Disaster Risk Reduction - Research Proposal Example

Research Proposal on Disaster Risk Reduction Presently, there is an increasing growth of informal settlements, especially squatter settlements and slums across the world. Growth in these informal settlements, especially in second and third-world countries is becoming a thorn in the flesh of urban planning. As a proposition, it would be appropriate to employ appropriate disaster mitigation measures in addressing the risks and challenges associated with upgrading and management of informal settlement schemes (ROBERTS, 2014). One basic component of an ideal disaster mitigation measures framework is environmental impact assessment, commonly abbreviated as EIA. Technically, environmental impact assessment entails quantifying the magnitude of potential effects and anticipated consequences in project developments. EIA falls within the preparedness section of disaster risk reduction. In practice, numerous methods with varied levels of effectiveness and efficiencies are used in conducting environmental impact assessment exercises. One emerging technique that can be effective in conducting EIA is the use of computer aided methods (CHENG & HUNG, 2012). Therefore, the topic for this research will be the use of computer aided methods in executing the prediction and interpretation stages of environmental impact assessment exercises in disaster preparedness for informal settlement projects. Theoretical Background Typically, informal settlements experience disastrous problems which include but not limited to natural disasters like earthquakes and tsunamis, and artificial disasters like excessive pollution from hazardous materials and tragedies like fire outbreaks. Unlike other residential settings, informal settlements have high vulnerability to both natural and artificial disasters. Technically, slums and squatter settlements are established on locations that do not meet the necessary standards for development of residential settings (REVI, 2003). For example, some slums in African nations like Kenya and South American nations like Guatemala are built adjacent to infrastructures like busy railway lines and petroleum transportation pipelines (KURIA, 2004). Practically, performing preparedness steps like environmental impact assessment exercises using traditional methods like interviews and questionnaire surveys can be both costly and ineffective (KAMBER, HAN & PEI, 2011). Most slum dwellers possess limited technical knowledge concerning anticipated natural or artificial disasters. Therefore, the use of computer aided methods, particularly data mining techniques can prove efficient in conducting environmental impact assessment exercises (KALIAM, 2010). Therefore, this research exercise will serve the purpose of ascertaining the effectiveness of computer aided methods in the performance of disaster preparedness exercises. Literature Review Actually, disasters in informal settlements are not theoretical in nature. Probable disasters in informal settlements include explosion of fuel storage units, release of toxic chemical wastes from industrial processes, water pollution by heavy metals, collapse of buildings, and earthquakes (MASKREY, 2013). Primarily, informal settlements, especially slums are heavily congested. For example, the Kibera slums in Kenya have a population of 8 times its official capacity. The Kenyan slum is built on top of a petroleum pipeline network. On September 12, 2011, one of the oil pipes ruptured, prompting the poor slum dwellers to salvage the spilling valuable commodity. Suddenly, the pipeline exploded, killing approximately 121 people in an instant, and injuring thousands others (PELLING & WISNER, 2012). Similar accidents have occurred in other parts of the world, especially in the infamous slums of Guatemala. Allegedly, Guatemala slums lack clean water and sewage systems. Researchers have termed some of the Guatemala slums as breeding grounds for infectious diseases, especially cholera and malaria. Each year, approximately 12,400 children die in Guatemala slums due to cholera, malaria and other waterborne diseases (LINDSEY, 2008). At this juncture, it is admissible that proper prediction and interpretation methods are necessary in the prevention and mitigation of such informal settlement disasters (DOWNING, 2006). Scope of the Research This research will be conducted within the boundaries of a practical environmental impact assessment exercise, particularly the prediction and interpretation steps using data mining technique as a computer aided method. During environmental impact assessment, prediction is instrumental in quantifying the effects of probable consequences. Technically, subsequent steps of disaster risk reduction like mitigation, response and recovery are formulated from prediction findings. Therefore, prediction and subsequent interpretation of disaster impacts feature as one of the key step in addressing environmental challenges associated with informal settlements (LAVELL & KAMANGA, 2003). In this context, data mining techniques are employed as means of promoting the economic efficiency, accuracy and reliability of prediction and interpretation exercises. As aforementioned, collection of environmental