthermore, information on hospital accessibility of CP children would allow for planning of proper and regular treatment for these patients. The study has shown that it is possible to improve the understanding of the distribution of CP cases by integrating spatial analysis using geographical information systems without relying on official information about the density of populations.
    Matched MeSH terms: Spatial Analysis
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