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• Monitoring temperature patterns at selected world heritage sites in Egypt using high resolution WorldClim data

  42-58

  Viranch N. Dave

  Views:

  476

  Long term temperature patterns helps in assessing changes in the climatic conditions of an area and climatic changes poses a major challenge to the world heritage sites whether it is natural or cultural. Therefore in this study using maximum and minimum temperature data for the period 1960-2021 downloaded from WorldClim 2.1 calculation of mean temperature is done in QGIS environment for the selected UNESCO world heritage sites of Arab Republic of Egypt. WorldClim 2.1 provides finer resolution gridded data downscaled from Climate Research Unit. Trend analysis using linear regression and Mann-Kendall method and Sen’s Slope estimate is used to understand the patterns of mean temperature at all the selected sites. The study reveals that mean temperature at all the selected sites is increasing but since 1990 the sites which are located geographically in lower Egypt are witnessing rapid increase in mean temperature compared to the sites located in upper Egypt which historically witnessed more temperature due to its geographical milieu. This study can help in stimulating the utility of geospatial data in understanding the changes in climatic parameters in relation to world heritage sites. Moreover it can serve as foundation upon which detailed longitudinal site specific investigation can be done.

• Prediction of industrial land use using linear regression and mola techniques: A Case Study of Siltara Industrial belt

  59-70

  Sheikh Mustak

  Naresh Baghmar

  Sudhir Singh

  Views:

  270

  The Siltara Industrial belt is an important industrial pocket of Chattisgarh state located in the northern part of the Raipur city, which is rapidly growing. In this process spatial, cultural, political and administrative factors are controlling its rate, direction and pattern. The Simple Linear Regression (SLR) and Multi-Objective Land Allocation (MOLA) techniques, which are embedded in SPSS and Idrisi Kilimanjaro software respectively, and have been used for the estimation of future scenario of the industrial growth. In this model, a suitable platform has been prepared in which future industrialization has been estimated by integrating physical, social, cultural factors and land acquisition policy. In this article, results have revealed that industrialization has occurred very fast during last one decade. The industrial land was 6.15 km2 in 2001 and 18.725 km2 in 2011 and estimated as 31.30 km2 in 2021 and 43.87 km2 in 2031 using SLR. The rapid industrial growth is very critical issues for agrarian society and fresh environment. This model very accurately estimating (overall accuracy=95.39%, Kno=97.24%, agreement=98.63 %) the future growth of industrial land. This work will be useful to the planners and policy makers of private and government sectors to regulate the sustainable planning practices and smart decision-making.