related data from informal settlements during environmental impact assessment exercises can be both costly and time consuming (SMITH, 2010). Fortunately, computer aided methods like data mining facilitates acquisition of enormous sets of relevant data from relevant databases. Examples of top environmental and health management databases include Flora-base, Scopus, Biosis Previews, and CAB Abstracts among others. Admittedly, most of these health and environmental management databases contain scientifically acquired data and information relating to variables of health and environmental disasters like fire tragedies and disease pandemics in informal settlements (MORIO, 2009). Therefore, this research will examine the use of data mining techniques in the acquisition of relevant data and information from databases, and subsequent establishment of relationship between relevant variables in predicting future disaster trends in informal settlements. For instance, relevant research articles contained in health databases sheds light on the expected evolutionary trends of waterborne disease causing microorganisms. Typically, evolutions and modifications of microorganisms translates to cases of drug resistance; hence worsening health pandemics like malaria and cholera in informal settlements. Therefore, data mining techniques facilitate cheap and timely acquisition of such valuable data and information, thus enhancing the accuracy and success of environmental impact assessment steps during disaster preparedness exercises (MOHAN & PARKER, 2005). Methodology The research project will be conducted in a scientific manner. Therefore, the research question will be; Are computer aided methods like data mining effective in the execution of prediction and interpretation stages during disaster preparedness exercises? Subsequently, the underlying research hypothesis statement will be; use of computer aided techniques during prediction of disaster effects and impacts increase the accuracy and reliability of findings in preparedness exercises within informal settlements. In this case, the independent variable in the hypothesis is the use of computer aided techniques in prediction exercise, while the dependent variable is the magnitude of accuracy and reliability observed in resultant findings (TREIMAN, 2009). This will be a quantitative research exercise where numerical data will be acquired and analyzed in order to establish the nature of relationship between the dependent and the independent variables (TING & LIM, 2013). Data will be acquired through secondary sources, especially online articles from scholarly journals, and relevant printed journals in the library. After data acquisition exercises are completed, relevant statistical techniques, especially correlation and regression tests will be performed in analyzing and interpreting the data. Research Timeline The tabulated timeline below indicates the approximate time that will be needed to sufficiently complete essential steps of the research project. Research Step Approximate Time Preparation of proposal 2 days Completion of literature review 2 day Completion of data collection 4 days Completion of data analysis 2 days Presentation of findings 2 days Compilation of the final report 3 days Total Time 15 days; approximately 2 weeks Expected limitations and Delimitations One anticipated limitation for the research project will be time constrains. Thorough investigation and sufficient acquisition of relevant data from either primary or secondary sources may require frequent visits to sources like libraries and databases. In addition, succinct analysis and interpretation of findings coupled with preparation of attractive reports may require more time than specified in the timeline (GACHOCHO, 1999). Expected delimitations for the projects would include omission of ethical steps like consent acquisition exercises, and reliance on secondary data sources instead of conducting field exercises on populations. Acquisition of consent from subjects will not be necessary, since data will be primarily acquired from secondary sources. In addition, much time and money will be saved by avoiding primary data collection exercises (KURIA, 2004). Part 2: A Brief Test Run with Reflection Data Collection Exercise As a test run, a data collection exercise will be conducted on the topic of economic benefits of disaster preparedness. As the old saying goes; prevention is better than cure. In this regard, disaster preparedness is a preventive measure that leads to reduction of economic costs related to disasters. Relevant literature indicates that every $1 used in preparedness measures saves up to $90 that would have been used in the control and recovery stages of disasters (KAMBER, HAN & PEI, 2011). Hypothesis for this test run will focus on the health issue of malaria in the African nation of Congo. Apparently, issuance of mosquito nets and disinfection of households in Congo has enabled USAID to save up to 90% of resources that would have been used in the treatment of malaria cases in the country (GADEL, 2008). In this context, disaster preparedness measures like issuance of mosquito nets is the independent variable, while the percentage of economic resources