Who can access what? Uncovering urban inequality in access to service for senior citizens

Abstract

The scarcity or lack of access to essential services at the local and neighbourhood levels in cities can result in significant spatial inequalities, as some areas and their residents can deal with disadvantages and a lower quality of daily life. In particular, the spatial distribution and the variety of amenities at the local scale represent an important feature of the liveliness of places. The local availability and access to essential services are particularly relevant for some demographic groups experiencing limited mobility or mobility poverty, such as older adults living in cities, and spatial disparities have been further exacerbated by the COVID-19 pandemic, which highlighted severe difficulties in accessing essential services. This work explores the issue focussing on the following question: who can access what depending on where they live in cities? Using Machine Learning and Spatial Autocorrelation applied to different data sources for spatial information on the location of urban amenities and Internet access, this work aims to identify the most underserved places in terms of the variety of available amenities and access to quality broadband in three European capital cities. A comparison to urban areas where high percentages of older adults reside makes it possible to identify where residents can locally access several essential services (green spaces, health care, and local shopping) and where this need cannot be satisfied because of a lack in the amenity variety available at walking distance to their home. The combination of underserved areas with a high concentration of senior residents identifies left-behind areas in these cities, where interventions on inequalities are most needed. Results can inform policies aiming at favouring fair access to services at the local scale, possibly including slow and active mobility modes, and in general to develop comprehensive and sustainable planning strategies for cities, leaving no place and no person behind.

Introduction

Uncovering urban inequalities is of paramount importance to developing policies that can effectively target places and residents that need interventions. In this sense, a quantitative understanding of the relationship between the availability of essential services and the residential location of citizens in vulnerable conditions is critical for implementing policies aimed at improving their quality of life, making cities more inclusive, safe, and sustainable.

Existing spatial disparities have been further exacerbated by the COVID-19 pandemic. The limitations on urban mobility during the emergency revealed severe difficulties related to the availability of essential services at the local scale, and the importance of reliable Internet access to perform daily activities and complement physical access with remote one (from education and health consultations). The relevance of having access to adequate opportunities in terms of physical and digital accessibility is also related to a re-emergence of the importance of local scale in cities, with ideas like the ‘15-minute city’ (Moreno et al., 2021) gaining momentum.

The local availability of essential services is particularly relevant for vulnerable demographic groups experiencing limited mobility or mobility poverty, such as senior citizens. Europe is the region that is ageing the most (European Commission, 2022), and its cities are at the forefront of this phenomenon, with citizens over 65 projected to increase from 20% in 2020 to 28% in 2050. Furthermore, a recent survey (European Commission, 2020) shows that 20% of people consider the city where they live not a good place for senior citizens, with this figure rising to 24% in Berlin, 27% in Amsterdam, and 33% in Paris. This information sheds light on the relevance of understanding in detail the needs of older adults within cities and promoting initiatives to improve age-friendliness.

This work explores this issue with the following question: who can access what, depending on where they live in cities? The aim is to identify the most underserved and disconnected places in terms of variety of available amenities and access to quality broadband. This work considers a population over 65 years old, to evaluate a population segment that is considered more fragile concerning access to urban services, and a list of essential services tailored to this demographic group. The spatial analysis employs various data sources to identify underserved areas in three European cities, Amsterdam, Berlin, and Paris, to provide a comparative analysis across different countries applying a method reproducible to other cities and geographic contexts. The analysis is performed at a fine spatial scale (grid level), using highly disaggregated information, and considering walking as the active mode of transport.

Information on senior residents in cities is sourced from the national statistics offices. For urban amenities, data are collected through Google Maps API. For broadband, data containing spatial information about broadband network performance in Europe are provided by Ookla. The datasets are aggregated into a hexagon cell grid for spatial comparability purposes. To analyse the variety of amenities available in each area, an unsupervised learning technique is applied to highlight places with similar patterns of amenity typologies, labelled under the same class. Once the underserved places have been identified, spatial autocorrelation is applied to explore the spatial relationship between them and the characteristics of the surrounding areas. Results from the analysis show different patterns in the distribution of older adults’ residents, amenity variety, and quality of broadband. The combination of underserved areas with a high concentration of senior citizens identifies left-behind urban places where interventions are most needed. Results can inform policies favouring fair and equal access to services at the local scale, and in general to develop comprehensive and sustainable planning strategies for cities, leaving no place and no person behind.

