Demographic transition in aging neighborhoods: a GIS-based analysis from Germany's countryside

Abstract

The ongoing demographic transition within aging single-family house neighborhoods in Germany poses a significant challenge for municipalities. The scarcity of data and information related to demographic composition and location quality complicates research efforts and the development of adaptive strategies for these residential areas. This issue is particularly pronounced in rural regions where resources for capturing and analyzing demographic trends are limited. To address this gap, we propose a methodology based on geographic information systems. In this approach, municipal population registers serve as a central data source for extracting insights about the residents. We present the findings primarily in the form of maps, as they are intended to be easily comprehensible for urban planners and local government staff. Additionally, we outline the initial steps in establishing a small-scale monitoring system that incorporates demographic indicators as well as reachability estimates. A case study from northern Bavaria is used as an illustration.

Der Generationenwechsel in alternden Einfamilienhausgebieten in Deutschland stellt eine bedeutende Herausforderung für die Gemeinden dar. Die begrenzte Verfügbarkeit von Daten und Informationen über die demografische Situation und Lagequalitäten erschwert die Forschung und die Entwicklung von Anpassungsstrategien für diese Wohnviertel. Die Problematik tritt insbesondere in ländlichen Regionen zu Tage, wo Ressourcen für die Erfassung und Analyse demografischer Dynamiken vergleichsweise gering sind. Um trotz dieser Rahmenbedingungen angemessen den Übergang in alternden Einfamilienhausgebieten gestalten zu können, wird eine methodische Herangehensweise vorgestellt, die auf Geografischen Informationssystemen basiert. Kommunale Bevölkerungsregister fungieren dabei als zentrale Datenquelle zur Extraktion von Informationen über die Bewohner. Die Ergebnisse werden insbesondere in Form von Karten visualisiert, die von Stadtplanern und kommunalen Verwaltungen einfach genutzt und verstanden werden können. Zusätzlich skizzieren wir die ersten Schritte auf dem Weg hin zu einem kleinräumigen Monitoring dieser Wohngebiete unter Einsatz von demografischen Indikatoren und Erreichbarkeitsberechnungen. Zur Veranschaulichung dient eine Fallstudie aus Nordbayern.

Keywords:

• Demographic transition
• Geographic information system (GIS)
• Monitoring
• Municipal population register
• Single-family house areas

1. Introduction

German villages have experienced substantial changes since the 1950s, characterized notably by a remarkable increase in the built-up urban area. This period has seen the emergence of many residential areas comprised of single-family homes (SFH), resulting in approximately 13 million SFHs currently existing across Germany (cf. Deilmann and Lorbeck 2016, Deilmann 2017, p. 3, Statistisches Bundesamt 2023). This housing type is not only prevalent in (sub-)urban settings but is also widespread in rural areas. One distinct characteristic of single-family residential areas in Germany´s countryside is their location on the outskirts of existing villages. The repeated development of new single-family zones contributes to a spatial structure resembling layers of an onion, where the building age decreases as one moves outward. This predominant pattern of spatial and temporal expansion leads to age-related segregation of residents: Homeownership is often acquired between one's late twenties and mid-forties (Palotz 2004, p. 25), resulting in residents of SFH areas generally being of similar ages. The parent generation usually prefers to remain in their homes as they age. Conversely, the generation of adult children typically relocates from their parent's homes, contributing today to an elevated average age of residents in single-family neighborhoods from the 1960s, 1970s, or 1980 (cf. Gans 2018, p. 362).