saved through the preparedness measures feature as the dependent variable. Relevant data was acquired from USAID websites, and other related research articles published online. Data collected from the secondary sources are tabulated below. Table 1: Yearly number of mosquito nets distributed and cost of malaria treatment in Congo Year Number of mosquito nets issued nationally Cost of malaria treatment 2006 20,000 $500,000 2007 70,000 $380,000 2008 150,000 $155,000 2009 200,000 $80,000 Data Analysis Exercise In the test run, relevant statistical techniques was used in establishing the nature relationship between the variable of malaria nets distributed and the variable of malaria treatment cost. First, arithmetic means for both the number of nets and the treatment costs were calculated. From the calculated means, the Pearson’s product moment correlation coefficient was calculated. During the correlation calculation, a coefficient of -0.65 was obtained. In statistical contexts, a correlation coefficient of -0.65 indicates presence of a strong negative relationship between variables under consideration (KEITH & PUNCH, 2005). In this case, there is a strong negative relationship between the number of nets issued by USAID to Congo citizens, and the cost of malaria treatment in the nation. This means that the more the number of nets issued, the lower the cost of malaria treatment. From the analysis exercise, it emerged that disaster preparedness exercises like issuance of mosquito nets by USAID in Congo reduced the cost of malaria treatment in the nation (CAPUTI, 2001). Personal Reflection After conducting the test run, it became evident that explicit determination of the independent and the dependent variables is instrumental in enhancing success of quantitative research exercises. Apparently, succinctly defined variables provide a directional effect during the entire data collection and data analysis exercises. In addition, selection of appropriate statistical analysis techniques facilitates objective determination of the nature and magnitude of relationship between underlying variables. Undeniably, secondary sources of data are as reliable as primary sources, just that secondary acquisition of data is not only less costly but also convenient in terms of time. At this juncture, it is acknowledgeable that the test run proved beneficial in ascertaining the feasibility of the proposed research project. Reference List CAPUTI, P. (2001). Advanced Quantitative Research Methods: An Investigative Approach. London: SAGE Publications. CHENG, Y. M. & HUNG, K. (2012). Computer-aided DSS for safety monitoring of geotechnical construction. American Journal of Automation and Construction, 11(5), p. 76-94. DOWNING, E. (2006). Reducing hazard vulnerability: Towards a common approach between disaster risk reduction. Journal of Climate Change and Disasters, 39(2), 06-13. GACHOCHO, M. (1999). Urban Poverty and Tragedies in Africa: Selected Countries Experiences. Geneva: UN-HABITAT. GADEL, M. (2008). Large-Scale Disasters: Prediction, Control, and Mitigation. Cambridge: Cambridge University Press. KALIAM, A. (2010). Research practice: Application of data mining concepts and techniques in secondary data collection. SRELS Journal of Information Management 41(4), p. 67-83. KAMBER, M., HAN, J. & PEI, J. (2011). Date Mining: Concepts and Techniques. New York: Elsevier Publishing. KEITH, F. & PUNCH, L. (2005). Introduction to Social Research: Quantitative and Qualitative Approaches. London: SAGE Publications. KURIA, G. (2004). Informal Settlements in Nairobi, Kenya: A Case Study. Nairobi: Undugu Society of Kenya. LAVELL, D. & KAMANGA, B. (2003). From everyday hazards to disasters: The accumulation of risk in urban areas. Journal of Environment and Planning, 15(1), p. 201-211. LINDSEY, K. (2008). Sanitation in informal settlements: State and infrastructure. Journal of Environment and Planning, 40(1). p.88-107. MASKREY, A. (2013). Disaster Mitigation: A Community Based Approach. Los Angeles: University of California in Berkeley. MOHAN, M. & PARKER, R. (2005). Informal Settlements, Environmental Degradation, and Disaster Vulnerability: Guatemala Case Study. New York: World Bank Publications. MORIO, R. (2009). Slums of the World: The Face of Urban Poverty in the New Millennium. Washington D.C.: Global Urban Observatory. PELLING, M. & WISNER, B. (2012). Disaster Risk Reduction: Cases from Urban Africa and South America. London: Earth-scan Publishing. REVI, A. (2003). Climate change risk: An adaptation and mitigation agenda for third-world cities. Delhi’s Journal of Environment & Urbanization, 38(7). p. 98-104. ROBERTS, C. M. (2014). The Dissertation Journey: A Practical and Comprehensive Guide to Planning, Writing and Defending your Dissertation. Cardiff: Corwin Press. SMITH, T. (2010). Natural disaster, mitigation and sustainability: The case of developing countries. Journal of International Planning Studies, 7(2). p. 157-176. TING, H. D. & LIM, M. (2013). Research Methodology: A Toolkit of Sampling and Data Analysis Techniques for Quantitative Research. Pittsburg: GRIN Verlag. TREIMAN, D. J. (2009). Quantitative Data Analysis: Doing Social Research to Test Ideas. Pittsburg: John Wiley & Sons. Read More