Unveiling the Impact of Physical Geography on Poverty: A Comprehensive Analysis for Sustainable Development

This study examines the effects of physical geography, demographic characteristics of household heads, and poverty, with a specific focus on the number of poor household heads within districts of Terengganu. Through the utilization of a Poisson log-linear modeling approach, the research investigates the effects of physical geography and demographic factors, on the number of poor household heads for each of the sub-districts. The central concern of this research revolves around the need to comprehend the underlying reasons for differing poverty rates among sub-districts in Terengganu. To carry out the analysis, a Poisson log-linear modeling is employed for the data, leveraging SPSS and Rstudio for statistical analysis. This method enabled us to thoroughly assess how physical geography factors (including terrain and accessibility) and demographic characteristics of household heads (including age, education level, and employment status) influence poverty rates. To determine the distribution of spatial poverty, ArcMap is used to visualize the Standardised Poverty Ratio. The results of the study show that 31 sub-districts were identified as not being at risk of poverty and another 31 were labeled as having a high poverty rate. Furthermore, the Poisson regression analysis yielded several important insights into the factors influencing poverty rates. Specifically, it is found that a higher average age is associated with a decrease in poverty. Conversely, an increase in non-formal education levels, lower elevations, steeper slopes, and higher river density are linked to an increase in poverty. These findings have significant implications for policy formulation and targeted interventions in Terengganu, providing valuable guidance for addressing poverty-related challenges. The mapping of high-risk poverty areas offers crucial information for spatially targeted interventions, facilitating the implementation of more efficient poverty reduction measures. Furthermore, research findings enhance the understanding of the intricate dynamics between physical geography, demographic characteristics, and household poverty. By identifying the significant factors impacting poverty, this study provides valuable insights for developing targeted poverty alleviation strategies and formulating evidence-based policies. In conclusion, this study serves to inform policymakers, researchers, and practitioners about the multifaceted relationships between physical geography, demographic characteristics, and household poverty. By recognizing the critical role played by these factors, stakeholders can devise comprehensive approaches tailored to specific contexts, effectively addressing poverty, promoting inclusive growth, and improving the well-being of vulnerable populations

Evolution of green space under rapid urban expansion in Southeast Asian cities

Globally, rapid urban expansion has caused green spaces in urban areas to decline considerably. In this study, the rapid expansion of three Southeast Asia cities were considered, namely, Kuala Lumpur City, Malaysia; Jakarta, Indonesia; and Metro Manila, Philippines. This study evaluates the changes in spatial and temporal patterns of urban areas and green space structure in the three cities over the last two decades. Land use land cover (LULC) maps of the cities (1988/1989, 1999 and 2014) were developed based on 30-m resolution satellite images. The changes in the landscape and spatial structure were analysed using change detection, landscape metrics and statistical analysis. The percentage of green space in the three cities reduced in size from 45% to 20% with the rapid expansion of urban areas over the 25-year period. In Metro Manila and Jakarta, the proportion of green space converted to urban areas was higher in the initial 1989 to 1999 period than over the latter 1999 to 2014 period. Significant changes in green space structure were observed in Jakarta and Metro Manila. Green space gradually fragmented and became less connected and more unevenly distributed. These changes were not seen in Kuala Lumpur City. Overall, the impact of spatial structure of urban areas and population density on green space is higher in Jakarta and Metro Manila when this is compared to Kuala Lumpur. Thus, the results have the potential to clarify the relative contribution of green space structure especially for cities in Southeast Asia where only a few studies in urban areas have taken place

Habitat quality assessment in the Royal Belum rainforest, Malaysia using spatial analysis

Royal Belum rainforest contains various flora and fauna species, however, the assessment of habitat quality is still lacking. This study aims to develop the habitat quality zone in the Royal Belum rainforest. The downloaded Landsat 8 OLI/TIRS CI satellite images in the year 2020 from the United States Geological Survey (USGS) were processed using supervised classification and exported into vector data in ArcGis 10.8. Land use, normalized difference vegetation index (NDVI), buffer, and land structure were then analyzed. The result shows that the highest percentage and density of the land use of the Royal Belum rainforest is vegetation. Buffer zone analysis identifies the risky area for habitat in the range of 1km and 5km from the built-up area. The area within the buffer ring should be protected from building and construction to ensure habitat quality in that area can be maintained. This study will give a better understanding of land use and vegetation index assessment for future planning in the Royal Belum rainforest. Therefore, habitat quality assessment is an important tool that can help to identify areas of high-quality habitat that are crucial for the survival and reproduction of target species and to prioritize these areas for conservation and management