The ongoing single-family zoning in Germany, coupled with the aging of both the housing stock and its residents, leads to several challenges. This becomes evident, among other things, when considering the options for aging in place, as SFH areas in Germany typically only serve residential purposes. The distance to shopping facilities and other amenities, which are located elsewhere, may be too far for older residents to walk. The demographic shift in SFH also raises questions about the future use of these properties. When parents pass away or relocate to a nursing home, the children may no longer live nearby and opt to sell the inherited property. Coupled with the changing financial environment of a post low-interest-rate phase, this may result in falling property sales prices, prolonged vacancy periods, or even increasing vacancy rates. These risks are particularly evident in rural areas experiencing population decline. In the last census conducted in 2011, a significant prevalence of vacant buildings in Germany´s rural areas was already identified (cf. Deilmann and Lorbeck 2016). In light of this, the results of the current census from 2022 are eagerly awaited. However, as they will not be published until 2024 (Zensus 2022), it is too late to include them in this article.

Adam et al. (2018, p. 1) perceive the German research on demographic transition in SFH areas from the latter half of the twentieth century as being in its early stages. This is in line with the observation by Cividino et al. (2020, p. 1), who found that the causal relationship between urban sprawl and demographic change has not yet been sufficiently researched in the European context. It is therefore not surprising that only limited efforts have been made to date to overcome the technical challenges associated with monitoring for this specific field of application. However, such endeavors are becoming increasingly necessary in view of the problems that are emerging. This is all the more true as demographic trends at the local level can vary widely, including population growth, stagnation, and decline (cf. Schaffert and Höcht 2018a, Wolff et al. 2021). Given the diverse and sometimes conflicting dynamics in neighborhoods, communities, and regions, it is high time to explore effective ways of monitoring demographic transition in residential areas.

The article aims to demonstrate the feasibility of small-scale population analyses in the context of a case study. In particular, the capability of processing and visualizing municipal population data, which has so far been underutilized in municipal planning in rural areas, is being investigated. We apply geographic information systems (GIS) for this purpose. In addition, we present conceptual ideas and initial proposals for monitoring aging neighborhoods in Germany's countryside. In this way, we want to show that fundamental challenges in the development of monitoring architectures and monitoring tools can be overcome.

After introducing the municipality of our case study, we explore how GIS can be used to generate meaningful and new information about demographic change in residential areas. The particular challenges that arise when implementing a regional monitoring approach based on municipal data are explored subsequently. We then look at the location qualities of single-family neighborhoods, as they determine the opportunities for re-use after the generational transition of residents has taken place. Of particular interest to us here is the accessibility of basic services and everyday amenities.

2. Case study area and methodological considerations

Flossenbürg, situated in the rural district of Neustadt an der Waldnaab in northern Bavaria, is a village with around 1700 inhabitants. Located near the border with the Czech Republic, it has a rich history dating back to the year 948, when it was first mentioned. Nevertheless, the village's predominant architectural composition took shape during the twentieth century, particularly in the decades following the Second World War. Today, the village center, which historically served as both the heart of the village's physical layout and its essential functions, now only encompasses a relatively minor portion of the expanded built-up area. The geographical expansion of the village was primarily driven by the construction of SFH but encountered topographical constraints that limit urban land take in a north-western and south-eastern direction. Apart from the impact of the terrain, Flossenbürg exhibits the onion-skin pattern mentioned earlier, which is typical for many countryside towns in Germany (Figure 1).

1 The expansion of Flossenbürg (background data sourced from the Bavarian State Office for Digitalization, Broadband, and Surveying)

The photo in Figure 2 is taken from the south and shows a section of Flossenbürg, specifically the town center (featuring the church and older buildings, which often have an agricultural origin) and adjacent areas. On the right as well as on the front left one can see detached houses built in the second half of the twentieth century as SFH.

2 View over Flossenbürg (Alois Köppl, Gleiritsch 2014; CC BY-SA 3.0)

In Germany, small towns and villages like Flossenbürg often lack the necessary human and financial resources to effectively address demographic transitions with the technical means available today (Schaffert and Höcht 2018b). For this reason, we utilize data that is already collected in these municipalities, even without a specific mandate for neighborhood research, to avoid time-consuming data collection by local users. The basic idea is to visualize this data in a way that furnishes planners with information that places identifiable demographic characteristics within the village in a spatial context, revealing features that would otherwise remain hidden. In addition, we apply workflows for data processing using common desktop GIS software that is established in today's local administrations. The work was carried out using GeoMedia by Hexagon but with functionalities that are also available in ESRI´s ArcGIS Pro or the free and open-source package QGIS. The computation of the indicators and the visualizations that we propose should therefore be practicable in every municipality in Germany.

