Open AccessAnalysis of population dynamics of the regional unit of Chania using remote sensing and census data
Author(s) -
Paschalina PAPANIKOLAOU,
Terpsichori MITSI
Publication year - 2020
Publication title -
european journal of geography/european journal of geography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.285
H-Index - 9
eISSN - 2410-7433
pISSN - 1792-1341
DOI - 10.48088/ejg.p.pap.11.4.110.125
Subject(s) - census , urban sprawl , population , geography , unit (ring theory) , index (typography) , gini coefficient , population density , population statistics , statistics , distribution (mathematics) , cartography , demography , urban planning , computer science , mathematics , inequality , ecology , economic inequality , sociology , mathematical analysis , mathematics education , world wide web , biology
Information about the size and the evolution of a place’s population has been sought from ancient times. That kind of information can be secured by periodic census enumerations. The Hellenic Statistical Authority provides data about the population and social conditions, also economical indices for each economic sector and the industrial trade. The study area, the regional unit of Chania, is in the island of Crete and the population resides mostly in the lowlands. In order to study the density, distribution and evolution of the population, many quantitative geographical methods were used, such as the Location Quotient (LQ), the Coefficient of Specialization (CS), the Coefficient of Localization (CL) and the Gini – Hirschman Index. The dataset chosen to execute the models, is derived from the Hellenic Statistical Authority’s website, for the years 2001 and 2011, the most recent census available data and is free of charge. To examine the distribution and the density of the population in accordance with the urban sprawl in the area the results were correlated with remotely sensed image. To do so, the Built – up index was calculated and for each municipality in the county and the mean value was used in a linear regression model with the population. In conclusion, the analysis combines the results of the quantitative methods for the productive sectors, the population density and growth with the urban sprawl, to examine the way the population of the county evolved.