The Ratio of the Land Consumption Rate to the Population Growth Rate: A Framework for the Achievement of the Spatiotemporal Pattern in Poland and Lithuania

Abstract

Indicator 11.3. 1 of the 2030 sustainable development goals (SDG) 11, i.e., the ratio of the land use to the population growth rate, is currently classified by the United Nations as a Tier II indicator, as there is a globally-accepted methodology for its calculation, but the data are not available, nor are not regularly updated. Recently, the increased availability of remotely sensed data and products allows not only for the calculation of the SDG 11.3. 1, but also for its monitoring at different levels of detail. That is why this study aims to address the interrelationships between population development and land use changes in Poland and Lithuania, two neighboring countries in Central and Eastern Europe, using the publicly available remotely sensed products, CORINE land cover and GHS-POP. The paper introduces a map modelling process that starts with data transformation through GIS analyses and results in the geovisualisation of the LCRPGR (land use efficiency), the PGR (population growth rate), and the LCR (land use rate). We investigated the spatial patterns of the index values by utilizing hotspot analyses, autocorrelations, and outlier analyses. The results show how the indicators’ values were concentrated in both countries; the average value of SDG 11.3. 1, from 2000 to 2018 in Poland amounted to 0.115 and, in Lithuania, to −0.054. The average population growth ratio (PGR) in Poland equaled 0.0132, and in Lithuania, it was −0.0067, while the average land consumption ratios (LCRs) were 0.0462 and 0.0067, respectively. Areas with an increase in built-up areas were concentrated mainly on the outskirts of large cities, whereas outliers of the LCRPGR index were mainly caused by the uncertainty of the source data and the way the indicator is interpreted.

Study on Spatial-Temporal Urban Growth and Land Consumption Patterns of Thimphu, Bhutan Using Multi-Temporal Satellite Images

“…Deep learning (DL) techniques have recently been widely incorporated for the classification of remote sensing images, but fewer research have used them for land consumption (LC) i.e the land transformation process which results into the loss of agricultural, natural and semi-natural areas due to the construction of new buildings, the expansion of cities, and the infrastructure in the city [18], [19]. In this study, multi-temporal Landsat images were acquired for the years 1990, 2000, 2013 and 2018.…”

Maps and SDG11: A Complex but Possible Relationship

“…The query was initiated according to the formulation "monitoring SDG" OR "localizing SDG" AND "GIS" OR "geographic information systems" AND "sustainable development goal", in a time-frame 2015-2022, to consider the literature published after the 2030 Agenda formulation. The result of this phase brought the identification of 29 references, then evaluated according to their potential relevance for the research, leading to 7 references [30][31][32][33][34][35][36].…”

Opportunities and Challenges of Remote Sensing, Geospatial Data, and Machine Learning in Obtaining Accessibility and Location Information for Sustainable Development in Indonesia

“…This advancement in technology has also provided us with other forms of data other than traditional tables such as road network maps or public facilities geotagging. This data could also be used to monitor SDGs indicators such as monitoring the ratio of land consumption rate to population growth rate [7], [8] or even monitor the accessibility of public facilities such as school [9] or healthcare [10].…”