Combination of geostatistics and drastic model in assessing groundwater vulnerability to nitrate contamination

Due to a rapid development of industrial and population growth in Malaysia, the demand for water has increased tremendously. This has caused the shortage of water supply and the new water sources have to be identified. Groundwater is one of the alternative ways. In the north eastern state of Kelantan, many people rely on the groundwater as their water resource. However, due to the uncontrolled development and human activities, groundwater is subjected to contamination by nitrate geochemical. This research is intended to assess this vulnerability by using a combination of geostatistical approach and a commonly applied model called DRASTIC (Depth, Recharge, Aquifer, Soil, Topography, Influence of the vadose zone and Conductivity). With Geographical Information System (GIS), each method is applied to produce the groundwater vulnerability maps. In geostatistical approach, the probability map is produced by using Kriging interpolation in order to predict a specific vulnerability of contamination risk. Meanwhile, the Weighted Overlay technique has been applied in the DRASTIC model to produce a vulnerability index map that shows the levels of vulnerability of the study area. The resultant maps are further combined to study the contamination pattern, in which case the nitrate concentration is taken as example. Then, the ground truthing of nitrate concentration tests are carried out to validate the reliability of the resultant maps. As a result, the pattern shows a positive relationship between the probabilility map and vulnerability index map. This indicates that the combination of these two methods has given a significant finding in assessing groundwater vulnerability to contaminatio

Assessing groundwater vulnarability to contamination using geostatical approach

Due to a rapid development of industrial and population growth in Malaysia, the demand for water has increased tremendously. This has caused the shortage of water supply and the new water sources have to be identified – groundwater is one of the alternative. In the north eastern state of Kelantan, most people rely on the groundwater as their water resource. However, due to uncontrolled development and human activities, groundwater is subjected to pollution and geochemical contamination. This paper is intended to assess groundwater vulnerability to contamination using geostatistical approach instead of using the typical DRASTIC (Depth, Recharge, Aquifer, Soil, Topography, Influence of the vadose zone and Conductivity) model. Geostatistical approach has the capability to predict a specific vulnerability of contamination risk as an alternative to the DRASTIC model which is unable to provide the measurable information of geochemical characteristics. The results of the modelling are produced and presented in the form of Prediction Map and Probability Map. The finding of the study can be used to identify the geochemical concentration based on Department of Environment (DOE) standards Assessing groundwater vulnerability to contamination using geostatistical approach (PDF Download Available). Available from: https://www.researchgate.net/publication/256375672_Assessing_groundwater_vulnerability_to_contamination_using_geostatistical_approach [accessed Jul 2, 2017]

Geostatics approach with indicator kriging for assesing groundwater vulnerablity to nitrate contamination

In Malaysia, the demand for water has increased tremendously and groundwater has been identified as one of the alternative to new water sources. In north eastern state of Kelantan, Malaysia, almost 70 percents of people consumes groundwater in their daily lives. However, due to uncontrolled development and human activities, groundwater is subjected to pollution and geochemical contamination. This paper is intended to assess groundwater vulnerability to nitrate (NO 3) contamination using geostatistical approach with indicator kriging instead of using the typical DRASTIC (Depth, Recharge, Aquifer, Soil, Topography, Influence of the vadose zone and Conductivity) model. Geostatistical approach has the capability to predict and understand the geochemical concentration pattern as an alternative to the DRASTIC model which is unable to provide the measurable information of geochemical characteristics. The results of the modelling are produced and presented in the form of probability map using indicator kriging interpolation. The finding of the study can be used to identify the probability of geochemical concentration exceeding the specified threshold. Geostatistics approach with indicator kriging for assessing groundwater vulnerability to Nitrate contamination (PDF Download Available). Available from: https://www.researchgate.net/publication/256375591_Geostatistics_approach_with_indicator_kriging_for_assessing_groundwater_vulnerability_to_Nitrate_contamination [accessed Jul 2, 2017]

Unveiling the Impact of Physical Geography on Poverty: A Comprehensive Analysis for Sustainable Development