Previous work

Ageing is a global phenomenon (WHO, 2015a) due to decreasing mortality rates and increasing life expectancy (Huang et al., 2023). Although some countries in the world are ageing at a faster speed (e.g. Japan), Europe is the region that is ageing the most, with the share of the population older than 65 years old projected to increase by 23% over the next 30 years (European Commission, 2022). In this context, international institutions and the scientific community are shaping together the global debate, elaborating strategies and policies to promote healthy ageing of society and the need to ‘leave no one behind’ (European Commission, 2021; Keating, 2022). The local aspect of ageing cannot be overlooked, and European cities are at the forefront of the phenomenon, with citizens over 65 projected to increase to 28% in 2050. The European local authorities tackle the phenomenon directly through their core urban policies and welfare, trying to promote initiatives to improve their age-friendliness (European Commission, 2020).

The World Health Organisation (2015b) recommends employing indicators belonging to three dimensions to measure the age-friendliness of cities: equality, inclusive social environment, and accessible physical environment. Previous work on neighbourhood’s physical environment typically focuses on the presence or absence of amenities (Diamond and Tolley, 2013; Meijers, 2008; Oldenburg, 1989). The spatial distribution and the variety of amenities at the local scale of a city represent a critical feature of the liveliness of places (Glaeser et al., 2001; Jacobs, 1961) and an enhanced potential for sociability and healthy lifestyles (Barton et al., 2012; Friman et al., 2017). The access and availability to different typologies of essential services (schools, hospitals, groceries etc.) can impact citizens’ daily routines (Alessandretti et al., 2020; Calabrese et al., 2010; Elldér et al., 2022).

The scarcity or lack of access to essential services at the local level in cities can result in persistent spatial disparities, with citizens dealing with disadvantages and a lower quality of life (e.g. older adults, low-income earners, and youth; see Southworth, 2005). Recent work on urban inequality focused on measuring spatial segregation and accessibility to urban amenities (among others, Calafiore et al., 2022; Staricco, 2022), whereas other studies compared different cities (Nicoletti et al., 2023), considering specific demographic groups (see e.g. Milias and Psyllidis, 2022). Fewer contributions are instead tackling the measurement of amenity mix at the neighbourhood level (Hidalgo et al., 2020). The low variety of amenity typologies in an area can also influence the lack of diversity in people visiting the neighbourhood (Moro et al., 2021) and the presence of people in the streets at different hours (De Nadai et al., 2016; Sulis et al., 2018; Sung et al., 2013, all fundamental elements for inclusion and safety in cities.

Regarding age-friendliness in cities, previous work includes studies on green spaces (Daams et al., 2019), whereas less attention is devoted to other amenities such as grocery shops (Yu-Tzu et al., 2022). Some scholars focused on the relationship between walking and accessing amenities for different population groups (Hunter et al., 2021; Talen, 2022; Vallée et al., 2022), investigating the safety and security of places (Tan and Lee, 2023), also including perceived crime risks (Buffel et al., 2013); access costs in terms of steep slopes, good quality sidewalks, and low risk of pedestrian accidents (Rhoads et al., 2023); the impact in the increase of physical activity (Handy et al., 2002; Heroy et al., 2022), the well-being, and satisfaction for older adults (An et al., 2020). These studies contribute to the body of research documenting spatial inequality in cities and the debate about the just city (Dlabac et al., 2022; Fainstein, 2014).

The contribution of this paper to the existing work on urban inequality and age-friendliness in cities is threefold:

• It explores the quantitative relationship between the spatial distribution of senior residents (aged over 65 years) and the variety of essential services specific for older adults and accessible at the neighbourhood scale through a comparative approach in three European capital cities.