3. Depicting demographics at the neighborhood level – data and indicators

In Germany, the acquisition of fine-grained demographic data, particularly on single-family neighborhoods, presents formidable challenges. Although the government agencies responsible for statistics operate at both the state and national levels, they do not provide tailored data for monitoring residential areas. Their datasets, organized by administrative levels with municipalities as the smallest units, either lack the spatial resolution needed for neighborhood research or the temporal resolution required for monitoring demographic dynamics. Given the lack of adequate data, some research projects have sought to modify the authoritative data sets in order to use them for small-scale monitoring.

Official statistics provide up-to-date and regionally comparable demographic data, but these are only available for the municipality as a whole and not for its neighborhoods. Consequently, one possible strategy for data improvement is to disaggregate the data. This entails subdividing the municipality-wide population data into spatially smaller units based on parameters such as building density, size, or housing type, using data sets like Germany´s digital landscape model or cadastral data (cf. Eichhorn 2020, Visca et al. 2022). However, this approach is not without limitations, particularly in the context of monitoring individual neighborhoods. This is because the urban fabric and structures act as the initial reference, and the population is allocated according to factors such as the housing type. Consequently, the identification of underutilized SFH remains elusive. Underutilization can occur when children have moved out of their parent's house – a characteristic phenomenon of the demographic transition in aging SFH areas.

An alternative data set employed in research on SFH neighborhoods is provided by the German census, which has an enhanced spatial resolution of just 100 by 100 meters. This data is widely used in neighborhood studies today (e.g. Klein and Müller 2014, Rüttenauer 2018, Moos et al. 2021). Nonetheless, the temporal resolution inherent in the census, taking place approximately every decade, imposes substantial constraints. UN-GGIM (2015, p. 19) draws attention to the need to geocode statistical data, which is often provided without coordinates, in order to achieve greater integration of statistical and other geospatial data. This task should be completed within the timeframe of the 2020 round of censuses. This request is well met in Germany. Still, working with census data that could be nearly a decade old is insufficient to depict the current demographics of single-family zones accurately.

Another approach is the disaggregation of population data to an intra-municipal scale using mobile phone data. This method has been combined with a projection of census 2011 population numbers for the years between the censuses to enhance the temporal resolution (Hadam et al. 2020). Inaccuracies arise here due to the model-based extrapolation of the population figure on the one hand (cf. Schaffert and Höcht 2018b, p. 267) and the disaggregation through mobile phone data on the other, as old people tend to use mobile phones less or at least different from younger people (for further reading Olson et al. 2011).

An alternative data set exists that holds the potential to yield a multitude of insights regarding SFH areas and does not have the aforementioned drawbacks. This dataset is the Einwohnermelderegister (municipal population register), in which every German municipality collects extensive demographic information about its residents in accordance with the Federal Registration Act. It is an official registry where the permanent or temporary residence of individuals is recorded, provided they are subject to registration requirements. This register encompasses demographic attributes like birthdate and gender, along with residency-related specifics such as move-in and move-out dates. Additionally, address information is provided. The handling of this data set, however, poses two distinct challenges that need to be addressed before its potential for granular monitoring can be fully realized. Firstly, the software designed for maintaining the population register is primarily geared towards registration purposes, making it difficult to extract meaningful information about individual residential areas from the raw data. For instance, they generally do not allow for spatial analyses or cartographic visualization (Schaffert and Höcht 2018b). Secondly, the utilization of this register is subject to strict legal regulations on privacy.

