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Assessing the economic value of urban green spaces in Kuala Lumpur

A Samad NS, Abdul-Rahim AS, Mohd Yusof MJ, Tanaka K

Environ Sci Pollut Res Int, 2020 Apr;27(10):10367-10390.
PMID: 31939016 DOI: 10.1007/s11356-019-07593-7

This study assessed the economic value of public urban green spaces (UGSs) in Kuala Lumpur (KL) city by using the hedonic price method (HPM). It involves 1269 house units from eight sub-districts in KL city. Based on the hedonic price method, this study formulates a global and local model. The global model and local model are analyzed using ordinary least square (OLS) regression and geographically weighted regression (GWR). By using the hedonic price method, the house price serves as a proxy for public urban green spaces' economic value. The house price is regressed against the set of three variables which are structural characteristics, neighborhood attributes, and environmental attributes. Measurements of interest in this study are environmental characteristics, including distance to public UGSs and size of public UGSs. The results of the OLS regression illustrated that Taman Rimba Kiara and Taman Tasik Titiwangsa provide the maximum economic value. On average, reducing the distance of the house location to Taman Rimba Kiara by 10 m increased the house price by RM1700. Similarly, increasing the size of the Taman Tasik Titiwangsa by 1000 m2 increases the house price by RM60,000. The advantage of the GWR result is the economic value of public UGSs which can be analyzed by the specific location according to sub-district. From this study, the GWR result exposed that the economic values of Taman Rimba Bukit Kiara and Taman Tasik Titiwangsa were not significant in each of the sub-district within KL city. Taman Rimba Bukit Kiara was negatively significant at all sub-districts except Setapak and certain house locations located at the sub-district of KL. In contrast, Taman Tasik Titiwangsa was positively significant at all sub-districts except certain house locations at the sub-districts of Batu, KL, Setapak, and KL city center. In conclusion, results show that the house price is influenced by the environmental attribute. However, even though both of these public UGSs generate the highest economic value based on distance and size, its significant values with an expected sign are only obtained based on the specific house location as verified by the local model. In terms of model comparison, the local model was better compared with the global model.

Matched MeSH terms: Spatial Regression*
Impact of population growth and land use and land cover (LULC) changes on water quality in tourism-dependent economies using a geographically weighted regression approach

Rimba AB, Mohan G, Chapagain SK, Arumansawang A, Payus C, Fukushi K, et al.

Environ Sci Pollut Res Int, 2021 May;28(20):25920-25938.
PMID: 33475923 DOI: 10.1007/s11356-020-12285-8

This paper aims to assess the influence of land use and land cover (LULC) indicators and population density on water quality parameters during dry and rainy seasons in a tourism area in Indonesia. This study applies least squares regression (OLS) and Pearson correlation analysis to see the relationship among factors, and all LULC and population density were significantly correlated with most of water quality parameter with P values of 0.01 and 0.05. For example, DO shows high correlation with population density, farm, and built-up in dry season; however, each observation point has different percentages of LULC and population density. The concentration value should be different over space since watershed characteristics and pollutions sources are not the same in the diverse locations. The geographically weighted regression (GWR) analyze the spatially varying relationships among population density, LULC categories (i.e., built-up areas, rice fields, farms, and forests), and 11 water quality indicators across three selected rivers (Ayung, Badung, and Mati) with different levels of tourism urbanization in Bali Province, Indonesia. The results explore that compared with OLS estimates, GWR performed well in terms of their R2 values and the Akaike information criterion (AIC) in all the parameters and seasons. Further, the findings exhibit population density as a critical indicator having a highly significant association with BOD and E. Coli parameters. Moreover, the built-up area has correlated positively to the water quality parameters (Ni, Pb, KMnO4 and TSS). The parameter DO is associated negatively with the built-up area, which indicates increasing built-up area tends to deteriorate the water quality. Hence, our findings can be used as input to provide a reference to the local governments and stakeholders for issuing policy on water and LULC for achieving a sustainable water environment in this region.

Matched MeSH terms: Spatial Regression*
Fulltext Spatial regression with conditional autoregressive (CAR) errors for annual mean relative humidity in Peninsular Malaysia