• It introduces the availability of broadband connectivity as an essential service in cities, with the idea of mapping the stratification of opportunities both for physical and digital accessibility.

• It employs three data sources at a high level of disaggregation to perform spatial analysis at the grid level (hexagon cells of 800 m diameter), to improve spatial comparison among cities and provide urban policy with quantitative evidence that can support target local initiatives to improve the accessibility for senior citizens and promote their active ageing and well-being.

Methods and data

Methodology

To uncover urban inequality in the access to essential services by senior residents, we first explore separately the spatial patterns of distribution for (a) residents over 65 years old, (b) urban amenities, and (c) broadband access and performance. Secondly, we checked the co-occurrence of those patterns to identify which areas with high percentages of senior residents are well-served or underserved in terms of essential services and good digital access.

To perform the spatial comparison, we selected a hexagon grid with cells of 800 m diameter to aggregate the information we collected into a shared spatial unit of analysis. The shape and size of the grid cells are chosen to perform better at joining data and at representing gradual spatial changes. They also approximate a circular buffer of 15 min walking distance for older adults, calculated according to an average speed of 0.96 m/s for adults over 64 years old (Silva et al., 2014; Yang and Diez-Roux, 2012). This distance has been selected also considering previous work on senior citizens in relation to walking patterns of seniors, active ageing, and amenity accessibility at the neighbourhood scale (among others, King et al., 2003; Ribeiro et al., 2021; Szeto et al., 2017; Yang et al., 2018). This spatial scale is also in line with the daily routines of older adults, that likely do not commute any longer for work and that need to easily reach available services in a close range of their place of residence. These aspects are all relevant when investigating accessibility at the local scale (Moreno et al., 2021) for a segment of the population that might need or favour slower means of transportation as walking.

For each hexagon cell, we computed several attributes. For population, the share of senior residents (over 65 years old) in comparison to the total population living in the area. For broadband performance, the average download speed in the cell.

Regarding amenities, we focused on mapping the variety of urban functions accessible at the local level, which is a critical factor impacting the daily routine and mobility of people in cities, especially those with reduced mobility or in mobility poverty. First, we computed the variety of amenities per area using Shannon’s entropy, using the entropy function in the scipy.stats ¹ package and calculated as follows:

$H=-sum\left(pk\hspace{0.17em}*\hspace{0.17em}\mathit{log}\left(pk\right)\right)$

In this context, the Shannon entropy quantifies the uncertainty in predicting the typology of an individual amenity in a specific cell from the dataset including all the amenity typologies.

To further explore the relationship between the variety and the number of amenity types, we applied an unsupervised learning technique to identify areas with similar profiles in terms of the number and the typologies of urban functions. This additional step in the analysis can reveal which areas are well-equipped in terms of number of amenities and typologies and which areas are instead underserved and possibly at a risk to become left-behind places. To do so, we selected the Jensen–Shannon divergence (JSD) ² as the distance metric, specifically designed to calculate the similarity between two (or multiple) probability distributions. In this case, it measures how similar the distribution of amenity typologies is amongst different places in the city. Areas with similar patterns of essential services availability are labelled under the same class using the clustering algorithm HDBSCAN (Campello et al., 2013) through the library hdbscan (McInnes et al., 2017; Sulis and Lavalle, 2020). By doing this, we could see which areas show the presence of several amenities available to residents in the local vicinity and which areas present fewer typologies.

To identify underserved areas for senior residents in cities, we apply spatial autocorrelation (Anselin, 1995; Anselin and Rey, 2014), using tools available in PySAL (Rey and Anselin, 2010) and following examples available in various online documentations. ³ We first applied Local Moran’s I to understand the spatial patterns of senior residents and possibly local clusters (hot and cold spots) and local spatial outliers in their residence distribution. We then applied Bivariate Local Moran’s I ⁴ to better understand the relationship between one variable in a certain area and the spatial lag of other variables. This step is useful to evaluate geographic overlapping and co-occurrence of spatial patterns between senior residents and the variety of urban functions and between senior residents and digital access.

