In the context of progressive urbanization and ageing of the population, the LONGEVICITY project has the objective to study advanced solutions for enhancing the social inclusion of the elderly through walkability. The paper is focused on the application of GIS for the de nition of a novel Elderly Walkability Index. This is based of the analysis of a series of geo-referenced data of the City of Milan (Italy) in order to measure: (i ) the residential density of the elderly inhabitants; (ii ) the level of pedestrian friendliness of the city in terms of usefulness, comfort and safety. Results allowed to identify the areas of the city where to collect empirical evidences about age-driven pedestrian mobility, for supporting the development of advanced AI-based simulation models.
Urbanization is one of the global trends of the 21st Century, which requires
institutions to more e ectively design and plan the cities to improve the quality
of life of the inhabitants. Today, over half of the world's population lives in urban
areas and all the regions of the world are expected to urbanize further over the
coming decades: by 2030 over 60% of the global population will live in cities and
large urban agglomerates [
Urbanization shift is occurring globally since 2007, joined to the increasing
proportion of elderly in the population. Population aging, due to longevity and a
decline in both mortality and fertility rates, is now occurring fastest in high and
middle-income countries, being Italy and Japan at the rst places experiencing
an aging society [
The World Health Organization [
Facing this trend, advanced urban planning activities [
The term walkability refers to those design elements which guarantee the
accessibility, comfort and safety of the urban setting for walking [
As part of the research project \LONGEVICITY: Social Inclusion for the Elderly through Walkability", this work aims at developing innovative strategies to sustain the active aging of the citizens of Milan (Italy), by enhancing pedestrian mobility through walkability. First, the paper to proposes a detailed de nition of the criteria, data and methods for the assessment of the level of walkability of urban areas, taking into account the needs of the elderly inhabitants. Then, the paper presents the results of a GIS analysis aimed at identifying the areas of the City of Milan characterized by the highest presence of elderly inhabitants and by the poorest level of walkability. The areas will be exploited to collect empirical evidences about age-driven pedestrian mobility, for supporting the development of advanced AI-based simulation models. The paper concludes with nal remarks about results and future works. 1.1
As described in previous works alredy presented by the authors [
The assessment of the level of walkability of an urban area for the elderly should comprise the evaluation of di erent typologies of data: (i ) structured data (e.g., topographical, cadastral, infrastructural and architectural elements of the urban area; census indicators of the socio-demographical characteristics of the inhabitants); (ii ) behavioral data (e.g., analysis of the impact of the spatial features of the area on the actual behaviors of the elderly inhabitants while walking and crossing); (iii ) subjective data (e.g., analysis of the elderly inhabitants' subjective evaluation about the level of walkability of the area).
A wide range of methods have been developed to empirically measure the
level of pedestrian friendliness of urban environments, within a multi-disciplinary
approach (e.g. urban studies, architecture, urban sociology, environmental
psychology, computer science). We propose below a brief description of each method,
towards their application for measuring the level of walkability of an urban area
for the elderly pedestrians:
{ Geographic Information Systems (e.g., [
The proposed GIS analysis was based on a series of structured open data of the City of Milan, focused on: (i ) the residential density of the elderly inhabitants; (ii ) the level of usefulness (accessibility to transport, social and commercial 3 Retrieved from: https://goo.gl/dXC4TL 4 Retrieved from: https://goo.gl/QzEBNu 5 Retrieved from: https://goo.gl/Wj2Amy services within a walkable distance); (iii ) the level of comfort (accessibility of pedestrian-friendly areas and urban green areas within a walkable distance); (iv ) level of safety (possibility for the elderly to do not be exposed to pedestrian-car accidents). We did not analyzed the level of attractiveness and legibility of the city, due to the di culty to collect geo-referenced data about these criteria.
The analysis was performed by means of the software ESRI ArcMap 10.5 and series of open data (see Tab. 1) retrieved from: Geo-portal of the City of Milan6; website of the City of Milan7; ISTAT-Italian National Institute of Statistics8. Part of the data were not geo-referred with latitude-longitude coordinates, so they have been coded by using the tool Geoplaner9.
The total area of the City of Milan (about 182 km2) is divided in No. 9 Municipalities, which have the prerogative to decentralize the government of the city. According to the local Territorial Administration Plan10 (2016), Municipalities are subdivided in No. 88 Neighborhoods or NLI-Nuclei of Local Identity: a system of areas connected by means of mobility infrastructures and services, and characterized by urban vitality, distinctive features, historical heritage, but also by ongoing renovation projects. Thus, NLI have been considered as the most appropriate spatial units for the proposed GIS analysis, in terms of historical peculiarity, granularity, average area (about 2 Km2).
