Displaying publications 1 - 20 of 57 in total

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  1. 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
  2. The genetic characteristics and evolution of Tembusu virus
    Lei W, Guo X, Fu S, Feng Y, Tao X, Gao X, et al.
    Vet Microbiol, 2017 Mar;201:32-41.
    PMID: 28284620 DOI: 10.1016/j.vetmic.2017.01.003
    BACKGROUND: Since the turn of the 21st century, there have been several epidemic outbreaks of poultry diseases caused by Tembusu virus (TMUV). Although multiple mosquito and poultry-derived strains of TMUV have been isolated, no data exist about their comparative study, origin, evolution, and dissemination.

    METHODOLOGY: Parallel virology was used to investigate the phenotypes of duck and mosquito-derived isolates of TMUV. Molecular biology and bioinformatics methods were employed to investigate the genetic characteristics and evolution of TMUV.

    PRINCIPAL FINDINGS: The plaque diameter of duck-derived isolates of TMUV was larger than that of mosquito-derived isolates. The cytopathic effect (CPE) in mammalian cells occurred more rapidly induced by duck-derived isolates than by mosquito-derived isolates. Furthermore, duck-derived isolates required less time to reach maximum titer, and exhibited higher viral titer. These findings suggested that poultry-derived TMUV isolates were more invasive and had greater expansion capability than the mosquito-derived isolates in mammalian cells. Variations in amino acid loci in TMUV E gene sequence revealed two mutated amino acid loci in strains isolated from Malaysia, Thailand, and Chinese mainland compared with the prototypical strain of the virus (MM1775). Furthermore, TMUV isolates from the Chinese mainland had six common variations in the E gene loci that differed from the Southeast Asian strains. Phylogenetic analysis indicated that TMUV did not exhibit a species barrier in avian species and consisted of two lineages: the Southeast Asian and the Chinese mainland lineages. Molecular traceability studies revealed that the recent common evolutionary ancestor of TMUV might have appeared before 1934 and that Malaysia, Thailand and Shandong Province of China represent the three main sources related to TMUV spread.

    CONCLUSIONS: The current broad distribution of TMUV strains in Southeast Asia and Chinese mainland exhibited longer-range diffusion and larger-scale propagation. Therefore, in addition to China, other Asian and European countries linked to Asia have used improved measures to detect and monitor TMUV related diseases to prevent epidemics in poultry.