Data

For this analysis, we employed three types of data containing information on population and essential service distribution across three European capital cities: Amsterdam (NL), Berlin (DE), and Paris (FR). The choice of these cities is related to the availability of data at high spatial resolution for comparative analysis. In this case, this type of information on amenities was available only for capital cities. Within this pool, high-resolution demographic data were not easily accessible for many cities (demographic group stratification was not available). Therefore, we selected three cities that cover different geographical domains in Europe.

Regarding population distribution, specifically the location of senior residents in the three cities, we employed demographic information from statistics offices available as open data at different granularities and spatial units.

For Amsterdam, the data are available through the CBS website (https://opendata.cbs.nl/statline/#/CBS/nl/dataset/83502NED/table?ts=1575556895525). The data are available at different aggregation levels, and we selected the aggregation by postcode for the year 2020. Information about the population includes the total population and population over 65 years old residing in the area for each postcode.

For Berlin, the data are retrieved through the Berlin open data portal (https://daten.berlin.de/datensaetze/einwohnerinnen-und-einwohner-berlin-lor-planungsr%C3%A4umen-am-31122020) available for 2020. Data are aggregated at the LOR level (Lebensweltlich orientierte Räume), used by statistics until 2020. Data include the total population and senior residents, divided between 65 and 79 years old and over 80 years old per spatial unit (LOR). These classes were aggregated into one for comparability with the other two datasets.

For Paris, the data are available through the INSEE website for the entire of France (https://www.insee.fr/fr/statistiques/6543200) and were extracted for the IRIS (Ilots Regroupés pour Information Statistique) tracts corresponding to the Greater City of Paris. The information is available for 2019 for the total population and senior residents over 65 years old residing in each spatial unit (ilot).

About urban amenities, the POI information was collected through Google Maps API in August 2018. Attributes used in this analysis include the geographic location of the amenity and its typology. These typologies were then grouped into larger classes of essential services that are considered particularly relevant to senior residents at the local scale of the city. The list includes arts and leisure, banks and post offices, grocery shops, libraries, medical services, parks, personal care shops, pharmacies, restaurants, and worship places. We selected these amenities taking into consideration lists of essential services compiled in previous work on urban accessibility to services (among others, Calafiore et al., 2022; Staricco, 2022) and looking at previous examples on senior residents’ needs (e.g. https://snoopeep.github.io/eqmap_mb0510, based on Yang and Diez-Roux, 2012) adapted to the context of European cities. Amenities included are considered essentially needed in the daily routine of this specific demographic group to have a good quality of life, such as health facilities (hospital and care centres), green spaces such as parks and gardens, and local grocery shops, since driving might not be an option for senior residents.

Regarding digital accessibility, we used spatial information on broadband performance supplied by Ookla (Speedtest® by Ookla®, 2022) and available for 2021. Data are collected at the grid level and are available for the three cities considered in this work. The attributes used for this analysis include the average download speed (Mbps), weighted by the number of tests performed in each square.

Results

Spatial patterns of senior residence and service availability at the local scale

In this section, we describe the results of the analysis of senior residence and service availability for the three cities compared in the work. Figures are provided only for the city of Berlin for space availability reasons. The other cities are available in Appendix.

The first attribute we calculated is the share of senior residents (people over 65 years old) compared to the total population localised in each cell (Figure 1). In doing so, we could identify which areas in the cities present the higher concentration of senior citizens and where most attention and later intervention would be needed to ensure good service availability for such specific demographics. In the case of Berlin, the higher percentage of senior residents appears to be located outside the city centre, with the south and northwest areas where one out of four residents is over 65 years old.