The retrieved open data were used to design a series of multi-layers maps the City of Milan, considering the territorial boundaries of the above described NLI. This was aimed at estimating the spatial distribution of each data set among the NLI. Then, we de ned a novel EWI-Elderly Walkability Index through the weighted summation of the considered indicators (see Tab. 1), to identify the NLI characterized by the highest residential density of elderly inhabitants and by the poorest levels of usefulness, comfort and safety for the elderly. 3 3.1
To estimate the residential density of the elderly population of the City of Milan, we analyzed a series of geo-referenced data set focused on the demographic characteristics of the inhabitants (e.g., age, gender, NLI of residence) among its No. 88 NLI. Data were retrieved from the Geo-portal of the City of Milan, on the basis of the national census executed in 2015 by ISTAT (see Tab. 1).
Data analysis shows that in 2015 the number of citizens with an age equal or more than 65 years old (about 324,300 people) was equal to the 24% of the total population of the City of Milan (about 1,351,500) (see Fig. 1/a). Then, we calculated the ratio between the number of elderly residents among the NLI (i.e. 6 Retrieved from: https://goo.gl/98Ztfa 7 Retrieved from: https://goo.gl/4K89Tt 8 Retrieved from: https://goo.gl/bE8nY9 9 Retrieved from: https://goo.gl/nJrK89 10 Retrieved from: https://goo.gl/e8Pj8p (a) Elderly population (b) ERD
ER-Elderly Residents) and the area of each NLI (Km2). Results were normalized (z values in a range between 0 and 1) in order to more easily compare data among the NLI. The map presented in Figure 1/b highlights in dark shade the NLI characterized by the highest ERD value (z 0.820212, quintile frequency distribution of standardized values), which corresponds to more than No. 4530 elderly residents per Km2. 3.2
To estimate the LUE-Level of Usefulness for the Elderly of the City of Milan, data analysis was based on the localization of those services speci cally tailored to accommodate the needs of the elderly, and on the calculation of their spatial distribution among the NLI. The objective was to estimate the possibility for the elderly residents of each NLI to reach various public services within a walkable distance from their place of residence. Data were retrieved from the Geo-portal of the City of Milan and from the website of the City of Milan (see Tab. 1).
We rstly focused on the localization of TS-Transport Services, which guarantee the autonomous mobility of the elderly through the city by using public transports. This comprehends the localization of: subway stations; bus and tram stops; railway stations (which serve as local transport service within the City of Milan); bike-sharing stations.
Then, we localized those SS-Social Services speci cally tailored to guarantee the social inclusion of the elderly within their NLI of residence, focusing on: social and health care services for the elderly; day time senior centers; multi-functional aggregation senior centers; social, cultural and recreational senior centers; cultural associations; public library and sport facilities.
Eventually, we localized those CS-Commercial Services which allow the elderly to access goods and products within a walkable distance from their place of residence, focusing on: medium and high sized commercial activities (shops of more than 250 m2); open-air local markets; pharmacies; post o ces.
The analysis was based on calculation of the density distribution of TS, SS and CS among the NLI (see Fig. 5 in Appendix), considering the area of each NLI (Km2). The estimation of the LUE-Level of Usefulness for the Elderly was based on the ratio between the summation of TS per Km2, SS per Km2 and CS per Km2 present in each NLI, and the number of ER-Elderly Residents per Km2 of each NIL. The summation has been weighted by means of constant parameters to accentuate the impact of the density distribution of SS on LUE, compared to TS and CS (see Eq. 1):
LU ENLI =
(1)
Results were normalized (z values in a range between 0 and 1) to more easily compare data among the NLI. The map presented in Figure 2/a shows the NLI characterized the lowest LUE-Level of Usefulness for the Elderly (z 0.237956, quintile frequency distribution), which corresponds to the poorest density distribution of TS (in average No. 22 TS per Km2), SS (in average No. 7 SS per Km2) and CS (in average No. 3 CS per Km2), considering also the number of elderly residents of the NLI (in average No. 1971 ER per Km2). 3.3
To estimate the LCE-Level of Comfort for the Elderly of the City of Milan, data analysis was based on the localization of those facilities speci cally designed to accommodate the needs of the elderly while walking through the city, and on the calculation of their spatial distribution among the NLI. The objective was to estimate the possibility for the elderly to bene t from pedestrian-friendly road infrastructures within a walkable distance from their place of residence. Data were retrieved from the Geo-Portal of the City of Milan and from the website of the City of Milan (see Tab. 1).
