Introduction to Health Geospatial Visualisation

Dr. Nitin Tripathi Professor Asian Institute of Technology

• Risk Maps • Ring Maps • Habitat Maps • Hotspot Maps • Simulation/ dynamic Maps • Context based

Definition A map that uses different colors to smoothly geo-visualize risk level. Aim to geo-visualize surface of risk from infectious diseases. Usefulness delineating spatial clusters of infectious disease;helping public health agencies to respond more effectively; helping epidemiologists to reveal area pattern, track the sources ofdiseases and movements of contagions. Procedure uses spatial density estimation to transform health data into a surface of disease risk.

 DF/DHF Risk Zonation Index (DRZI):  ( )DRZI = FVBCi + FRCi + FLU Ci + FECi + FPDCi / W1 Where; F is factor weight of village buffer (VB), rainfall (R), land use (LU), elevation (E), and population density (PD) Ci is class weight of sub-criteria.

 DF/DHF risks zones, Chachoengsao Province, Thailand

Kanchanaburi is Thailand's third largest province. It covers an area of 19,486 square kilometers most of which is forested mountains. 130 kilometers from Bangkok, Kanchanaburi is now composed of 13 districts (Amphoes), and 98 Tambons;

N W E S THONG PHA PHUM ; TAK Sa Phan Lao View3 Huay Som Jit ; ; Village's malaria affected Sa Ha Korn Ni Kom ; Road SANGK# HLA BURI UTHAI THANI Hydrology ;; CHAINAT Malaria risk zone map ;; Song Ngan # High ## Low Moderate ## # SUPHAN BURI # # ## # # ## Sa Ha Korn # THON#G PHA# PH####U# #M # ## N#O##N#G####PR#U# E # # ## SI SAWAT ## # ################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################ # ######## ## # LAO KHWAN## ## # SAI YOK DMAUNEMAANGKBHKOAAPMNHCLTTHOIAHAIAPNHHMAUABUANUIAORKNTMIRHGAATCHMHUAAAKONA### # ## 5 0 5 Kilometers # # # # # # ## # MYANMAR # # # ## RATCHABURI

 Definition A set of ring shaped maps, where each ring displays temporal ➢ dimension of data.  Use ➢ to geo-visualize health related data for public in an active way.  Goal better than helping people understand public health data far with ➢ tables of numbers; ➢ displaying spatial and temporal information of health data in an effective way.

Ring Map Ring map visualization has been explored. A ring map shows multiple attribute datasets as separate rings of information surrounding a base map of a particular geographic region of interest.

Ring Mapping Out of the B.I. sampled villages in districts 6501 to 6509, 0, 0, 13, 25, 0, 8, 0, 0, and 0% had optimal B.I.; and 15, 0, 12, 25, 0, 8, 17, 0, and 0% had emergency B.I., respectively District. No. Name %age 6501 Muang Phitsanulok 15 6504 Bang Rakam 25 6507 Wat Bot 17

 Definition ➢ Breeding and living habitats of agents carrying vectors/ virus/pathogens/ bacteria  Aim ➢ to geo-visualize habitat of agents/ carrier host of particular disease vectors, pathogens, bacteria in an active way.  Goal ➢ helping people/ officers understand areas where the source of disease is present ➢ So that they can take remedial measures to control and eliminate the root of disease itself as prevention;

Phetchabun Dengue habitat prediction

Dengue risk factors estimation

Dengue risk zonation Geo-spatial analysis on public health data indicated that most of the dengue cases were found in densely populated areas surrounded by dense vegetation. People living in independent houses, having sparse vegetation in surrounding, were found to be less vulnerable.





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Source: Annual Epidemiological Surveillance Report 2011; Bureau of Epidemiology, MO19PH

 Descriptive statistics of TB registration  Global spatial autocorrelation analysis  Local spatial autocorrelation analysis 20

Type of Registration 2004 2005 2006 2007 2008 Grand Total New 2,103 2,243 2,089 1,935 1,952 10,332 Failed 17 17 10 11 7 62 Relapse 45 45 50 43 Treatment after 32 25 36 22 46 229 defaulted 26 141 113 105 126 112 21 Other 131 157 165 122 68 524 Transferred in 121 696 Not specified 1 3 9 9 Grand Total 2,442 2,595 2,485 2,254 10 32 2,230 12,006

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Low - High High - High Low - Low High - Low 26

Note: • 0 < Moran’s Index < 1 : Clustered • Moran’s Index = 0 : Random • -1 < Moran’s Index < 0 : Dispersed 27

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