The project “Remote detection of (de)population processes” was realised as one of the winning solutions to the challenge “Data to better understand the depopulation process” announced by the United Nations Development Program (UNDP) and the UN Population Fund (UNFPA) in Serbia. The primary idea of the project is the identification and perception of depopulation at various levels of the territorial organization of the Republic of Serbia using data sets based on remote sensing. Our idea is to develop a permanent infrastructure of different data sets and establishment of a unique “user-friendly” environment for identification, observation, and spatio-temporal detection of the depopulation process in Serbia.
The realization is dedicated to the creation of an online service that enables further and more detailed research of depopulation and related processes and phenomena, creating guidelines for development planning purposes, but also general information of the population issue in Serbia. A set of 12 indicators with an interactive cartographic interface is available for the users. There is a possibility of choosing the spatial and temporal components to get essential information about the condition and changes in population processes in Serbia. Additionally, for the advanced users, provided indicators are available for download including information about data sources, methodological framework, and technical guidelines useful for further research.
Remote sensing (detection) includes a set of methods that enable the collection of information without direct, physical contact with the observed phenomenon or object (satellite, aerial imagery, etc.), by measuring reflected and emitted radiation, and further processing and analysis of such information. Geospatial data generated by remote sensing methods have a wide range of applications in various fields. In terms of phenomena and processes related to the population are: data on the spatial distribution of the population, built-up area, morphometric features of the terrain, infrastructural elements, population, and economic activity measured by the emitted light from the Earth's surface, etc. Such data are characterised by a high level of spatial and temporal detail, opening up new possibilities for quantitative and qualitative analysis.
The application of remote sensing data in the study of (de)population processes enables to overcome the limitations and supplement traditional data sources (e.g. Census). The advantages, as spatially and temporally “sensitive” data, are the possibilities of continuous monitoring of population changes. Using satellite images and other remote sensing data, it is possible to monitor population dynamics with a spatial resolution of several hundred meters (e.g. fields of 250 x 250 m) at the level of settlements and municipalities. For this purpose, a set of 12 indicators has been created that directly or indirectly indicate the depopulation process, by using one or overlapping several different data types to enable the identification, visualisation, and interpretation of (de)population changes.
Some of the proposed indicators are well known in science, and they are used to observe and perceive population changes (Population Change Index, Population Density) as well as its spatial manifestations. Others are non-typical and and presented on this platform in order to detect the causes of depopulation and spatial transformation under the population changes (nighttime lights, seasonal settling, distribution of the population by hypsometric zones, accessibility, land cover changes, etc.). To facilitate the overview and understanding of provided indicators, they are classified into four groups:
<aside> 🔗 A list of indicators can be seen here. Technical documentation with a methodological framework for indicators (file Meta) is available here. Download indicators as GeoTIFF, Shapefile, and/or TXT file here.
Demographic processes in Serbia have traditionally been expressed through quantitative changes registered in the inter-census period. Such an approach lacked the possibility of observing at the higher resolution level, precise identification of spatial phenomena and changes, crossing with other types of data that would indicate the structure of the process and indicate its dynamics. On the other hand, the introduction of alternative data sources minimizes the generalisation, enables observation at the micro-level (lower than statistical and administrative spatial units), and provides insight into microdata that can be added as an attribute to the noticed phenomenon and process. In this sense, the advantage of the data sets presented here, characterized as an alternative, is reflected.