Pitch and Flat Roof Factors’ Association with Spatiotemporal Patterns of Dengue Disease Analysed Using Pan-Sharpened Worldview 2 Imagery

ISPRS International Journal of Geo-Information

Fedri Ruluwedrata Rinawan²  Ryutaro Tateishi²  Ardini Saptaningsih Raksanagara¹  Dwi Agustian³  Bayan Alsaaideh²  Yessika Adelwin Natalia³  Ahyani Raksanagara⁴ 

[1] Department of Public Health, Faculty of Medicine, Padjadjaran University, Jl. Eyckman No. 38, Bandung 40161, Indonesia; E-Mail:;Center for Environmental Remote Sensing, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba-shi 263-8522, Japan; E-Mails:;Department of Epidemiology and Biostatistics, Padjadjaran University, Jl. Eyckman No. 38, Bandung 40161, Indonesia; E-Mails:;Bandung City Health Service, Jl. Supratman No. 73, Bandung, West Java 40114, Indonesia; E-Mail:

关键词: dengue disease incidence;    address-approach-based data;    Getis-Ord score;    segmentation;    Supervised Minimum Distance;    ordinary least squares (OLS);    geographically weighted regression (GWR);   

DOI  :  10.3390/ijgi4042586

来源: mdpi

【 摘 要 】

Dengue disease incidence is related with the construction of a house roof, which is an Aedes mosquito habitat. This study was conducted to classify pitch roof (PR) and flat roof (FR) surfaces using pan-sharpened Worldview 2 to identify dengue disease patterns (DDPs) and their association with DDP. A Supervised Minimum Distance classifier was applied to 653 training data from image object segmentations: PR (81 polygons), FR (50), and non-roof (NR) class (522). Ground validation of 272 pixels (52 for PR, 51 for FR, and 169 for NR) was done using a global positioning system (GPS) tool. Getis-Ord score pattern analysis was applied to 1154 dengue disease incidence with address-approach-based data with weighted temporal value of 28 days within a 1194 m spatial radius. We used ordinary least squares (OLS) and geographically weighted regression (GWR) to assess spatial association. Our findings showed 70.59% overall accuracy with a 0.51 Kappa coefficient of the roof classification images. Results show that DDPs were found in hotspot, random, and dispersed patterns. Smaller PR size and larger FR size showed some association with increasing DDP into more clusters (OLS: PR value = −0.27; FR = 0.04; R² = 0.076; GWR: R² = 0.76). The associations in hotspot patterns are stronger than in other patterns (GWR: R² in hotspot = 0.39, random = 0.37, dispersed = 0.23).

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.