The second attribute we considered is the availability of a reliable Internet connection, which is an essential service to consider nowadays: it gives access to numerous opportunities and can supply valuable alternatives for needs such as health consultation, education, and shopping, as it became evident during the recent pandemic event. This aspect is especially true for people with reduced mobility (such as senior citizens). However, we should consider that, especially in this demographic segment, the lack of digital literacy might be a problem to overcome to make these digital opportunities fully available. In Berlin (Figure 1), a reliable Internet speed appears to be available in the whole city, with places in the south and east city showing a lower speed and scattered areas reaching broadband speeds over 300 Mbps.

The third attribute we considered is the variety of amenities available to senior residents at their local scale. We calculated Shannon entropy to have a first overview of the heterogeneity in the spatial distribution of urban functions. Central places show the highest diversity (over 1.5), whereas southeast and northwest areas appear more variegated. This visualisation (Figure 1) can give us a preliminary insight into how this distribution relates to the patterns of residence of seniors in the city: areas with less variety of urban functions mean that residents must move to other places, in the short or long range, to fulfil their daily needs. This aspect can have different consequences, both in terms of the active mobility modes available to people to reach the amenities (walking, cycling, and others) and the frequency with which they need to move (i.e. grocery: every day; leisure: once/twice a week). Especially for senior citizens, who might have different routines and needs in terms of mobility, it is critical to understand which services are available at the local scale and in immediate proximity to make sure they have a good quality of life.

Figure 1.

Top map: senior residents’ percentage in comparison to total population of each cell (grey areas indicate no information on this population class is available). Middle map: average download speed measured in Mbps (grey areas indicate no information on broadband performance is available). Bottom map: Shannon entropy for amenities (grey areas indicate no presence of amenities of the selected classes).

To better understand which urban functions senior residents can access in each area, we further explore the variety of amenities available across cities. The cluster analysis revealed which places are similar across the three cities in terms of service availability, highlighting places well-served by multiple urban functions at the local distance and other areas where only a few service typologies are available, meaning that residents would need to travel to different lengths to fulfil some daily needs.

The three cities show some similarities in the amenity distribution patterns: for example, most areas are assigned to label 6, which presents a well-balanced variety of amenities that can fulfil many of the daily needs residents may have. This amenity distribution appears to characterise the central places of the three cities homogeneously. On the other hand, in the outer areas, especially in Amsterdam and Berlin, the situation is more heterogeneous, with places characterised by the neat prevalence of one function over the others. Also, peripheral areas (in grey in the maps in Figure 2) often do not have amenities classified as essential services for senior residents. Areas characterised by a high presence of restaurants and cafes (in yellow on the maps) are frequently present in Paris and Amsterdam, especially in the central and northern parts of the two cities. In Berlin (Figure 2 and 3), a clear pattern for areas rich in arts and leisure amenities is easily identifiable in the city centre. Another frequent label in Berlin is characterised by a high percentage of parks and green areas (dark blue on the maps): in Berlin, such places can be found frequently across the entire city, whereas in the other two cities, and especially in Amsterdam, this amenity typology is less frequent and located mainly in the city outskirts. Another pattern that can be observed, particularly in the outer areas of Berlin and Paris, is the presence of adjacent places where grocery stores are the prevalent amenity at the local level (light green in the maps). In Paris, this is particularly evident in the southern part of the city. In Berlin, this happens in the south and west outer city areas. In Amsterdam, instead, we can notice numerous places characterised by the location of personal care shops (from toiletries to hairdresser salons) outside the city centre and especially in the north.

Figure 2.

Patterns of similarity of amenity availability at the local scale for Berlin. In the following page, the amenity distribution profile corresponding to each cluster is provided.

Figure 3.

Amenity distribution profile corresponding to each cluster.

Finally, we can notice how, among the three cities, Berlin stands out in terms of heterogeneity, especially in the eastern part of the city: this might be related to the specific history of Berlin and the several modifications and recombinations of urban fabric that the city underwent in the recent past (Figure 2).