We focused on the localization of: (i ) PFA-Pedestrian Friendly Areas (Km2), comprising pedestrian areas restricted to vehicular tra c, urban areas with vehicle speed limit to 30 km/h, limited tra c areas to vehicular tra c and congestion charge areas; (ii ) PCA-Pedestrian Circulation Areas (Km2), comprising sidewalks and squares; (iii ) UGA-Urban Green Areas (Km2), comprising parks, public garden, private courtyards, woods, and plantations areas.
The analysis was based on the calculation of density distribution of PFA, PCA and UGA among the NLI (see Fig. 6 in Appendix), considering the area of each NLI (Km2). The estimation of the LCE-Level of Comfort for the Elderly was based on the ratio between the summation of the PFA per Km2, PCA per Km2 and UGA per Km2 present in each NLI, and the number of ER-Elderly Residents per Km2 of each NLI. The summation has been weighted to accentuate the impact of the density distribution of PFA on the overall LCE, compared to PCA and UGA (see Eq. 2):
LCENLI =
(2)
Results were normalized (z values in a range between 0 and 1) to more easily compare data among the NLI. The map presented in Figure 2/b shows the NLI characterized the lowest LUE-Level of Comfort for the Elderly (z 0.364242, quintile frequency distribution), which corresponds to the poorest density distribution of PFA (in average 0.017 Km2 of PFA per Km2), PCA (in average 0.069 Km2 of PFA per Km2) and UGA (in average 0.271 Km2 of PFA per Km2), considering also the number of elderly residents of the NLI (in average No. 4264 ER per Km2). 3.4
To estimate the LSE-Level of Safety for the Elderly, data analysis was based on the localization of the pedestrian-car accidents occurred in the City of Milan, and on the calculation of their spatial distribution among the NLI. The objective was to estimate the possibility for the elderly to bene t from safe road infrastructures within their place of residence. Data were retrieved from the ISTAT-Italian National Institute of Statics in 2016, on the basis of the data gathered by local authorities during that year (see Tab. 1).
As highlighted by the WHO [
We focused on the localization of PA-Pedestrian Accidents, comprising fatal and non-fatal pedestrian-car accidents. The analysis was based on the calculation of density distribution of PA among the NLI (see Fig. 7 in Appendix), considering the area of each NLI (Km2). The estimation of the LSE-Level of Safety for the Elderly was based on the ratio between PA per Km2 and the number of ERElderly Residents per Km2 of each NLI (see Eq. 2): 11 Retrieved from: https://goo.gl/VdCg9f jPANLI j jERNLI j (3)
Results were normalized (z values in a range between 0 and 1) to more easily compare data among the NLI. The map presented in Figure 2/c shows the NLI characterized the lowest LSE-Level of Safety for the Elderly (z 0.267516, quintile frequency distribution), which corresponds to the highest density distribution of PA (in average No. 15 PA per Km2), considering also the number of elderly residents of the NLI (in average No. 2198 ER per Km2). 3.5
The last analysis was aimed at identifying the NLI of the City of Milan characterized by the highest residential density of elderly inhabitants and the poorest levels of usefulness, comfort and safety for the elderly pedestrians. To estimate the EWI-Elderly Walkability Index, data analysis was based on the summation of the achieved results about LUE, LCE and LSE. The summation has been weighted to accentuate the impact of the density distribution of LSE on the overall EWI, compared to LUE and LCE (see Eq. 2):
EW INLI = KLUE jLUENLI j + KLCE jLCENLI j + KLSE jLSENLI j (4)
Results were normalized (z values in a range between 0 and 1) to more easily compare data among the NLI. The map presented in Figure 2/d shows the NLI characterized the lowest EWI-Elderly Walkability Index (z 0.182536, quintile frequency distribution). Average results about NLI quintile frequency distribution among ERD, LUE, LCE, LSE and EWI are presented in Figure 3. 4
In the context of global progressive urbanization and ageing of the population, the LONGEVICITY project studies the cities of the future as characterized by the growing presence of long-lived and active citizens and by the need to design technologically advanced services and infrastructures to enhance sustainable mobility strategies. The project is be carried out by an international consortium (University of Milano-Bicocca, Politecnico di Milano, The University of Tokyo, AUSER Volontariato Lombardia) and is based on a strongly cross-disciplinary approach, integrating skills, methodologies and tools ranging from Social Sciences, Design of Services, Arti cial Intelligence and Complex Systems Science.
