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1860797422333067264
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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2015-12-30 15:51:38
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Restricted Document
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12653
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UniSZA
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1. Neuroscience of psychoactive substance use and dependence. http:// www.who.int /substance _abuse / publications/en/Neuroscience. Accessed on 18 Disember 2014. 2. Sayed Mohamed S. M. A. A., Mohamad, Z. Ismail, B. and Yusof, R. A. R. M. (2013). Therapeutic Experience of drug rehabilitation clients through expressive arts therapy. International Journal of Humanities and Social Science 3 (17):210-223. 3. Kulsudjarit, K. (2004). Drug problem in Southeast and South West Asia. New York Academy of Science 1025:446-457. 4. Huong, A. G. W., Guan, N. C., Nordin, A. S. A., Adlan, A. S. A. and Habil, H. (2009). Quality of life assessment of opioid substance abusers on methadone maintainance therapy (MMT) in University Malaya medical centre. Journal of Psychiatry 10 (1): 1-11. 5. Devi, J. P., Azriani, A. B., Mohd, Z. W., Ariff, M. N. M. and Hashimah, A. N. (2012). The effectiveness of methadone maintainance therapy among opiate depandents registered with Hospital Raja Perempuan Zainab II Kota Bharu Kelantan. Malay Journal Medical Sciences 19 (4): 17-22. 6. Yusoff, F., Shril, N., Rasidi, N. M., Zaki, N. A. M., Muhamad, N. and Ahmad, N. (2014). Illicit Drug Use Among Svhool-Going Adolescents in Malaysia. Asia-Pacific Journal of Public Health 26(55): 100S-107S. 7. National Antidrug Agency, (2013). Laporan dadah bulan disember 2013. National Antidrug Agency. 8. Hao, S-H., Zhao, M., Zhang, R-W., Zhang, J-C., Zhang, J. and Feng, X-S. (2013). The effectiveness comparison of Jitai tablets versus methadone in community-based drug treatment: A 1-year follow up study. Addictive Behaviours 38: 2596-2600. 9. Sainders, B. and Allops, S. (1987). Relapse a psychological perspective. British Journal of Addiction 82: 417- 429. 10. Selected Social Statistics. Series 12/2010. (2010). National Antidrug Agency. 11. Daash, A., Srivastava, A., Nagpal, B.N., Saxena, R. and Gupta, S.K. (2009). Geographical information system (GIS) in decisión support to control malaria- a case study of Koraput district in Orissa. India Journal Vector Borne Diseases 46 (1):72-74. 12. Hidalgo, B., & Goodman, M. (2013). Multivariate or multivariable regression? American Journal of Public Health 103(1): 39–40. 13. D’Ovidio, F. D., Leogrande, D., Mancarella, R., Schinzano, A. and Viola, D. (2014). A multivariate analysis of the quality of public transport services. Procedia Economics and Finance 17: 238-247 14. Petronis, K.R., Johnson, C.C. and Wish, E.D. (1995). Location of drug-using arrested and treatment centres in Washington D.C.: A geocoding demonstration Project.University of Maryland at College Park. Washington, D.C. 15. Johnson, C.P. and Johnson, J. (2001). GIS: A tool for monitoring and management of epidemics. Map India Conference. New Delhi. 16. Blanton, J.D, Manangan, A.,Manangan, J., Hanlon, C.A., State, D. and Rupprect, C.E. (2006). Development of a GIS-based, real-time Internet mapping tolos for rabies surveillance. International Journal of Health Geographics 5:47-55. 17. Srivastava, A., Nagpal, B. N., Srivastava, A., Gupta, S. K. and Dash, A.P. (2009). Identification of malaria hot spots for focused intervention in tribal state of India: a GIS based appoach. International Journal Health Geographics, 8:30-38. 18. Sanders, L. J., Aguilar G. D. and Bacon C. J. (2013). A spatial analysis of the geographic distribution of musculoskeletal and general practice healthcare clinics in Auckland, New Zealand. Applied Geography 44: 60- 78. 19. Carroll, L. N., Au, A. P., Detwiler, L.T., Fu, T-C, Painter, I. S. and Albernethy, N. F. (2014). Visualization and analytics tolos for infectious disease epidemiology: A systematic review. Journal of Biomedical Informatics. 51:287-298. 20. Kwan, M-P. (2000). Analysis of human spatial behavior in a GIS environment: Recent developments and future prospects. Journal of Geographical Systems 2: 85-90. 21. Elebead, F.M., Hamid, A., Hilmi, H. S. M. and Galal, H. (2012). Mapping cáncer disease using geographical information system (GIS) in Gezira state-sudan. Journal Community Health 37: 830-839. 