Spatial planning decisions have to consider complex networks of influences in order to deliver sustainable results. For example, the construction of a retirement home may cause older people to migrate from the aging single-family home areas (SFHA) in the surrounding region. The answer to the question if and where to migrate is an individual human decision and thus depends not only on the surrounding circumstances and influences but also on the experiences and knowledge every individual has. Therefore, the analysis has to incorporate data on the influences as well as data on the individual subjects as well. There are several approaches in the field of well-being and quality of life research. The approaches are mostly specific for the respective area and research questions and sometimes based on existing data, e.g. [1], and do not always deliver the necessary information. Thus, active data acquisition has to be considered to gain information for influences on the subjective well-being of individuals. The understanding of the rural village as a system of interdependencies is mostly missing in the practical spatial planning process in rural areas. Visvizi and Lytras formalized key research questions for the field of smart villages. They address mapping, planning and managing as well as well-being as some of the most important research areas for smart villages [2]. The work presented here aims to create a model for the study and visualization of interdependencies, in particular between supply facilities, demographics, settlement structures and individual wellbeing in rural areas in Germany and answer the following research questions: • Is the construction of a retirement home linked to the migration of older peo-ple (RQ1)? • Is the designation of new zonings for single-family homes in spatial planning documents linked to the migration of younger people (RQ2)? • How is the individual well-being of SFHA’s residents affected by influencing factors like communication infrastructure, green space, leisure facilities or supply infrastructure (RQ3)? The generation of a knowledge graph focusing on demographic processes in rural areas in Germany is a promising step to build a model which can answer these research questions. Approaches in the field of spatial planning do already exist, for example the World Avatar project [3]. Knowledge graphs have also already been used in the field of smart cities[4]. Stochl et al. use a quantitative approach using network analysis to find the most central elements based on four surveys with 47578 participants to identify significant aspects of psychological wellbeing in different cohorts[5]. Network analysis is a valuable tool in many areas of research [6-7]. It has also already been used for the modeling and analysis of migration. Tranos et al. estimated an international migration model for OECD countries based on gravity models using conventional econometric approaches like panel data regressions and network-based regression techniques like multivariate regression quadratic assignment procedures [8]. Charyyev and Gunes analyzed the national migration network between counties and states in the US between 2000 and 2015. The analyses incorporated different time windows on county and state level and were based on IRS records. The analysis also used a gravity model [9]. Garas et al. used the complex network perspective to analyze the interdependencies between migration and the foreign direct investments (FDI). They also used a gravity model enriched with complexnetwork effects [10]. Références [1] F. O. Ostermann, ‘Linking Geosocial Sensing with the Socio-Demographic Fabric of Smart Cities’, ISPRS International Journal of Geo-Information, vol. 10, no. 2, Art. no.2, Feb. 2021, doi: 10.3390/ijgi10020052. [2] A. Visvizi and M. Lytras, ‘It’s Not a Fad: Smart Cities and Smart Villages Research in European and Global Contexts’, Sustainability, 2018, doi: 10.3390/SU10082727. [3] J. Akroyd, S. Mosbach, A. Bhave, and M. Kraft, ‘Universal Digital Twin - A Dynamic Knowledge Graph’, Data-Centric Engineering, vol. 2, ed 2021, doi:10.1017/dce.2021.10. [4] H. Santos, V. Dantas, V. Furtado, P. Pinheiro, and D. L. McGuinness, ‘From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards’, in The Semantic Web, Cham, 2017, pp. 94–108. doi: 10.1007/978-3-319-58451-5_7. [5] J. Stochl, E. Soneson, A. P. Wagner, G. M. Khandaker, I. Goodyer, and P. B. Jones, ‘Identifying key targets for interventions to improve psychological wellbeing: replicable results from four UK cohorts’, Psychological Medicine, vol. 49, no. 14, pp. 2389–2396, Oct. 2019, doi: 10.1017/S0033291718003288. [6] Ahmed Ibnoulouafi et al J. Stat. Mech. (2018) 07340 [7] Messadi, M., Cherifi, H., & Bessaid, A. (2021). Segmentation and ABCD rule extraction for skin tumors Classification. In arXiv preprint arXiv:2106.04372, 2021 [8] E. Tranos, M. Gheasi, and P. Nijkamp, ‘International Migration: A Global Complex Network’, Environment and Planning B: Planning and Design, vol. 42, pp. 4–22, Feb. 2015, doi: 10.1068/b39042. [9] B. Charyyev and M. H. Gunes, ‘Complex network of United States migration’, Computational Social Networks, vol. 6, no. 1, p. 1, Jan. 2019, doi: 10.1186/s40649-019-0061-6. [10] A. Garas, A. Lapatinas, and K. Poulios, ‘The relation between migration and FDI in the OECD from a complex network perspective’, Nov. 18, 2016. https://mpra.ub.unimuenchen.de/75134/ (accessed Apr. 11, 2022).