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

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.

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

2020 ◽  
Author(s):  
Nuriah Abd Majid ◽  
Ruslan Rainis ◽  
Mazrura Sahani ◽  
Ahmad Fariz Mohamed ◽  
Sarah Aziz Abdul Ghani Aziz ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Distribution Pattern ◽  
Pattern Analysis ◽  
Nearest Neighbor ◽  
Statistical Analyses ◽  
Spatial Pattern Analysis ◽  
Study Region ◽  
Directional Distribution ◽  
Standard Distance ◽  
Positive Factor

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.

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

2019 ◽  
Vol 11 (13) ◽  
pp. 3572 ◽  
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.

Determinants of Spatial Pattern in Saxicolous Lichen Communities

1991 ◽  
Vol 23 (3) ◽  
pp. 227-236 ◽  
Author(s):  
Elizabeth John ◽  
M. R. T. Dale
Keyword(s):  
Spatial Pattern ◽  
Plant Community ◽  
Analytical Techniques ◽  
Complete Picture ◽  
Nearest Neighbour ◽  
Ecological Strategies ◽  
Community Or ◽  
Lichen Community ◽  
Grid Points ◽  
Neighbour Analysis

AbstractIn order to obtain a complete picture of the factors determining spatial pattern in a saxicolous lichen community (or any other plant community) it is important to use a range of analytical techniques at a variety of scales. The Jonas Rockslide study is an example of this, and three analyses from this study are discussed here. Each scale gives a different perspective on the processes acting in the community. The largest scale shows that lichen distributions are correlated with attributes of whole rockfaces such as slope and aspect. The second analysis examines pattern at the scale of 10 cm to 20 cm grid points and shows that within a rockface are many micro-habitats, reflected in the pattern of occurrence of different lichen species at this scale. The smallest scale of analysis is a ‘nearest-neighbour’ analysis, which indicates that lichens sample their environment differently and have different ecological strategies. These processes ultimately act together to give the observed pattern in the community.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

The Distribution of Hospitality Services in Uyo Urban, Nigeria

2018 ◽  
pp. 137-150
Author(s):  
Violet Bassey Eneyo
Keyword(s):  
Data Collection ◽  
Global Positioning System ◽  
Central Area ◽  
Positioning System ◽  
Nearest Neighbour ◽  
Global Positioning ◽  
Neighbour Analysis

This paper examines the distribution of hospitality services in Uyo Urban, Nigeria. GIS method was the primary tool used for data collection. A global positioning system (GPS) Garmin 60 model was used in tracking the location of 102 hospitality services in the study area. One hypothesis was stated and tested using the nearest neighbour analysis. The finding shows evidence of clustering of the various hospitality services. The tested hypothesis further indicated that hospitality services clustered in areas that guarantee a sustainable level of patronage to maximize profit. Thus, the hospitality services clustered in selected streets in the metropolis while limited numbers were found outside the city’s central area.

Spatial Patterns of Urban Innovation and Their Evolution from Perspectives of Capacity and Structure: Taking Shenzhen as an Example

2021 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Erjie Hu ◽  
Di Hu ◽  
Handong He
Keyword(s):  
Spatial Distribution ◽  
Spatial Pattern ◽  
Distribution Pattern ◽  
Spatial Patterns ◽  
Spillover Effect ◽  
Tertiary Industry ◽  
Innovation Capacity ◽  
Structure Index ◽  
Secondary Industry ◽  
Spatial Spillover Effect

Innovation is a key factor for a country’s overall national strength and core competitiveness. The spatial pattern of innovation reflects the regional differences of innovation development, which can provide guidance for the regional allocation of innovation resources. Most studies on the spatial pattern of innovation are at urban and above spatial scale, but studies at urban internal scale are insufficient. The precision and index of the spatial pattern of innovation in the city needs to be improved. This study proposes to divide spatial units based on geographic coordinates of patents, designs the innovation capability and innovation structure index of a spatial unit and their calculation methods, and then reveals the spatial patterns of innovation and their evolutionary characteristics in Shenzhen during 2000–2018. The results show that: (1) The pattern of innovation capacity of secondary industry exhibited a pronounced spatial spillover effect with a positive spatial correlation. The innovation capacity and innovation structure index of the secondary industry evolved in a similar manner; i.e., they gradually extended from the southwest area to the north over time, forming a tree-like distribution pattern with the central part of the southwest area as the “root” and the northwest and northeast areas as the “canopy”. (2) The pattern of innovation capacity of tertiary industry also had a significant spatial spillover effect with a positive spatial correlation. There were differences between the evolutions of innovation capacity and innovation structure index of tertiary industry. Specifically, its innovation capacity presented a triangular spatial distribution pattern with three groups in the central and eastern parts of the southwest area and the south-eastern part of the northwest area as the vertices, while its innovative structure showed a radial spatial distribution pattern with the southwestern part of the southwest area as the source and a gradually sparse distribution toward the northeast. (3) There were differences between the evolution modes of secondary and tertiary industries. Areas with high innovation capacity in the secondary industry tended to be more balanced, while areas with high innovation capacity in the tertiary industry did not necessarily have a balanced innovation structure. Through the method designed in this paper, the spatial pattern of urban innovation can be more precise and comprehensive revealed, and provide useful references for the development of urban innovation.

scholarly journals Extension of mathematical background for Nearest Neighbour Analysis in three-dimensional space