22. Rasidi, M. N. M., Sahani, M., Othman, H., Hod, R., Idrus, S., Ali, M. Z., Choy E. A. and Rosli. M. H. (2013). Aplikasi sistem maklumat geografi untuk pemetaan reruang masa suatu kajian kes denggi di daerah Seremban, Negeri Sembilan, Malaysia. Sains Malaysiana 42 (8):1073-1080. 23. Brownstein, J. S., Green T. C., Cassidy T. A. and Buttler, S. F. (2010). Geographic Information Systems and Pharmacoepidemiology, usng spatial cluster detection to monitor local patterns of prescription opioid abuse. Pharmacoepidemiol Drug Safety 19:627-637. 24. Mitchell, A. (2005). The ESRI guide to GIS analysis: Spatial measurements & statistics. V 2. Redlands, California: ESRI Press. 25. Kannel, P.R., Lee, S., Kanel, S.R. and Khan, S.P. (2007). Chemometric application in classification and assessment of monitoring locations of urban river system. Analytica. Chim Acta 582: 390-399. 26. Official Portal Terengganu State.(2015). Terengganu http: //jheatweb. terengganu. gov. my/ maxc2020 /agensi/article2.php?sectionid=1&cid=1&aid=6997. Accessed on 21 December 2014. 27. Population and Housing Census Report, Population and Housing Census of Malaysia (2014) Oficial Website.https://www.statistics.gov.my/mycensus2010/index.php?option=com_content&view=frontpage&Itemi d=1&lang=en. Accessed on 21 Dicember 2014. 28. Imam, E. (2011). Use of geospatial technology in evaluating landscape cover type changes in Chandoli National Park, India. Computational Ecology and Software 1 (2): 95-111. 29. Shrestha, S. and Kazama, F. (2007). Assessment of Surface wáter quality Using Multivariated Statistical Techniques: a case study of the Fuji River Basin, Japan. Environment Modelling Software 22 (4): 464-475. 30. Sulong, I. and Ismail, A. (2002). Mangrove mapping using landsat imagery and aerial photographs : kemaman district . Environment, Development and Sustainability 4: 135–152. 31. Yusoff, F., Sahril, N., Rasidi, N. M., Zaki, N. A. M., Muhamad, N., and Ahmad, N. (2014). Illicit Drug Use among School-Going Adolescents in Malaysia. Asia-Pacific Journal of Public Health 26(5 Suppl): 100S–107S. 32. Colasante, E., Molinaro, S. and Mariani, F. (2008). Italian of public health spatial analysis of drug-related hospital admissions : an auto-Gaussian model to estimate the hospitalization rates in Italy. Italian Journal of Public Health 5(4): 253–260. 33. National Antidrug Agency, (2010). Laporan Dadah Bulan Disember 2010. National Antidrug Agency
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12653 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12653 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 norman 1411 762 71 71 2015-12-30 15:51:38 1411x762 6960-01-FH02-ESERI-15-04693.jpg UniSZA Private Access Spatial and temporal assessment on drug addiction using multivariate analysis and GIS [Penilaian ruang dan masa terhadap penagihan dadah menggunakan analisis multivariat dan GIS] Malaysian Journal of Analytical Sciences There is a need for managing and displaying drug addiction phenomena and trend at both spatial and temporal scales. Spatial and temporal assessment on drug addiction in Terengganu was undertaken to understand the geographical area of district in the same cluster, in addition, identify the hot spot area of this problem and analysis the trend of drug addiction. Data used were topography map of Terengganu and number of drug addicted person in Terengganu by district within 10 years (2004-2013). Number of drug addicted person by district were mapped using Geographic Information system and analysed using a combination of multivariate analysis which is cluster analysis were applied to the database in order to validate the correlation between data in the same cluster. Result showed a cluster analysis for number of drug addiction by district generated three clusters which are Besut and Kuala Terengganu in cluster 1 named moderate drug addicted person (MDA), Dungun, Marang, Setiu and Hulu Terengganu in cluster 2 named lower drug addicted person (LDA) and Kemaman in cluster 3 named high drug addicted person(HDA). This analysis indicates that cluster 3 which is Kemaman is a hot spot area. These results were beneficial for stakeholder to monitor and manage this problem especially in the hot spot area which needs to be emphasized. 19 6 Malaysian Society of Analytical Sciences Malaysian Society of Analytical Sciences 1361-1373 1. Neuroscience of psychoactive substance use and dependence. http:// www.who.int /substance _abuse / publications/en/Neuroscience. Accessed on 18 Disember 2014. 2. Sayed Mohamed S. M. A. A., Mohamad, Z. Ismail, B. and Yusof, R. A. R. M. (2013). Therapeutic Experience of drug rehabilitation clients through expressive arts therapy. International Journal of Humanities and Social Science 3 (17):210-223. 