However, these challenges are not insurmountable. Initiatives have been launched in recent years that leverage GIS software to unlock the capacities of this database (cf. Schaffert and Höcht 2018a). Using GIS facilitates the mapping of various demographic and statistical aspects onto individual single-family houses as well as entire single-family zones. This methodology can generate valuable insights while upholding individual privacy. As the population register not only contains current data but also records alterations in demographic composition and migration, it even allows the charting of trends over time. In earlier research with a focus on the generational transition within single-family neighborhoods, the population register has therefore served as a key source of information (e.g. De Temple 2005, Planinsek 2011, Hutter 2013). The indicators presented in Table 1 provide fundamental insights into the demographics of SFH areas; they are the ones that have been central in these studies. They all can be derived solely from the population register. Planinsek and Hutter follow a strategy that presupposes the geocoding of the register data, as these contain the addresses but not their coordinates. This spatially explicit approach is of particular importance for monitoring, as the spatial reference allows demographic information to be combined with other spatial characteristics, e.g. on energy consumption of houses or accessibility to local amenities. This is in line with UN GGIM (2015, p. 17), which advocates the establishment of a common spatial reference to be able to link data from different data sources, including statistical data. The data used for geocoding in our use case comes from the German cadastre (Amtliches Liegenschaftskataster-Informationssystem, ALKIS) or from the derived data product, Hauskoordinaten (building coordinates), which is provided by the German surveying authorities.

Table 1 Indicators used in previous studies on the demographic shift in single-family neighborhoods (cf. Hutter 2013)

Topic	Indicator
Age structure	Average age
	Old-age dependency ratio
	Share of foreigners
Natural population development	Change in population number
Population movement	Net migration
	In-migration
	Out-migration
Other	Duration of residence
	Fluctuation

The compilation in Table 1 shows demographic indicators that were applied in previous studies. However, the list does not encompass all demographic indicators that are potentially relevant for monitoring purposes. An illustrative example of an additional meaningful indicator is the share of buildings accommodating two or fewer senior residents (cf. Schaffert et al. 2024). This indicator is intended to contribute to the identification of buildings occupied by empty nesters. Due to privacy concerns, we aggregated this initially address-specific information in Flossenbürg to larger areas, specifically areas with similar-aged housing (Figure 3). The boundaries of these neighborhoods were participatively mapped by local experts. Despite its significance, this indicator has been omitted in earlier research, primarily because previous studies concentrate on examining neighborhood-level patterns instead of delving into the demographic characteristics within specific structures. The indicator ‘Age of the youngest resident per address’ has also been scarcely considered so far. It provides similar information, especially if this resident is elderly.

3 Share of houses with one or two residents being 60 years or older (own source, based on Schaffert 2011; background data sourced from Bavarian State Office for Digitalization, Broadband, and Surveying)

Höger (2018) proposes an approach for monitoring SFH areas that differs from those underlying Table 1, advocating for the additional integration of purchase data. He additionally suggests composite indices instead of using individual indicators to better illustrate relationships between them. For instance, in his opinion, low real estate prices combined with a high age structure indicate a critical profile. Data on real estate prices in Germany are usually not held by rural municipalities but by the appraisal committees for real estate valuation. Working with this data requires an additional logistical step and further considerations for data protection-compliant data transfer. Nevertheless, the committee is once again an official public body, which makes it realistic to combine the data from the population register and transaction data of buildings, provided that the political will to do so can be established.

4. Neighborhood monitoring at the regional level

Housing markets are regional markets, and demographic drivers such as immigration and emigration do not adhere to municipal boundaries (cf. Dieterich et al. 2018). Therefore, demographic information for SFH areas should also be consolidated and made available at the regional level. The municipal population register can portray individual residential areas and is at the same time available in all municipalities, e.g. of a German county. This data set lends itself therefore even to the observation of demographic change in a regional environment. However, consolidating register contents within a regional setting is associated with considerable privacy challenges.

