Spatial Pattern Analysis on Dengue Cases in Bangi district, Selangor, Malaysia

Abstract Background: Dengue outbreak has proliferated around the developing countries, including Malaysia, in recent decades. Thus, understanding the distribution pattern is essential for urbanization livelihood. Method: The objective of this study is to determine the trend of dengue cases reported from year 2014 to 2018 and the spatial pattern for dengue spread with reference to weather elements in Bangi town. Results: Spatial statistical analyses conducted found that the distribution pattern and spatial mean center for dengue cases was clustered at the east of Bangi region. Directional distribution observed that the elongated polygon of dengue cluster stretched from the northeast to the southwest of Bangi district. Standard distance for dengue cases was the smallest for the year 2014 (0.017 m), and the largest was in the year 2016 (0.019 m), whereas dengue cases in year 2015, 2017, and 2018 were measured at 0.018 m. The average nearest neighbor analysis also observed clustered patterns for dengue cases in Bangi district. Pearson’s correlation analysis found that temperature (r = -0.269) was negatively correlated with dengue cases for year 2014 and 2018; however, rainfall amount (r = 0.286) and rain days (r = 0.250) were positively correlated with dengue cases in year 2018.Conclusions: The three spatial statistical analyses (spatial mean center, standard distance, and directional distribution) findings illustrated that the dengue cases from the year 2014 to 2018 are clustered on the northeast to the southwest of the study region. The rainfall element is found to be a significant positive factor correlated for most study years compared to temperature element.

Download Full-text

Spatial pattern of dengue cases: An analysis in Bangi District, Selangor, Malaysia

Geospatial health ◽

10.4081/gh.2021.915 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Nuriah Abd Majid ◽

Ruslan Rainis ◽

Mazrura Sahani ◽

Ahmad Fariz Mohamed ◽

Sarah Aziz Abdul Ghani Aziz ◽

...

Keyword(s):

Developing Countries ◽

Spatial Pattern ◽

Distribution Pattern ◽

Statistical Analyses ◽

Nearest Neighbour ◽

Study Region ◽

Directional Distribution ◽

Standard Distance ◽

Dengue Outbreaks ◽

Neighbour Analysis

In recent decades, dengue outbreaks have become increasingly common around the developing countries, including Malaysia. Thus, it is essential for rural as well as urbanised livelihood to understand the distribution pattern of this infection. The objective of this study is to determine the trend of dengue cases reported from the year 2014 to 2018 and the spatial pattern for this spread. Spatial statistical analyses conducted found that the distribution pattern and spatial mean centre for dengue cases were clustered in the eastern part of the Bangi region. Directional distribution observed that the elongated polygon of dengue cluster stretched from the Northeast to the Southwest of Bangi District. The standard distance observed for dengue cases was smallest in the year 2014 (0.017 m), and largest in 2016 (0.019 m), whereas in the year 2015, 2017 and 2018, it measured 0.018 m. The average nearest neighbour analysis also displayed clustered patterns for dengue cases in the Bangi District. The three spatial statistical analyses (spatial mean centre, standard distance and directional distribution) findings illustrate that the dengue cases from the year 2014 to 2018 are clustered in the Northeast to the Southwest of the study region.

Download Full-text

Distribution and Spatial Pattern Analysis on Dengue Cases in Seremban District, Negeri Sembilan, Malaysia

Sustainability ◽

10.3390/su11133572 ◽

2019 ◽

Vol 11 (13) ◽

pp. 3572 ◽

Cited By ~ 3

Author(s):

Nuriah Abd Majid ◽

Nurafiqah Muhamad Nazi ◽

Ahmad Fariz Mohamed

Keyword(s):

Spatial Pattern ◽

Distribution Pattern ◽

Pattern Analysis ◽

Distribution Patterns ◽

Spatial Pattern Analysis ◽

Directional Distribution ◽

Spatial Statistical Analysis ◽

Standard Distance ◽

Urbanization Rate ◽

And Control

Dengue fever disease increases alongside urbanization rate in tropical countries. Hence, the need to visualize the distribution pattern of increases is vital for the management of dengue cases, especially in Malaysia. Thus, the dengue surveillance system is proposed for the monitoring of dengue cases using computer-generated modeling for spatial distribution patterns, which is important for management and control. The present study performed distribution and spatial pattern analysis of dengue cases reported in the growing Seremban district in Negeri Sembilan, Malaysia in 2008 and 2009. The purpose of the study is to evaluate the pattern of distribution and determine whether it is clustered or dispersed. A total of 1401 and 1056 cases for dengue-related diseases were reported by the Ministry of Health Malaysia in Seremban district in the years 2008 and 2009, respectively. Three spatial statistical analysis were conducted: Spatial mean center, directional distribution, and standard distant on distribution of dengue cases reported. This study found that the distribution pattern for dengue cases is clustered. Spatial mean center and directional distribution for both sets of years have slight differences. Meanwhile, standard distance for dengue cases reported in the year 2008 is 22,085.82 m, which is bigger than dengue cases reported in 2009, showing a standard distance of 20,318.35 m. More sets of cases throughout years are required in further studies to identify factors that contribute to dengue epidemiology in the Seremban district undergoing urbanization.

Download Full-text

EFFECTS OF HETEROGENIETY ON SPATIAL PATTERN ANALYSIS OF WILD PISTACHIO TREES IN ZAGROS WOODLANDS, IRAN

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-2-w3-109-2014 ◽

2014 ◽

Vol XL-2/W3 ◽

pp. 109-114 ◽

Cited By ~ 1

Author(s):

Y. Erfanifard ◽

F. Rezayan

Keyword(s):

Spatial Pattern ◽

Pattern Analysis ◽

Second Order ◽

Spatial Pattern Analysis ◽

Fars Province ◽

Summary Statistics ◽

Goodness Of Fit Test ◽

Study Region ◽

Vegetation Heterogeneity ◽

Pistachio Trees

Vegetation heterogeneity biases second-order summary statistics, e.g., Ripley's K-function, applied for spatial pattern analysis in ecology. Second-order investigation based on Ripley's K-function and related statistics (i.e., L- and pair correlation function g) is widely used in ecology to develop hypothesis on underlying processes by characterizing spatial patterns of vegetation. The aim of this study was to demonstrate effects of underlying heterogeneity of wild pistachio (Pistacia atlantica Desf.) trees on the second-order summary statistics of point pattern analysis in a part of Zagros woodlands, Iran. The spatial distribution of 431 wild pistachio trees was accurately mapped in a 40 ha stand in the Wild Pistachio & Almond Research Site, Fars province, Iran. Three commonly used second-order summary statistics (i.e., K-, L-, and g-functions) were applied to analyse their spatial pattern. The two-sample Kolmogorov-Smirnov goodness-of-fit test showed that the observed pattern significantly followed an inhomogeneous Poisson process null model in the study region. The results also showed that heterogeneous pattern of wild pistachio trees biased the homogeneous form of K-, L-, and g-functions, demonstrating a stronger aggregation of the trees at the scales of 0–50 m than actually existed and an aggregation at scales of 150–200 m, while regularly distributed. Consequently, we showed that heterogeneity of point patterns may bias the results of homogeneous second-order summary statistics and we also suggested applying inhomogeneous summary statistics with related null models for spatial pattern analysis of heterogeneous vegetations.

Download Full-text