Overall, we can observe how, in terms of the variety of services available at the local scale (neighbourhood with a short distance walking or cycling), these three cities perform well, especially in central areas. In outer areas, many essential services (groceries, parks, and personal care) are still available, even though places are more likely to have only a few amenity typologies, and residents might need to travel further to fulfil other needs. Some amenities such as arts and leisure and eating out are more frequent in central areas, meaning that some activities would require residents to travel to specific places in the city to spend some leisure time during their daily or weekly routines. Having observed this distribution, we now need to consider how it relates to the location of senior residents in these cities, as the two distribution patterns might not overlap. This aspect is already noticeable in Amsterdam and Berlin, where citizens over 65 years old seem to live in higher percentages in outer areas of the cities. To better understand this specific spatial relationship, we developed an additional step in our analysis, presented in the following section.

Spatial autocorrelation between underserved areas and senior citizens residence

To better identify well-served and underserved areas where senior residents live in cities, we added a further step in the analysis to localise places that would show significant unevenness in the availability of amenities or digital connectivity (Figure 4). We employed Bivariate Local Moran’s I ⁵ for the analysis as a tool that makes it possible to explore the spatial relationships of different variables, being aware that a careful evaluation of the results is required.

Figure 4.

Berlin – Bivariate Local Moran’s I: senior residents and amenity diversity (above), senior residents and digital connectivity (below).

Amsterdam (see Appendix)

Regarding the variety of amenities available to residents, the places where most senior residents live (the north and south-west part) show different patterns: northern areas appear mostly underserved (with few well-served islands), whereas the south-west part seems to provide better availability to residents in terms of variety of amenities present at the local scale. As expected, the large island in the city centre has a higher variety of amenities than other areas, where also a high share of citizens over 65 years old lives. The situation appears more fragmented for digital availability, with several well-served areas (higher broadband speed) among those where the senior residents are 20% of the total population or more.

Berlin

In terms of amenity variety, the model seems to capture well the relationship among senior residents, with fewer residents living in the city centre, which appears to be a very well-served area. On the other end, areas highly inhabited by seniors show two different patterns: places in proximity of the city centre appear well-served in terms of the variety of amenities available at the local scale (red in the maps), whereas places more distant from the centre, especially in the south and east sides of the city, appear as underserved areas (yellow in the maps). This result might highlight a condition of spatial disparities in terms of the quality of life of people residing in such places, which need to be addressed by targeted policies. The availability of a reliable broadband connection appears to show similar patterns, although in this case areas distant from the centre present slightly larger islands of well-served places, especially on the east side of the city.

Discussion and conclusion

This work proposes a quantitative approach to pinpoint underserved and marginalised places in cities with respect to the variety of essential services available to older adults. It compares three European capital cities – Amsterdam, Berlin, and Paris – to highlight similarities in spatial inequality patterns in urban areas where a significant percentage of senior citizens reside.

In this sense, it aims to contribute to the research stream that investigates urban inequalities and disparities, adding novelty to the matter with a dedicated focus on ageing population groups and left-behind places. Previous work on this area explored spatial disparities and segregation, focussing on the accessibility to amenities at the local level for residents in cities (Calafiore et al., 2022; Staricco, 2022), and investigating the needs of specific demographic groups across different cities (Milias and Psyllidis, 2022) and countries (Nicoletti et al., 2023). Other research focused specifically on measuring walking distances and access to amenities in US cities for different population groups (Hunter et al., 2021; Talen, 2022) or specifically for older adults (Ribeiro et al., 2021).

Our work contributes to this discourse by investigating what senior residents can access at walking distance in city neighbourhoods across Europe. We selected a list of amenities representing an assorted mix that could fulfil the essential daily needs and routines of older adults. We also added the availability of a reliable Internet connection to the mix of essential services, since it can give access to relevant services such as online delivery or telemedicine. Applying this tailored selection, our findings provide a further step to the characterisation of urban inequality affecting a vulnerable group of city residents. One finding is the notable spatial discrepancy between the location of elderly friendly amenities and where the higher share of older adults resides in the cities. All three cities show a wider variety of amenities in their central areas, whereas patterns of senior residents are more pronounced in the outer parts of cities, especially in Berlin. Findings also show that such discrepancies differ between cities, with Amsterdam showing a better concurrence between patterns of amenities and senior residents, whereas Berlin presents quite a striking discrepancy, especially in the central and eastern areas. Differences could be explained by the variety of social initiatives in cities, such as housing policies.