This study examines the effects of physical geography, demographic characteristics of household heads, and poverty, with a specific focus on the number of poor household heads within districts of Terengganu. Through the utilization of a Poisson log-linear modeling approach, the research investigates the effects of physical geography and demographic factors, on the number of poor household heads for each of the sub-districts. The central concern of this research revolves around the need to comprehend the underlying reasons for differing poverty rates among sub-districts in Terengganu. To carry out the analysis, a Poisson log-linear modeling is employed for the data, leveraging SPSS and Rstudio for statistical analysis. This method enabled us to thoroughly assess how physical geography factors (including terrain and accessibility) and demographic characteristics of household heads (including age, education level, and employment status) influence poverty rates. To determine the distribution of spatial poverty, ArcMap is used to visualize the Standardised Poverty Ratio. The results of the study show that 31 sub-districts were identified as not being at risk of poverty and another 31 were labeled as having a high poverty rate. Furthermore, the Poisson regression analysis yielded several important insights into the factors influencing poverty rates. Specifically, it is found that a higher average age is associated with a decrease in poverty. Conversely, an increase in non-formal education levels, lower elevations, steeper slopes, and higher river density are linked to an increase in poverty. These findings have significant implications for policy formulation and targeted interventions in Terengganu, providing valuable guidance for addressing poverty-related challenges. The mapping of high-risk poverty areas offers crucial information for spatially targeted interventions, facilitating the implementation of more efficient poverty reduction measures. Furthermore, research findings enhance the understanding of the intricate dynamics between physical geography, demographic characteristics, and household poverty. By identifying the significant factors impacting poverty, this study provides valuable insights for developing targeted poverty alleviation strategies and formulating evidence-based policies. In conclusion, this study serves to inform policymakers, researchers, and practitioners about the multifaceted relationships between physical geography, demographic characteristics, and household poverty. By recognizing the critical role played by these factors, stakeholders can devise comprehensive approaches tailored to specific contexts, effectively addressing poverty, promoting inclusive growth, and improving the well-being of vulnerable populations

Habitat quality assessment in the Royal Belum rainforest, Malaysia using spatial analysis

Royal Belum rainforest contains various flora and fauna species, however, the assessment of habitat quality is still lacking. This study aims to develop the habitat quality zone in the Royal Belum rainforest. The downloaded Landsat 8 OLI/TIRS CI satellite images in the year 2020 from the United States Geological Survey (USGS) were processed using supervised classification and exported into vector data in ArcGis 10.8. Land use, normalized difference vegetation index (NDVI), buffer, and land structure were then analyzed. The result shows that the highest percentage and density of the land use of the Royal Belum rainforest is vegetation. Buffer zone analysis identifies the risky area for habitat in the range of 1km and 5km from the built-up area. The area within the buffer ring should be protected from building and construction to ensure habitat quality in that area can be maintained. This study will give a better understanding of land use and vegetation index assessment for future planning in the Royal Belum rainforest. Therefore, habitat quality assessment is an important tool that can help to identify areas of high-quality habitat that are crucial for the survival and reproduction of target species and to prioritize these areas for conservation and management

Spatial Distribution of COVID-19 Infected Cases in Kelantan, Malaysia

Kota Bharu city in Kelantan, Malaysia was reported with the highest cases of coronavirus disease 2019 (COVID-19) among other districts. Kota Bharu is the capital city of Kelantan, which acts as the administrative, commercial, and financial areas. A large population pool may become a potential carrier for disease transmission to become an epidemic. However, the impact of population density on the COVID-19 outbreak in Malaysia is still unknown and undiscovered. Therefore, this study investigates the impact of population density on COVID-19 as a potential virus transmission carrier using linear regression models. The chances of formulating new strategies for combating COVID-19 are higher when the driver of transmission potential is identified. This study shows that the highest value of infected area density is in Kota Bharu (0.76), while the infected risk area was highest in Jeli (0.33). This study found that there is a strong relationship between COVID-19 infection cases in Kelantan and population density (R2 which is 0.845). Therefore, high population density was identified as a potential driver of transmission of COVID-19 outbreak. Understanding the potential drivers of the disease in a local setting is very important for better preparation and management. The outcome of the study can aid in the development of a new analytical model for strategic planning of Zero COVID-19 for securing the public health and wellness, both social and economic, by researchers, scientists, planners, resource managers, and decision-makers