In particular, the LONGEVICITY project aims at supporting the social inclusion and active aging of the population in urban settings by enhancing their pedestrian mobility through walkability. To this end, the project is based on (a) LUE (b) LCE (c) LSE (d) EWI methodological and computational tools aimed at assessing the level of pedestrian friendliness of urban areas, and at achieving advanced solutions focused on the needs and perceptions of senior citizens with respect to infrastructures and mobility services in the City of Milan (Italy).
In this context, the current work was aimed at presenting a systematic review of the contributions present in the literature about the criteria, data and methods for the assessment of the level of walkability of urban environments, focusing of the speci c needs of the elderly pedestrians. Then, the paper proposed the Fig. 3. Average results of NLI quintile frequency distribution of standardized values about: ERD-Edlerly Residential Density; LUE-Level of Usefulness for the Elderly; LCELevel of Comfort for the Elderly; LSE-Level of Safety for the Elderly; EWI-Elderly Walkability Index. de nition of a novel EWI-Elderly Walkability Index, based on the the application of GIS-Geographic Information Systems for the analysis of a series of structured and geo-referenced data on the City of Milan.
Results are focused on the residential density of the elderly inhabitants and on the level of pedestrian friendliness of the city in terms of usefulness, comfort and safety for the elderly pedestrians. The objective of the research was to identify the Neighborhoods or NLI of the City of Milan characterized by the poorest level of walkability and by the highest presence of elderly residents.
In line with the research plan of the project, the presented multi-layer map of the City of Milan represents a preliminary step aiming at identifying the NLI where to execute a series of outdoor and indoor activities participated by a large sample of elderly inhabitants of the area (e.g., walking groups, participatory design activities). On the basis of the achieved results, we identi ed the Neighborhood \Padova" (see Fig. 4) as one of the most critical area of the City of Milan in terms of usefulness, comfort and safety for the elderly pedestrians (about No. 5189 ER per Km2; EWI = 0.318198). The identi ed NLI will be object of further investigations through audit tools, questionnaires, eld observations, mobile applications, controlled experiments and computer-based simulations.
Future works will be focused on the integration between GIS and AI-based
techniques [
This is aimed at executing a bottom-up assessment of the level of walkability
of the City of Milan (Italy) by means of three main functionalities of the
application: (i ) tracking tool, to collect quantitative data about the daily walking
Fig. 4. The localization of TS-Transport Services, SS-Social Services, CS-Commercial
Services, PFA-Pedestrian Friendly Areas, PCA-Pedestrian Circulation Areas,
UGAUrban Green Areas and PA-Pedestrian accidents within the territorial boundaries of
the NLI \Padova" of the City of Milan.
behaviors of participants (e.g., trajectories, speed, walking pace); (ii ) audit tool,
to collect the subjective evaluations of the elderly about the level of
walkability of a speci c area; (iii ) spatial transcript tool, to design augmented maps of
urban areas, by integrating geo-referenced and multimedia annotations of the
elderly users. The above described results will be exploited to support the
development of advanced AI-based simulation models [
According to the SUMP-Sustainable Urban Mobility Plan of the City of Milan12 (2017), the presented results could be of notable interest for those public institutions involved in the design of sustainable mobility strategies. The results achieved through the proposed EWI are able, in fact, to highlight the NLI of the city which require to be more e ectively managed in order to support pedestrian mobility, with particular reference to the needs of the elderly. This could motivate, for example, strategic investments to optimize the provision of transport 12 Retrieved from: https://goo.gl/ZPUdAC and social services among the NLI or to design age-friendly areas where people can enjoy walking and gathering in comfort. In conclusion, the presented results are potentially relevant to complement studies on outdoor ambient assisted environments, to evaluate future transportation scenarios in Smart Cities.
The project \LONGEVICITY: Social inclusion for the Elderly through Walkability" is funded by Fondazione Cariplo (Grant No. 2017-0938). The analyzed open data have been treated according to the General Data Protection Regulation (EU, 2016/679). The annotated data set is available only for research purposes (contact: andrea.gorrini@unimib.it). The authors thank Luca Crociani for his fruitful contribution. Fig. 7. The localization of PA-Pedestrian accidents and of the car-accidents involving elderly pedestrian among the NLI of the City of Milan.