000			03166nam a22002897a 4500
003			URS
005			20251029113001.0
008			250904b ||||| |||| 00| 0 eng d
040			_cURS
245			_aSpatial And Temporal Analysis Of Calamity Prone Areas In Tanay, Rizal: A Regression And Time Series Approach _bJoenila N. Marajo et...al...
260			_cNovember 2024
300			_a41 pages.,; _bilustration, _c28cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
502			_aThesis _bCollege of Science _cUniversity of Rizal System-Morong _d2024
520			_aNatural disasters pose significant risks to communities worldwide, particularly in regions like Tanay, Rizal, where geographic and climatic conditions exacerbate vulnerability. This study, titled "Spatial and Temporal Analysis of Calamity-Prone Areas in Tanay, Rizal: A Regression and Time Series Approach," employs advanced analytical techniques to examine the spatial distribution and temporal trends of disaster risks. Using multiple linear regression and time series forecasting, the research integrates geographic variables such as elevation, proximity to rivers, and population density with temporal data on rainfall to develop predictive models. The findings reveal that rainfall is the most critical factor influencing flood incidents, while elevation is a mitigating element. Barangays with lower altitudes and closer proximity to rivers exhibit heightened vulnerability, whereas higher elevations demonstrate reduced flood risks despite substantial precipitation. Time series analysis uncovers an upward trend in precipitation patterns over recent years, highlighting the influence of climatic changes and the increasing frequency of natural calamities. The study's predictive models, which combine spatial and temporal analyses, achieve high levels of accuracy in identifying areas and periods of heightened disaster risks. These models offer valuable insights for disaster preparedness and resource allocation, allowing local authorities to prioritize interventions in high-risk areas. Furthermore, integrating historical data with predictive analytics underscores the need for proactive, data-driven strategies in mitigating disaster impacts and enhancing community resilience. By addressing the interplay between geographic and temporal factors, this research contributes to the growing knowledge on disaster risk management and provides a framework for evidence-based policy development. The outcomes of the study aim to support the creation of sustainable, resilient communities in Tanay, Rizal, and other calamity-prone regions
700			_aJoenila N. Marajo _eAuthor
700			_aJohn Emmanuel T. Atanacio _eAuthor
700			_aJen Rose Ds. Campo _eAuthor
700			_aMichael Jay R. Maraquio _eAuthor
700			_aJesriel A. Salcedo _eAuthor
700			_aMatematico, Aireen C. _edegree supervisor
856			_3Online Request for Student Unpublished Works _uhttps://forms.gle/7LqvGGkaDrUQqz429
856			_uhttps://drive.google.com/file/d/1vLNC5U5q0DmcXp3LCdWXOWl8vroM6bqL/view?usp=drive_link
942			_2lcc _cT
999			_c85924 _d85922