Our work also contributes to the renewed interest in urban research for the local scale and livability in cities. Although the importance of these features is not something new, as the emphasis on vitality, diversity, and walkability in the established work of Jacobs (1961) and (Gehl, 2011) demonstrates, it has recently gained momentum with the concept of ‘15-Minute City’ (Moreno et al., 2021), which is currently being debated in the literature (among others, Dunning et al., 2021; Glaeser, 2021).

Our study advances previous work on the local scale, performing a comparative analysis across three different European capital cities at the grid level. Analysing both patterns of residence and essential services at a finer spatial scale serves two purposes/makes it possible to achieve two results. Firstly, findings at such a granular level identify underserved areas and disparities in more detail, providing target areas for policy. Secondly, it represents an attempt to spatially assess concepts proposed in the literature from a quantitative perspective. Employing a hexagon grid that approximates a 15-min walking distance for older adults, our findings also provide insight into the possible discrepancy between urban policy concepts and the actual condition of service accessibility for vulnerable groups. Our attempt can serve as input for developing further evaluation methods to test concepts and policies that aim at transforming cities fairly and sustainably.

Overall, the findings of our study reinforce the importance of local scale in cities, especially for vulnerable groups. As previous studies have shown, the spatial distribution and the variety of amenities represent a significant feature of the liveliness of places, and a balanced and accessible mix of different urban services at the local level can favour walkability and encounters, foster a sense of attachment to places, and possibly counteract segregation (Sabater et al., 2017). These aspects are deemed relevant to ensure a good quality of life for older adults in cities, and therefore, findings can provide evidence to inform policies aimed at contributing to a more sustainable development of an ageing society (European Commission, 2021; OECD, 2015. For example, assessing how essential services are accessible at walking distance can provide urban decision-makers with detailed insights to plan interventions that make it easier for older adults to navigate the city through slow mobility modes suitable for their daily needs. Findings can also help to define place-based policy and strategies to mitigate existing and potential disparities (United Nations, 2015). Moreover, they can localise areas where initiatives such as ‘ageing in place’, which push for the permanence of senior residents in their homes and neighbourhoods, can be implemented (Fernández-Carro, 2016). This initiative can prevent dissociation from their social network and physical environment and promote initiatives to improve city age-friendliness.

This study proposes a novel framework of data and methods to enrich the characterisation of spatial inequalities in cities in relation to vulnerable population groups and identify who can access what depending on where they live in cities. We performed a comparative analysis that included three European capital cities for which high-resolution data were available. Additionally, we employed a spatial grid to compare cities with different administrative boundaries. Although this choice might have resulted in some loss in terms of the specific urban morphology, it was nevertheless considered the best option for comparison and reproducibility reasons across different spatial contexts.

Our framework can be used in future work to analyse different geographical contexts facing similar trends in ageing societies and cities (such as Japan), to explore how patterns of inequality might be influenced by different urban morphology, mobility behaviours, and sociocultural contexts. Future work can also aim at further enriching the characterisation of spatial inequalities: this analysis can be replicated for other segments of the older population (Sanderson and Scherbov, 2010) or population groups belonging to vulnerable groups, selecting services that are deemed essential for such target population (for children, it could be schools, sports facilities), to understand which areas and resident population are at a risk of being left behind. Additional research is also required to broaden our understanding of the impact of local urban policies on spatial discrepancies affecting different population groups, in the effort to improve sustainable developments in cities that leave no one and no place behind.

Supplemental Material

Supplemental Material - Who can access what? Uncovering urban inequality in access to service for senior citizens

Supplemental Material for Who can access what? Uncovering urban inequality in access to service for senior citizens by Patrizia Sulis and Paola Proietti in Journal of Environment and Planning B: Urban Analytics and City Science.