    Matched MeSH terms: Spatial Analysis
  3. Measurement of dengue epidemic spreading pattern using density analysis method: retrospective spatial statistical study of dengue in Subang Jaya, Malaysia, 2006-2010
    Dom NC, Ahmad AH, Latif ZA, Ismail R
    Trans R Soc Trop Med Hyg, 2013 Nov;107(11):715-22.
    PMID: 24062522 DOI: 10.1093/trstmh/trt073
    Dengue has emerged as one of the major public health problems in Malaysia. The Ministry of Health, Malaysia, is committed in monitoring and controlling this disease for many years. The objective of this study is to analyze the dengue outbreak pattern on a monthly basis in Subang Jaya in terms of their spatial dissemination and hotspot identification.
    Matched MeSH terms: Spatial Analysis
  4. 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
  5. 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
  6. Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer
    Page DB, Broeckx G, Jahangir CA, Verbandt S, Gupta RR, Thagaard J, et al.
    J Pathol, 2023 Aug;260(5):514-532.
    PMID: 37608771 DOI: 10.1002/path.6165
    Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.
    Matched MeSH terms: Spatial Analysis
  7. Fulltext Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees
    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*
  8. 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
  9. Fulltext D-Shaped Polarization Maintaining Fiber Sensor for Strain and Temperature Monitoring
    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
  10. 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
  11. 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
  12. Fulltext Applying the method of Lagrange Multipliers to derive an estimator for unsampled soil properties
    Foong, Ng Set, Eng, Ch’ng Pei, Ming, Chew Yee, Shien, Ng Kok
    Scientific Research Journal, 2014;11(1):0-0.
    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
  13. 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
  14. Spatial analysis of environmental factors influencing typhoid endemicity in Kelantan, Malaysia
    Shamsul Azhar Shah, Suzuki H, Mohd Rohaizat Hassan, Saito R, Nazarudin Safian, Shaharudin Idrus
    Sains Malaysiana, 2012;41:911-919.
    The determination of the high-risk area and clusters of typhoid cases is critical in typhoid control. The purpose of this study was to identify and describe the epidemiology and spatial distribution of typhoid in four selected districts in Kelantan using GIS (geographical information system). A total of 1215 (99%) of the cases were coordinated with GPS (global positioning system) and mapping was done using ArcGIS 9.2. Spatial analysis was performed to determine the cluster and high-risk area of typhoid. Results showed that typhoid incidence was not associated with race and sex. Most affected were from the age group of 5-14 followed by 15-24 year olds. Nine sub-districts were categorized as highly endemic. In addition typhoid has shown a significant tendency to cluster and a total of 22 hotspots were found in Kota Bharu, Bachok and Tumpat with a few sub districts identified as high risk for typhoid. No significant relationships between the treated water ratio and flood risk area were found with the cluster of cases. The cluster of typhoid cases in the endemic area did not appear to be related to environmental risk factors. Understanding the characteristics of these clusters would enable the prevention of typhoid disease in the future.
    Matched MeSH terms: Spatial Analysis
  15. 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
  16. Fulltext Predictive analysis across spatial scales links zoonotic malaria to deforestation
    Brock PM, Fornace KM, Grigg MJ, Anstey NM, William T, Cox J, et al.
    Proc Biol Sci, 2019 Jan 16;286(1894):20182351.
    PMID: 30963872 DOI: 10.1098/rspb.2018.2351
    The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case-control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. By contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.
    Matched MeSH terms: Spatial Analysis
  17. Fulltext Polycyclic aromatic hydrocarbons in coastal sediment of klang strait, Malaysia: distribution pattern, risk assessment and sources
    Tavakoly Sany SB, Hashim R, Salleh A, Rezayi M, Mehdinia A, Safari O
    PLoS One, 2014;9(4):e94907.
    PMID: 24747349 DOI: 10.1371/journal.pone.0094907
    Concentration, source, and ecological risk of polycyclic aromatic hydrocarbons (PAHs) were investigated in 22 stations from surface sediments in the areas of anthropogenic pollution in the Klang Strait (Malaysia). The total PAH level in the Klang Strait sediment was 994.02±918.1 µg/kg dw. The highest concentration was observed in stations near the coastline and mouth of the Klang River. These locations were dominated by high molecular weight PAHs. The results showed both pyrogenic and petrogenic sources are main sources of PAHs. Further analyses indicated that PAHs primarily originated from pyrogenic sources (coal combustion and vehicular emissions), with significant contribution from petroleum inputs. Regarding ecological risk estimation, only station 13 was moderately polluted, the rest of the stations suffered rare or slight adverse biological effects with PAH exposure in surface sediment, suggesting that PAHs are not considered as contaminants of concern in the Klang Strait.
    Matched MeSH terms: Spatial Analysis
  18. Fulltext Using GIS and stakeholder involvement to innovate marine mammal bycatch risk assessment in data-limited fisheries
    Verutes GM, Johnson AF, Caillat M, Ponnampalam LS, Peter C, Vu L, et al.
    PLoS One, 2020;15(8):e0237835.
    PMID: 32817725 DOI: 10.1371/journal.pone.0237835
    Fisheries bycatch has been identified as the greatest threat to marine mammals worldwide. Characterizing the impacts of bycatch on marine mammals is challenging because it is difficult to both observe and quantify, particularly in small-scale fisheries where data on fishing effort and marine mammal abundance and distribution are often limited. The lack of risk frameworks that can integrate and visualize existing data have hindered the ability to describe and quantify bycatch risk. Here, we describe the design of a new geographic information systems tool built specifically for the analysis of bycatch in small-scale fisheries, called Bycatch Risk Assessment (ByRA). Using marine mammals in Malaysia and Vietnam as a test case, we applied ByRA to assess the risks posed to Irrawaddy dolphins (Orcaella brevirostris) and dugongs (Dugong dugon) by five small-scale fishing gear types (hook and line, nets, longlines, pots and traps, and trawls). ByRA leverages existing data on animal distributions, fisheries effort, and estimates of interaction rates by combining expert knowledge and spatial analyses of existing data to visualize and characterize bycatch risk. By identifying areas of bycatch concern while accounting for uncertainty using graphics, maps and summary tables, we demonstrate the importance of integrating available geospatial data in an accessible format that taps into local knowledge and can be corroborated by and communicated to stakeholders of data-limited fisheries. Our methodological approach aims to meet a critical need of fisheries managers: to identify emergent interaction patterns between fishing gears and marine mammals and support the development of management actions that can lead to sustainable fisheries and mitigate bycatch risk for species of conservation concern.
    Matched MeSH terms: Spatial Analysis
  19. Fulltext Seagrass habitat suitability model for Redang Marine Park using multibeam echosounder data: Testing different spatial resolutions and analysis window sizes
    Muhamad MAH, Che Hasan R, Md Said N, Ooi JL
    PLoS One, 2021;16(9):e0257761.
    PMID: 34555110 DOI: 10.1371/journal.pone.0257761
    Integrating Multibeam Echosounder (MBES) data (bathymetry and backscatter) and underwater video technology allows scientists to study marine habitats. However, use of such data in modeling suitable seagrass habitats in Malaysian coastal waters is still limited. This study tested multiple spatial resolutions (1 and 50 m) and analysis window sizes (3 × 3, 9 × 9, and 21 × 21 cells) probably suitable for seagrass-habitat relationships in Redang Marine Park, Terengganu, Malaysia. A maximum entropy algorithm was applied, using 12 bathymetric and backscatter predictors to develop a total of 6 seagrass habitat suitability models. The results indicated that both fine and coarse spatial resolution datasets could produce models with high accuracy (>90%). However, the models derived from the coarser resolution dataset displayed inconsistent habitat suitability maps for different analysis window sizes. In contrast, habitat models derived from the fine resolution dataset exhibited similar habitat distribution patterns for three different analysis window sizes. Bathymetry was found to be the most influential predictor in all the models. The backscatter predictors, such as angular range analysis inversion parameters (characterization and grain size), gray-level co-occurrence texture predictors, and backscatter intensity levels, were more important for coarse resolution models. Areas of highest habitat suitability for seagrass were predicted to be in shallower (<20 m) waters and scattered between fringing reefs (east to south). Some fragmented, highly suitable habitats were also identified in the shallower (<20 m) areas in the northwest of the prediction models and scattered between fringing reefs. This study highlighted the importance of investigating the suitable spatial resolution and analysis window size of predictors from MBES for modeling suitable seagrass habitats. The findings provide important insight on the use of remote acoustic sonar data to study and map seagrass distribution in Malaysia coastal water.
    Matched MeSH terms: Spatial Analysis
  20. 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
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