Identifying spatial context and associated factors of poverty using spatial statistics and gis

Lal Samarakoon; Romanee Thongdara; Rajendra P. Shresta; S. L.R. Ranamukhaarachchi

Identifying spatial context and associated factors of poverty using spatial statistics and gis

Lal Samarakoon
, Romanee Thongdara
, Rajendra P. Shresta
, S. L.R. Ranamukhaarachchi

Asian Institute of Technology Thailand

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Mapping poverty has become increasingly useful in identifying poverty traps or pockets of deprived communities as well as identifying underlying factors those associate with poverty in a locality. This information assists with the targeting of interventions or recognizing appropriate development projects. The ability to analyze relationships between different data sources in a spatial context using GIS and spatial statistics also provide important insights into possible associated factors of poverty that are not readily accessible in any other means of analysis. This paper investigates the potential of descriptive statistics, GIS, and spatial autocorrelation in identifying poverty association of rural context of northeast Thailand. It was showed that GIS is a useful tool in identifying some of the associated factors related to spatial segregation of poor households and spatial autocorrelation could satisfactorily used in recognizing similarities or dissimilarities among households examined in this work.

Original language	English
Title of host publication	32nd Asian Conference on Remote Sensing 2011, ACRS 2011
Pages	775-779
Number of pages	5
Publication status	Published - 2011
Externally published	Yes
Event	32nd Asian Conference on Remote Sensing 2011, ACRS 2011 - Tapei, Taiwan, Province of China Duration: 3 Oct 2011 → 7 Oct 2011

Publication series

Name	32nd Asian Conference on Remote Sensing 2011, ACRS 2011
Volume	2

Conference

Conference	32nd Asian Conference on Remote Sensing 2011, ACRS 2011
Country/Territory	Taiwan, Province of China
City	Tapei
Period	3/10/11 → 7/10/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 1 No Poverty

Keywords

GIS
Poverty
Spatial statistics
Thailand

Cite this

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title = "Identifying spatial context and associated factors of poverty using spatial statistics and gis",

abstract = "Mapping poverty has become increasingly useful in identifying poverty traps or pockets of deprived communities as well as identifying underlying factors those associate with poverty in a locality. This information assists with the targeting of interventions or recognizing appropriate development projects. The ability to analyze relationships between different data sources in a spatial context using GIS and spatial statistics also provide important insights into possible associated factors of poverty that are not readily accessible in any other means of analysis. This paper investigates the potential of descriptive statistics, GIS, and spatial autocorrelation in identifying poverty association of rural context of northeast Thailand. It was showed that GIS is a useful tool in identifying some of the associated factors related to spatial segregation of poor households and spatial autocorrelation could satisfactorily used in recognizing similarities or dissimilarities among households examined in this work.",

keywords = "GIS, Poverty, Spatial statistics, Thailand",

author = "Lal Samarakoon and Romanee Thongdara and Shresta, \{Rajendra P.\} and Ranamukhaarachchi, \{S. L.R.\}",

year = "2011",

language = "English",

isbn = "9781618394972",

series = "32nd Asian Conference on Remote Sensing 2011, ACRS 2011",

pages = "775--779",

booktitle = "32nd Asian Conference on Remote Sensing 2011, ACRS 2011",

note = "32nd Asian Conference on Remote Sensing 2011, ACRS 2011 ; Conference date: 03-10-2011 Through 07-10-2011",

}

Samarakoon, L, Thongdara, R, Shresta, RP & Ranamukhaarachchi, SLR 2011, Identifying spatial context and associated factors of poverty using spatial statistics and gis. in 32nd Asian Conference on Remote Sensing 2011, ACRS 2011. 32nd Asian Conference on Remote Sensing 2011, ACRS 2011, vol. 2, pp. 775-779, 32nd Asian Conference on Remote Sensing 2011, ACRS 2011, Tapei, Taiwan, Province of China, 3/10/11.

Identifying spatial context and associated factors of poverty using spatial statistics and gis. / Samarakoon, Lal; Thongdara, Romanee; Shresta, Rajendra P. et al.
32nd Asian Conference on Remote Sensing 2011, ACRS 2011. 2011. p. 775-779 (32nd Asian Conference on Remote Sensing 2011, ACRS 2011; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Identifying spatial context and associated factors of poverty using spatial statistics and gis

AU - Samarakoon, Lal

AU - Thongdara, Romanee

AU - Shresta, Rajendra P.

AU - Ranamukhaarachchi, S. L.R.

PY - 2011

Y1 - 2011

N2 - Mapping poverty has become increasingly useful in identifying poverty traps or pockets of deprived communities as well as identifying underlying factors those associate with poverty in a locality. This information assists with the targeting of interventions or recognizing appropriate development projects. The ability to analyze relationships between different data sources in a spatial context using GIS and spatial statistics also provide important insights into possible associated factors of poverty that are not readily accessible in any other means of analysis. This paper investigates the potential of descriptive statistics, GIS, and spatial autocorrelation in identifying poverty association of rural context of northeast Thailand. It was showed that GIS is a useful tool in identifying some of the associated factors related to spatial segregation of poor households and spatial autocorrelation could satisfactorily used in recognizing similarities or dissimilarities among households examined in this work.

AB - Mapping poverty has become increasingly useful in identifying poverty traps or pockets of deprived communities as well as identifying underlying factors those associate with poverty in a locality. This information assists with the targeting of interventions or recognizing appropriate development projects. The ability to analyze relationships between different data sources in a spatial context using GIS and spatial statistics also provide important insights into possible associated factors of poverty that are not readily accessible in any other means of analysis. This paper investigates the potential of descriptive statistics, GIS, and spatial autocorrelation in identifying poverty association of rural context of northeast Thailand. It was showed that GIS is a useful tool in identifying some of the associated factors related to spatial segregation of poor households and spatial autocorrelation could satisfactorily used in recognizing similarities or dissimilarities among households examined in this work.

KW - GIS

KW - Poverty

KW - Spatial statistics

KW - Thailand

UR - https://www.scopus.com/pages/publications/84865689782

M3 - Conference contribution

AN - SCOPUS:84865689782

SN - 9781618394972

T3 - 32nd Asian Conference on Remote Sensing 2011, ACRS 2011

SP - 775

EP - 779

BT - 32nd Asian Conference on Remote Sensing 2011, ACRS 2011

T2 - 32nd Asian Conference on Remote Sensing 2011, ACRS 2011

Y2 - 3 October 2011 through 7 October 2011

ER -

Identifying spatial context and associated factors of poverty using spatial statistics and gis

Abstract

Publication series

Conference

UN SDGs

Keywords

Other files and links

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Cite this