In recent years, however, monitoring infrastructures for municipal applications have emerged, with a cross-sectoral and inter-municipal focus, as exemplified by projects such as KomMonitor (Danowski-Buhren et al. 2022), smartdemography (Kelm et al. 2019), and WebWiKo (Wette and Kramer 2020). These initiatives aim to gather and consolidate data from multiple municipalities and their respective population registers. Additionally, they emphasize the importance of addressing data privacy concerns by working collaboratively with data protection officers from the relevant entities.

These projects employ a similar strategy to safeguard data privacy. They collect data from the population registers of all participating municipalities and consolidate it on a larger geographic scale, such as at the neighborhood or city district level while ensuring that all raw data remains within the jurisdiction of the responsible municipality and does not leave its confines. This approach ensures that no sensitive data is shared beyond the municipality, and only non-sensitive demographic information is transmitted to other administrative bodies or made publicly available. The shared information is organized in a way that prevents individual identification, as it ensures that a sufficient number of individuals with similar age and gender characteristics are grouped within each geographic aggregation unit.

From a technical perspective, the approach outlined in these projects could be readily adapted for monitoring SFH areas. However, the tools developed in these projects were not designed for this specific use case. Instead, their scope encompasses a wide range of topics within urban planning, which is why they do not provide customized indicators (as shown in Table 1). Furthermore, they do not work with reference geometries (geographical aggregation units) that enable the visualization of demographic information specific to individual single-family house areas within their spatial extent. To address this issue, geometric data for smaller regions can be displayed using grid-based data like the INSPIRE Grid. Grids offer the advantage of easy comparability with other municipalities, which can vary in area size (cf. Eichhorn 2020; Figure 4).

4 Net migration in Flossenbürg (own source, based on Schaffert 2011; background data sourced from Bavarian State Office for Digitalization, Broadband, and Surveying)

The map in Figure 4 shows a well-known pattern, namely out-migration from village centers, which frequently follows a functional decline. At the time of the survey, mainly older residents lived here in houses from the time before the Second World War. However, even within a single-family house area of Flossenbürg´s northeastern periphery (white circle), one can find dark red grid cells indicating a double-digit number of people who have moved away within 10 years. This residential area dates back to the 1950s, and the migration might be an indication of a generational change among the residents that is taking place here.

As an alternative to grids, building blocks can be derived from the authoritative Digital Landscape Model in a technically straightforward manner. The Digital Landscape Model is a data product of the official surveying authorities in Germany, which aims to model Germany´s geotopography in a discipline-neutral way within the framework of ATKIS – the Amtliches Topographisch-Kartographisches Informationssystem (Official Topographic Cartographic Information System). Building blocks are used in the KomMonitor project to represent demographic data within municipalities (Schonlau et al. 2019). However, it's important to note that both building blocks, as well as grids, are spatially not identical to SFH areas. Effective monitoring, on the other hand, requires geometries that can accurately map individual single-family house areas in their spatial extent. Particularly within a regional context, zoning plan data therefore becomes a focal point. In Germany, smaller rural municipalities often rely on private planning offices to generate these plans. Traditionally, municipalities received finalized zoning plans in PDF format from them. However, due to regulatory changes like the INSPIRE directive and the need to align zoning information with the INSPIRE land use data schema, there is a growing demand for planning offices to provide additional geometric-semantic data models in vector format. For newly developed areas, the transition to vector-based, object-oriented models is straightforward to implement, as some GIS software allows for the export into such application schemas. However, for older plans – some of them handwritten and dating back decades – the conversion process is time-consuming and costly. A widely accepted compromise for older plans in the INSPIRE context therefore involves creating vector geometries that represent the zoning plan's spatial scope, with some, but limited additional information (such as name and effective date) and a link to the PDF of the zoning plan. These polygons, delineating the spatial extent of the binding land use plan, can serve as a meaningful spatial aggregation unit in a monitoring application as they represent the locations of individual SFH zones. By using these polygons, it becomes possible to examine individual residential areas as well as to provide information on all SFH areas within a region.

