Socio-Agricultural Correlation and Regionalization: A Case of the Districts of Pakistan
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Keywords

Multivariate analysis, planning and development, regionalization, socio-agriculture relationship, uneven distribution.

How to Cite

Razzaq Ahmed, Khalida Mahmood, & Anila Kausar. (2021). Socio-Agricultural Correlation and Regionalization: A Case of the Districts of Pakistan. Journal of Basic & Applied Sciences, 10, 7–19. https://doi.org/10.6000/1927-5129.2014.10.02

Abstract

The main economic activity of a large segment of population in Pakistan is agriculture. The contrasting pattern of topography and uneven distribution of resources create a wide difference in socio-agricultural relationship among the different administrative districts of the country. The study aims to investigate the correlation based on a number of variables extracted from different sectors of Pakistan’s agriculture and social infrastructure. In order to study the regionalization multivariate analysis has been done for hundred districts of Pakistan. The results produced, show sharp variation of regional disparity among the different districts of Pakistan. A clear cut longitudinal east-west divide is visible from the outcome of the study. The provinces of Punjab and Sindh, consisting of fertile plains of river Indus and its tributaries stand out with better socio-agricultural correlation. The western provinces of Khyber Pakhtoonkhwa and Balochistan, surrounded by mountains and plateau depict a deprived scene in terms of socio-agricultural well-being. In Pakistan districts are very vital for resource planning and development. These administrative units have a mix of both rural and urban activities that is why this study becomes more significant for future district planning decisions.

https://doi.org/10.6000/1927-5129.2014.10.02
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References

GoP. Population and housing census of Pakistan, 1998. Census Bulletin -1, Population Census Organization, Statistics Division, Government of Pakistan: Islamabd.

GoP. Pakistan statistical year book 2011-12. Federal Bureau of Statistics, Government of Pakistan: Islamabad.

GoP. Agriculture statistics of Pakistan 2008-09. Ministry of Food and Agriculture (Economic Wing), Governemnt of Pakistan: Islamabad.

Jamal H and Malik S. Shifting pattern in developmental rank ordering: A case of the districts of Sindh province. The Pakistan Development Review 1998; vol. XXVII (2), pp. 159-182.

Mahmood K and Ahmed R. Inter-regional migration and urban growth: A case study of Pakistan. Pakistan Perspective 2010; Vol. 15(2), pp.157-177.

Lenon B J and Cleves PG. Techniques and fieldwork in Geography, Bell and Hyman 1987: London.

Rogerson PA. Statistical methods for Geography. Sage Publication 2010: London.

Manly BFJ. Multivariate statistical methods: A primer. Chapman and Hall 1993: London.

Berry BJL. Basic patterns of economic development in Ginsburg N. ed., Essays on Geography and EconomicDevelopment. University of Chicago. Department of Geography, 1961. Research Paper no. 62: 78-108.

Mahmood K. Changes in the spatial structure of administrative areas in Pakistan: A geographical evaluation. Unpublished Ph. D. dissertation, Department of Geography, University of Karachi 2003.

Ahmed R. Economic regions of Pakistan: An integrated geographical approach. Unpublished Ph. D. dissertation, Department of Geography, University of Karachi 2013.

Ahmad QS. Indian cities: Characteristics and correlates. University of Chicago Press, Department of Geography 1965; Research Paper no. 102.

Soares JO, Marques MML and Monteiro CMF. A multivariate methodology to uncover regional disparity: A contribution to improve European Union and governmental decisions. European Journal of Operational Research 2003; vol. 145: 121-135. http://dx.doi.org/10.1016/S0377-2217(02)00146-7

Campo C, Monteiro CMF and Soares JO. The European regional policy and the socio-economic diversity of European regions: A multivariate analysis. European Journal of Operational Research 2008; vol. 187: 600-612. http://dx.doi.org/10.1016/j.ejor.2007.03.024

Zeng SX, Liu HC, Tam CM and Shao YK. Cluster analysis for studying industrial sustainability: An empirical study in Shanghai. Journal of Cleaner Production 2008; vol. 16: 1090-1097. http://dx.doi.org/10.1016/j.jclepro.2007.06.004

Zhang Q, Wu F, Wang L, Yuan L and Zhao L. Application of PCA integrated with CA and GIS in eco-economic regionalization of Chinese Loess Plateau. Ecological Economics 2011; vol. 70: 1051-1056. http://dx.doi.org/10.1016/j.ecolecon.2011.01.016

Ward JH. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 1963; vol. 58(301): 236-244. http://dx.doi.org/10.1080/01621459.1963.10500845

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