Shitan, Mahendran, Kok, Wei Ling

Pertanika Journal of Science & Technology, 2009;17(2):-.
MyJurnal

Modelling observed meteorological elements can be useful. For instance, modelling rainfall has
been an interest for many researchers. In a previous research, trend surface analysis was used and
it was indicated that the residuals might spatially be correlated. When dealing with spatial data, any
modelling technique should take spatial correlation into consideration. Hence, in this project, fitting
of spatial regression models, with spatially correlated errors to the annual mean relative humidity
observed in Peninsular Malaysia, is illustrated. The data used in this study comprised of the annual
mean relative humidity for the year 2000-2004, observed at twenty principal meteorological stations
distributed throughout Peninsular Malaysia. The modelling process was done using the S-plus
Spatial Statistics Module. A total of twelve models were considered in this study and the selection
of the model was based on the p-value. It was found that a possible appropriate model for the
annual mean relative humidity should include an intercept and a term of the longitude as covariate,
together with a conditional autoregressive error structure. The significance of the coefficient of the
covariate and spatial parameter was established using the Likelihood Ratio Test. The usefulness
of the proposed model is that it could be used to estimate the annual mean relative humidity at
places where observations were not recorded and also for prediction. Some other potential models
incorporating the latitude covariate have also been proposed as viable alternatives.

Matched MeSH terms: Spatial Regression
Spatially varying correlation between environmental conditions and human leptospirosis in Sarawak, Malaysia

Kira R, Bilung LM, Ngui R, Apun K, Su'ut L

Trop Biomed, 2021 Jun 01;38(2):31-39.
PMID: 33973570 DOI: 10.47665/tb.38.2.034

The spatial distribution of environmental conditions may influence the dynamics of vectorborne diseases like leptospirosis. This study aims to investigate the global and localised relationships between leptospirosis with selected environmental variables. The association between environmental variables and the spatial density of geocoded leptospirosis cases was determined using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR). A higher prevalence of leptospirosis was detected in areas with higher water vapour pressure (exp(â): 1.12; 95% CI: 1.02 - 1.25) and annual precipitation (exp(â): 1.15; 95% CI: 1.02 - 1.31), with lower precipitation in the driest month (exp(â): 0.85; 95% CI: 0.75 - 0.96) and the wettest quarter (exp(â): 0.88; 95% CI: 0.77 - 1.00). Water vapor pressure (WVP) varied the most in the hotspot regions with a standard deviation of 0.62 (LQ: 0.15; UQ; 0.99) while the least variation was observed in annual precipitation (ANNP) with a standard deviation of 0.14 (LQ: 0.11; UQ; 0.30). The reduction in AICc value from 519.73 to 443.49 indicates that the GWPR model is able to identify the spatially varying correlation between leptospirosis and selected environmental variables. The results of the localised relationships in this study could be used to formulate spatially targeted interventions. This would be particularly useful in localities with a strong environmental or socio-demographical determinants for the transmission of leptospirosis.

Matched MeSH terms: Spatial Regression
Measuring spatial inequalities in maternal and child mortalities in Pakistan: evidence from geographically weighted regression

Muhammad FS, Shahabudin SM, Talib MBA

BMC Public Health, 2024 Aug 16;24(1):2229.
PMID: 39152373 DOI: 10.1186/s12889-024-19682-5

BACKGROUND: In developing countries, the death probability of a child and mother is more significant than in developed countries; these inequalities in health outcomes are unfair. The present study encompasses a spatial analysis of maternal and child mortalities in Pakistan. The study aims to estimate the District Mortality Index (DMI), measure the inequality ratio and slope, and ascertain the spatial impact of numerous factors on DMI scores across Pakistani districts.
METHOD: This study used micro-level household datasets from multiple indicator cluster surveys (MICS) to estimate the DMI. To find out how different the DMI scores were, the inequality ratio and slope were used. This study further utilized spatial autocorrelation tests to determine the magnitude and location of the spatial dependence of the clusters with high and low mortality rates. The Geographically Weighted Regression (GWR) model was also applied to examine the spatial impact of socioeconomic, environmental, health, and housing attributes on DMI.
RESULTS: The inequality ratio for DMI showed that the upper decile districts are 16 times more prone to mortalities than districts in the lower decile, and the districts of Baluchistan depicted extreme spatial heterogeneity in terms of DMI. The findings of the Local Indicator of Spatial Association (LISA) and Moran's test confirmed spatial homogeneity in all mortalities among the districts in Pakistan. The H-H clusters of maternal mortality and DMI were in Baluchistan, and the H-H clusters of child mortality were seen in Punjab. The results of GWR showed that the wealth index quintile has a significant spatial impact on DMI; however, improved sanitation, handwashing practices, and antenatal care adversely influenced DMI scores.
CONCLUSION: The findings reveal a significant disparity in DMI and spatial relationships among all mortalities in Pakistan's districts. Additionally, socioeconomic, environmental, health, and housing variables have an impact on DMI. Notably, spatial proximity among individuals who are at risk of death occurs in areas with elevated mortality rates. Policymakers may mitigate these mortalities by focusing on vulnerable zones and implementing measures such as raising public awareness, enhancing healthcare services, and improving access to clean drinking water and sanitation facilities.

Matched MeSH terms: Spatial Regression*