The conclusion drawn from the previous considerations is that the establishment of a register-based monitoring system for all SFH areas in a rural district, such as Neustadt an der Waldnaab, and the municipalities comprising it, including, for example, Flossenbürg, is feasible. This is valid from technical, indicator-, and privacy-related perspectives. Nonetheless, additionally, it is essential to gain public and stakeholder support and recognition of the benefits of such a system. SFH are regularly in private ownership, which is why the public authorities often hesitate to view changes in single-family house areas as their responsibility. Research indicates that the maintenance and monitoring of such neighborhoods are not perceived as a priority task (Berndgen-Kaiser et al. 2020, p. 133). Therefore, persuasion efforts are necessary. The previous analyses carried out within a municipality, as in our case in the village of Flossenbürg, play a crucial role in demonstrating the added value and making it understandable to individuals without a primarily technical background.

5. Location characteristics of single-family neighborhoods: the example of walkability

Urban planning is increasingly focusing on the active mobility of residents and proximity to frequently visited amenities, as evidenced by the now widely prevalent discourse around the 15-minute city (Shields et al. 2021, Caselli et al. 2022, Kissfazekas 2022). There are many compelling reasons to aspire to the ideal of a pedestrian-friendly city. Numerous studies demonstrate that pedestrian-friendly neighborhoods contribute to improving the daily mobility of residents, preventing diseases, and promoting healthy aging (Chandrabose et al. 2022, Yang et al. 2021).

In contrast to cities, the challenges in rural areas are often more fundamental and practical. Due to the decades-long expansion of villages through continually new residential areas, the distances to shopping facilities and other essential amenities have become significant in many cases. This presents particular challenges for individuals with limited mobility and those who do not have access to a car, with older individuals being a prominent example in this category. Our research in the village of Flossenbürg, for instance, demonstrates that in certain cases the walking distances exceed 500 meters. In this particular instance, we set a threshold of 500 meters because a total distance of 1000 meters (considering both the outward and return journeys) appears to present a significant challenge for pedestrians who have limited mobility.

Nonetheless, in this scenario, the presence of accessible public transportation in Flossenbürg can alleviate the effects of limited walkability: The blue area in Figure 5 indicates that the nearest bus stop is within a 500-meter radius of the residential addresses of many citizens.

5 Accessibility of bus stops in Flossenbürg (own source, based on Schaffert 2011; background data sourced from Bavarian State Office for Digitalization, Broadband, and Surveying)

However, the situation becomes significantly more intricate when we depart from a strictly two-dimensional planning approach and start considering the slopes and gradients within the road network. Flossenbürg is located in a southern German upland region (Figure 6). In this area, there are often road inclines of five percent or more between bus stops, shopping facilities, and detached houses, which are frequently inhabited by older individuals (Figure 7). Such barriers are likely to pose considerable challenges for seniors reliant on a walking aid. Barriers due to steep terrain that restrict accessibility are not only common in Flossenbürg but also in other German villages and regions, as previous research has shown (Schaffert et al. 2023). These challenges should be given greater consideration in spatial planning to ensure that urban planning adequately addresses the needs of people at different stages of their lives and supports the residents’ widespread wish to age in place (cf. Jehle et al. 2024).

6 Terrain in Flossenbürg – a village located in a low mountain region in southern Germany (own source, based on Schaffert 2011; background data sourced from Bavarian State Office for Digitalization, Broadband, and Surveying)

7 Terrain and accessibility in Flossenbürg (own source, based on Schaffert 2011; background data sourced from Bavarian State Office for Digitalization, Broadband, and Surveying)

6. Conclusion and future work

The transformation in demographics poses substantial challenges for German municipalities. In addition to regional inequalities, there are notable demographic characteristics at the micro-level that continue to receive insufficient attention in current research. Of particular note, the demographic transition within aging single-family homes that were built from the 1950s on poses a considerable challenge across numerous rural regions in Germany. In this context, a shortage of technical solutions compounds the complexities that planners and decision-makers face in navigating this transformation. Similar to the academic realm, practical strategies for addressing this phenomenon exhibit substantial gaps in knowledge and application.