3. Kulsudjarit, K. (2004). Drug problem in Southeast and South West Asia. New York Academy of Science 1025:446-457. 4. Huong, A. G. W., Guan, N. C., Nordin, A. S. A., Adlan, A. S. A. and Habil, H. (2009). Quality of life assessment of opioid substance abusers on methadone maintainance therapy (MMT) in University Malaya medical centre. Journal of Psychiatry 10 (1): 1-11. 5. Devi, J. P., Azriani, A. B., Mohd, Z. W., Ariff, M. N. M. and Hashimah, A. N. (2012). The effectiveness of methadone maintainance therapy among opiate depandents registered with Hospital Raja Perempuan Zainab II Kota Bharu Kelantan. Malay Journal Medical Sciences 19 (4): 17-22. 6. Yusoff, F., Shril, N., Rasidi, N. M., Zaki, N. A. M., Muhamad, N. and Ahmad, N. (2014). Illicit Drug Use Among Svhool-Going Adolescents in Malaysia. Asia-Pacific Journal of Public Health 26(55): 100S-107S. 7. National Antidrug Agency, (2013). Laporan dadah bulan disember 2013. National Antidrug Agency. 8. Hao, S-H., Zhao, M., Zhang, R-W., Zhang, J-C., Zhang, J. and Feng, X-S. (2013). The effectiveness comparison of Jitai tablets versus methadone in community-based drug treatment: A 1-year follow up study. Addictive Behaviours 38: 2596-2600. 9. Sainders, B. and Allops, S. (1987). Relapse a psychological perspective. British Journal of Addiction 82: 417- 429. 10. Selected Social Statistics. Series 12/2010. (2010). National Antidrug Agency. 11. Daash, A., Srivastava, A., Nagpal, B.N., Saxena, R. and Gupta, S.K. (2009). Geographical information system (GIS) in decisión support to control malaria- a case study of Koraput district in Orissa. India Journal Vector Borne Diseases 46 (1):72-74. 12. Hidalgo, B., & Goodman, M. (2013). Multivariate or multivariable regression? American Journal of Public Health 103(1): 39–40. 13. D’Ovidio, F. D., Leogrande, D., Mancarella, R., Schinzano, A. and Viola, D. (2014). A multivariate analysis of the quality of public transport services. Procedia Economics and Finance 17: 238-247 14. Petronis, K.R., Johnson, C.C. and Wish, E.D. (1995). Location of drug-using arrested and treatment centres in Washington D.C.: A geocoding demonstration Project.University of Maryland at College Park. Washington, D.C. 15. Johnson, C.P. and Johnson, J. (2001). GIS: A tool for monitoring and management of epidemics. Map India Conference. New Delhi. 16. Blanton, J.D, Manangan, A.,Manangan, J., Hanlon, C.A., State, D. and Rupprect, C.E. (2006). Development of a GIS-based, real-time Internet mapping tolos for rabies surveillance. International Journal of Health Geographics 5:47-55. 17. Srivastava, A., Nagpal, B. N., Srivastava, A., Gupta, S. K. and Dash, A.P. (2009). Identification of malaria hot spots for focused intervention in tribal state of India: a GIS based appoach. International Journal Health Geographics, 8:30-38. 18. Sanders, L. J., Aguilar G. D. and Bacon C. J. (2013). A spatial analysis of the geographic distribution of musculoskeletal and general practice healthcare clinics in Auckland, New Zealand. Applied Geography 44: 60- 78. 19. Carroll, L. N., Au, A. P., Detwiler, L.T., Fu, T-C, Painter, I. S. and Albernethy, N. F. (2014). Visualization and analytics tolos for infectious disease epidemiology: A systematic review. Journal of Biomedical Informatics. 51:287-298. 20. Kwan, M-P. (2000). Analysis of human spatial behavior in a GIS environment: Recent developments and future prospects. Journal of Geographical Systems 2: 85-90. 21. Elebead, F.M., Hamid, A., Hilmi, H. S. M. and Galal, H. (2012). Mapping cáncer disease using geographical information system (GIS) in Gezira state-sudan. Journal Community Health 37: 830-839. 22. Rasidi, M. N. M., Sahani, M., Othman, H., Hod, R., Idrus, S., Ali, M. Z., Choy E. A. and Rosli. M. H. (2013). Aplikasi sistem maklumat geografi untuk pemetaan reruang masa suatu kajian kes denggi di daerah Seremban, Negeri Sembilan, Malaysia. Sains Malaysiana 42 (8):1073-1080. 23. Brownstein, J. S., Green T. C., Cassidy T. A. and Buttler, S. F. (2010). Geographic Information Systems and Pharmacoepidemiology, usng spatial cluster detection to monitor local patterns of prescription opioid abuse. Pharmacoepidemiol Drug Safety 19:627-637. 24. Mitchell, A. (2005). The ESRI guide to GIS analysis: Spatial measurements & statistics. V 2. Redlands, California: ESRI Press. 25. Kannel, P.R., Lee, S., Kanel, S.R. and Khan, S.P. (2007). Chemometric application in classification and assessment of monitoring locations of urban river system. Analytica. Chim Acta 582: 390-399. 26. Official Portal Terengganu State.