2013 ◽  
Vol 11 ◽  
pp. 25-36
Author(s):  
Eva Stopková
Keyword(s):  
Dimensional Space ◽  
Average Distance ◽  
Three Dimensional ◽  
Nearest Neighbour ◽  
Mathematical Background ◽  
Area Of Interest ◽  
Nearest Neighbours ◽  
Anisotropic Function ◽  
Neighbour Analysis ◽  
Three Dimensional Space

Proceeding deals with development and testing of the module for GRASS GIS [1], based on Nearest Neighbour Analysis. This method can be useful for assessing whether points located in area of interest are distributed randomly, in clusters or separately. The main principle of the method consists of comparing observed average distance between the nearest neighbours r A to average distance between the nearest neighbours r E that is expected in case of randomly distributed points. The result should be statistically tested. The method for two- or three-dimensional space differs in way how to compute r E . Proceeding also describes extension of mathematical background deriving standard deviation of r E , needed in statistical test of analysis result. As disposition of phenomena (e.g. distribution of birds’ nests or plant species) and test results suggest, anisotropic function would repre- sent relationships between points in three-dimensional space better than isotropic function that was used in this work.

scholarly journals Distribusi Keruangan Penanaman Modal di Kabupaten Bantul

2020 ◽  
Vol 34 (1) ◽  
pp. 25
Author(s):  
Aprilia Prasmudika Sighita ◽  
Bambang Sriyanto Eko Prakoso
Keyword(s):  
Economic Growth ◽  
Spatial Distribution ◽  
Descriptive Analysis ◽  
Analytical Techniques ◽  
Nearest Neighbour ◽  
Investment Climate ◽  
R Value ◽  
Investment Firm ◽  
Neighbour Analysis ◽  
Selection Of

Kabupaten Bantul meraih prestasi tingkat nasional di tahun 2008 yakni memperoleh penghargaan dalam KPPOD Award. Penghargaan yang diperoleh menjadi awal yang baik bagi Kabupaten Bantul dalam memperbaiki iklim penanaman modal. Tujuan dari penelitian ini untuk mengidentifikasi dan menganalisis distribusi keruangan penanaman modal dan pengaruh karakteristik wilayah terhadap pemilihan lokasi penanaman modal. Teknik analisis yang digunakan adalah analisis deskriptif, analisis tetangga terdekat, dan analisis regresi berganda. Berdasarkan hasil kajian dapat disimpulkan bahwa distribusi keruangan penanaman modal di Kabupaten Bantul terdistribusi di 10 kecamatan, sedangkan 7 kecamatan lainnya belum menjadi destinasi penanaman modal. Untuk lokasi perusahaan penanaman modal membentuk pola dispersed atau merata dengan nilai R sebesar 5,920887 (R>1). Sebagian dari lokasi penanaman modal berada di tepi jalan raya. Pemilihan lokasi penanaman modal di Kabupaten Bantul dipengaruhi oleh faktor daya tarik karakteristik wilayah seperti pertumbuhan ekonomi dan jumlah objek wisata. Bantul Regency won the national award of KPPOD in 2008. That award was a good commencement to improve Bantul Regency’s investment climate. The aims of this research were to identify and analyse the spatial distribution of investment and the effect of regional characteristics on the selection of investment site. The analytical techniques used in the research are descriptive analysis, nearest neighbour analysis, and multiple regression analysis. Based on the analysis, it can be concluded that distribution of investment in Bantul Regency distributed in 10 sub-districts, while 7 others are not yet be destination of investment. For the location of investment firms forms a dispersed pattern with R value 5,920887 (R>1). Some investment firm are located on the edge of highway. The selection of investment sites in Bantul regency is affected by the attraction factors of the region characteristics such as economic growth and number of tourism objects.   

scholarly journals Spatial Patterns of Carbon Monoxide Distribution to Traffic Jam in East Jakarta

2021 ◽  
Vol 940 (1) ◽  
pp. 012010
Author(s):  
A W Ramadhan ◽  
A Wibowo ◽  
R Saraswati
Keyword(s):  
Spatial Pattern ◽  
Distribution Pattern ◽  
The Other ◽  
Emission Model ◽  
Analysis Method ◽  
Traffic Jam ◽  
High Concentrations ◽  
Mobile Emission ◽  
Spatial Analysis Method ◽  
The Mathematical Model

Abstract The rapid growth of cities will certainly also increase traffic jams and emissions in the air. This study aims to analyze the increase in car volume and the CO distribution pattern in East Jakarta. Data for traffic jam patterns were recorded based on Google Maps on weekdays in the morning and evening. The spatial analysis method used to find the CO distribution pattern is the IDW interpolation, and the mathematical model calculates the moving emission based on the distance travelled (VKT). The spatial pattern of CO distribution in 2020 was scattered with high concentrations in Pasar Rebo, Ciracas, Cipayung, Kramat Jati, and Makasar Districts, with CO levels above 4,500 ppm. The spatial pattern of CO distribution from the mobile emission model differs from the air station IDW interpolation. The CO distribution pattern from the mobile emission model is very concentrated in Makassar, and Kramat Jati District was 6,740.91 tons/year. The result concluded that the increase in vehicle volume is not related to the distribution of the CO model from air station IDW interpolation, and the other hand, the congestion pattern was related to the distribution pattern of the CO model from vehicles from the level of congestion.

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