A significant step in this effort involves visualizing small-scale demographic and accessibility challenges within towns and regions. Often, GIS software solutions used by many German municipalities are sufficient to handle these issues.

Furthermore, the primary data set for this approach, the municipal population register, already exists within the municipalities’ jurisdiction and only requires more focused processing. However, this kind of processing is time-intensive and, therefore, cannot be managed in the daily operations of local authorities with limited staff and modest ICT expertise. Consequently, the automatic processing of the population registers and concatenating the resulting information in a regional spatial data infrastructure are promising in this regard.

In a multi-thematic and regionally oriented decision support system, various layers encompassing factors such as the accessibility of essential services, public transportation options, and the condition of residential structures can be integrated and presented alongside demographic information. The integration of these layers into a monitoring tool would enable regional planning authorities to assess multiple parameters simultaneously, facilitating a more sustainable and holistic decision-making process. Such an integrated approach has the potential to significantly expedite the identification of single-family house areas in the region that have unfavorable location characteristics. As a result, local governments could strategically prioritize these areas and take measures to meet the needs of an aging population or create publicly coordinated initiatives to support the sale of vacant properties.

A further dimension towards a more comprehensive monitoring system can be achieved by gaining insights from additional datasets, such as satellite images or data from drone flights. For instance, thermal cameras have the potential to unveil valuable information concerning energy-related deficiencies that could pose obstacles for prospective property buyers or occupants (cf. Zhu et al. 2019). These shortcomings often arise due to gaps in maintenance, as statistics indicate that there is typically reduced investment in building modernization as residents age (cf. Jakob 2007). Consequently, we can indirectly acquire details about the residents and their ages through the utilization of remote sensing data. This approach serves to validate or enhance demographic information sourced from official records. Another pertinent illustration involves identifying alterations in the state of a property's lawns or hedges, especially when these are irregularly maintained, as might be the case for elderly residents facing physical constraints. Such modifications can influence the spectral attributes of the area, influencing the reflection and absorption of energy, a phenomenon discernible through precise airborne sensor technology. However, rural communities will hardly be able to implement such technically sophisticated approaches on their own. Bundling these activities within a municipal network, at the district level, or with the assistance of federal states appears necessary for villages and country towns to keep pace with digitalization.

Acknowledgment

The article was authored in the project RAFVINIERT – Raumintelligenz für die integrierte Versorgung von Seniorinnen und Senioren in ländlichen Quartieren (Spatial Intelligence for the Integrated Supply of Senior Citizens in Rural Neighborhoods), funded by the Carl Zeiss Foundation. The analyses were partly performed in earlier research.

Additional information

Funding

This work was supported by Carl-Zeiss-Stiftung.

Notes on contributors

Markus Schaffert

Markus Schaffert is a teacher and senior researcher at Hochschule Mainz: University of Applied Sciences in Germany. He graduated in geography (Diplom) at the University of Heidelberg and completed his doctorate at the Technical University of Darmstadt (Dr.-Ing.). In, 2019 he was appointed full professor in geospatial informatics in Mainz. His research focuses on the use of geospatial information to support sustainable spatial transformation, e.g. in the areas of ‘Demographic Change', ‘Rural Digitalisation' and ‘Transdisciplinary Research’.

Torge Steensen

Torge Steensen is a Senior Risk Manager at the Continental AG. He graduated from the University of Bristol, UK (MSc) and the University of Alaska Fairbanks, USA (PhD) with degrees in Geophysics, Volcanology and Spatial Sciences. Afterwards, he focussed on assessments of geospatial informations, biomass evaluations and demographic change during a postdoctoral stay at the University of Hanover, Germany. Currently, his work comprises risk assessments and management on a global scale at the Continental AG.