(2015). Terengganu http: //jheatweb. terengganu. gov. my/ maxc2020 /agensi/article2.php?sectionid=1&cid=1&aid=6997. Accessed on 21 December 2014. 27. Population and Housing Census Report, Population and Housing Census of Malaysia (2014) Oficial Website.https://www.statistics.gov.my/mycensus2010/index.php?option=com_content&view=frontpage&Itemi d=1&lang=en. Accessed on 21 Dicember 2014. 28. Imam, E. (2011). Use of geospatial technology in evaluating landscape cover type changes in Chandoli National Park, India. Computational Ecology and Software 1 (2): 95-111. 29. Shrestha, S. and Kazama, F. (2007). Assessment of Surface wáter quality Using Multivariated Statistical Techniques: a case study of the Fuji River Basin, Japan. Environment Modelling Software 22 (4): 464-475. 30. Sulong, I. and Ismail, A. (2002). Mangrove mapping using landsat imagery and aerial photographs : kemaman district . Environment, Development and Sustainability 4: 135–152. 31. Yusoff, F., Sahril, N., Rasidi, N. M., Zaki, N. A. M., Muhamad, N., and Ahmad, N. (2014). Illicit Drug Use among School-Going Adolescents in Malaysia. Asia-Pacific Journal of Public Health 26(5 Suppl): 100S–107S. 32. Colasante, E., Molinaro, S. and Mariani, F. (2008). Italian of public health spatial analysis of drug-related hospital admissions : an auto-Gaussian model to estimate the hospitalization rates in Italy. Italian Journal of Public Health 5(4): 253–260. 33. National Antidrug Agency, (2010). Laporan Dadah Bulan Disember 2010. National Antidrug Agency
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| spellingShingle |
Spatial and temporal assessment on drug addiction using multivariate analysis and GIS [Penilaian ruang dan masa terhadap penagihan dadah menggunakan analisis multivariat dan GIS]
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| summary |
There is a need for managing and displaying drug addiction phenomena and trend at both spatial and temporal scales. Spatial and temporal assessment on drug addiction in Terengganu was undertaken to understand the geographical area of district in the same cluster, in addition, identify the hot spot area of this problem and analysis the trend of drug addiction. Data used were topography map of Terengganu and number of drug addicted person in Terengganu by district within 10 years (2004-2013). Number of drug addicted person by district were mapped using Geographic Information system and analysed using a combination of multivariate analysis which is cluster analysis were applied to the database in order to validate the correlation between data in the same cluster. Result showed a cluster analysis for number of drug addiction by district generated three clusters which are Besut and Kuala Terengganu in cluster 1 named moderate drug addicted person (MDA), Dungun, Marang, Setiu and Hulu Terengganu in cluster 2 named lower drug addicted person (LDA) and Kemaman in cluster 3 named high drug addicted person(HDA). This analysis indicates that cluster 3 which is Kemaman is a hot spot area. These results were beneficial for stakeholder to monitor and manage this problem especially in the hot spot area which needs to be emphasized.
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| title |
Spatial and temporal assessment on drug addiction using multivariate analysis and GIS [Penilaian ruang dan masa terhadap penagihan dadah menggunakan analisis multivariat dan GIS]
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| title_full |
Spatial and temporal assessment on drug addiction using multivariate analysis and GIS [Penilaian ruang dan masa terhadap penagihan dadah menggunakan analisis multivariat dan GIS]
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| title_fullStr |
Spatial and temporal assessment on drug addiction using multivariate analysis and GIS [Penilaian ruang dan masa terhadap penagihan dadah menggunakan analisis multivariat dan GIS]
|
| title_full_unstemmed |
Spatial and temporal assessment on drug addiction using multivariate analysis and GIS [Penilaian ruang dan masa terhadap penagihan dadah menggunakan analisis multivariat dan GIS]
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| title_short |
Spatial and temporal assessment on drug addiction using multivariate analysis and GIS [Penilaian ruang dan masa terhadap penagihan dadah menggunakan analisis multivariat dan GIS]
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| title_sort |
spatial and temporal assessment on drug addiction using multivariate analysis and gis [penilaian ruang dan masa terhadap penagihan dadah menggunakan analisis multivariat dan gis]
|