This study explores the ‘15-minute city’ concept in Hamilton, New Zealand, focusing on challenges related to car dependency and urban sprawl. Triggered by the greater emphasis on sustainable urban ...environments following the global pandemic, the research employs a mobility-based approach to assess the model's applicability across various urban districts. Geographic Information System (GIS) mapping is used to identify ‘liveable areas’ in business, residential, and industrial districts where essential services are accessible within three different thresholds of 5, 10, and 15 min walking distance. This tiered approach offers a detailed view of urban accessibility, highlighting the practicality and varying implementation levels of the 15-minute city concept across diverse urban areas. Geolocated mobile phone data from 88,660 residents is analysed with a focus on ‘inflow’ and ‘outflow’ travel distances at both city and district levels. While the results reveal the practical challenges of implementing the ‘15-minute city’ paradigm, they also show partial alignment of Hamilton's urban fabric with the paradigm, offering scope for adjustments to better suit the city's specific characteristics and residents' behaviours. The study highlights opportunities for enhancing the diversity and accessibility of amenities and improving public transportation and alternative transport options, all key factors for sustainable urban development. This adaptable methodology serves as a valuable reference for other cities in developing strategies for sustainable living. The study concludes that while Hamilton shows potential for transformation, a nuanced and locally focused approach is crucial. These insights contribute to the current new urbanist literature by providing a comprehensive city-district perspective, extending the discourse to include distances beyond the ‘15-minute city’ and highlighting areas where further urban planning or intervention is necessary.
This study contributes to the existing knowledge on the aspects that are commonly found in socially preferred public spaces. Specifically, it provides a thematic characterisation of specific factors ...that may play a critical role in their success. User-generated data is used for identifying, characterising, and comparing the most successful public spaces in the historic centres of four cities in the central Spanish Mediterranean Arc. Data from Google Places, Foursquare, Twitter and Instagram are used to infer insights on the offer and demand of economic and urban activity; the spatiotemporal presence of people; and the collective urban image of the plazas and their surrounding areas (500 m isodistance from the plazas' accesses). The results suggest that the success of public plazas nowadays is strongly associated to: (1) the functional complexity of buildings located at the intersections of streets leading into the plazas; (2) having other landmarks or venues of interest and/or clustered activities within 3–5-minutes walking distance from the plazas; (3) good connectivity to other neighbourhoods (2–3-minutes walking distance) with provision of good pedestrian infrastructure; and, (4) the importance of hosting local festivities and celebrations which generate a sense of community and belonging.
•Economic and urban activities influence social preferences of plazas•Preferred plazas have walkable and functionally complex access points•Successful plazas are well connected (less than 200m) to other parts of the city•Connectivity and functional diversity of the plazas’ surroundings is decisive•Other landmarks or venues nearby (3-5 min walk) is key for success of plazas
•We assess cultural ecosystem services of urban green and blue spaces through two methods.•The social media-based approach is better suited for assessing aesthetic services and touristic ...areas.•Participatory GIS allows for the characterization of little publicized urban sites and recreational services.•The two methods often provide unique results and are complementary in many instances.
Cultural ecosystem services (CES) are important components of urban quality of life. Public participation GIS (PPGIS) is widely used to assess and map these services. However, it is often a time-consuming exercise with which only small spatial and temporal scales can be addressed. Assessments based on geolocated, passively crowdsourced data from social media present new opportunities to assess CES through a large amount of available data and for broad spatial and temporal scales. We assess the potential of these two methods to substitute, supplement or complement each other in terms of the qualitative information they provide (i.e., landscape features of interest and CES). We take as a case study seven green and blue open spaces of the city of Haifa (Israel), each presenting different elements of interest in the landscape and degrees of accessibility. Results indicate that the two methods provide unique results and are complementary in many instances. We discuss the representativeness of the social media data, the strength of the two methods with respect to the qualitative information obtained, the specificities related to the urban context and the instances of complementarity. We suggest that crowdsourced social media data should be included in broad, multi-methodological approaches to CES.
Human diffusion and city influence Lenormand, Maxime; Gonçalves, Bruno; Tugores, Antònia ...
Journal of the Royal Society interface,
08/2015, Volume:
12, Issue:
109
Journal Article
Peer reviewed
Open access
Cities are characterized by concentrating population, economic activity and services. However, not all cities are equal and a natural hierarchy at local, regional or global scales spontaneously ...emerges. In this work, we introduce a method to quantify city influence using geolocated tweets to characterize human mobility. Rome and Paris appear consistently as the cities attracting most diverse visitors. The ratio between locals and non-local visitors turns out to be fundamental for a city to truly be global. Focusing only on urban residents' mobility flows, a city-to-city network can be constructed. This network allows us to analyse centrality measures at different scales. New York and London play a central role on the global scale, while urban rankings suffer substantial changes if the focus is set at a regional level.
Water demand forecasting is a crucial task in the efficient management of the water supply system. This paper compares classical and adapted machine learning algorithms used for water usage ...predictions including ARIMA, support vector regression, random forests and extremely randomized trees. These models were enriched with human mobility data to improve the predictive power of water demand forecasting. Furthermore, a framework for processing mobility data into time-series correlated with water usage data is proposed. This study uses 51 days of water consumption readings and over 7 million geolocated mobility records from urban areas. Results show that using human mobility data improves water demand prediction. The best forecasting algorithm employing a random forest method achieved 90.4% accuracy (measured by the mean absolute percentage error) and is better by 1% than the same algorithm using only water data, while classic ARIMA approach achieved 90.0%. The Blind (copying) prediction achieved 85.1% of accuracy.
The paper presents an alternative method for tracking the spatiotemporal dynamics of social interactions in public space in the context of the European Capital of Culture-based urban regeneration. ...The paper analyses publicly available geolocated data from two social media platforms, Instagram and Flickr, which are characterised by the posting of photos on the Internet. The quantity of social media posts in a given time period is used as a proxy indicator to identify and retrospectively analyse the attractiveness and spatiotemporal dynamics of public spaces. Using georeferenced interaction data from social media platforms, two case studies of regenerated public spaces from ECoC cities are presented: the DEPO2015 area in Pilsen (Czech Republic) and the DOKK1 urban waterfront in Aarhus (Denmark). The results show that the data from the Flickr platform, which allows access to the exact geolocation of the posted photos, can reveal attractive public spaces, as the popular landmarks were clearly identified on the generated heatmaps. The analysis of data from the Instagram social media platform, which uses georeferencing, can reveal the most important events and should be thus considered a valuable proxy for determining the overall level of social interaction in a public space. The methodology presented is particularly well suited for the analysis of central locations and special events, as is the case with the ECoC.
This research sheds light on the relationship between the presence of location-based social network (LBSN) data and other economic and demographic variables in the city of Valencia (Spain). For that ...purpose, a comparison is made between location patterns of geolocated data from various social networks (i.e., Google Places, Foursquare, Twitter, Airbnb and Idealista) and statistical information such as land value, average gross income, and population distribution by age range. The main findings show that there is no direct relationship between land value or age of registered population and the amount of social network data generated in a given area. However, a noteworthy coincidence was observed between Google Places data-clustering patterns, which represent the offer of economic activities, and the spatial concentration of the other LBSNs analyzed, suggesting that data from these sources are mostly generated in areas with a high density of economic activities.
Understanding the structuration of spatio-temporal information is a common endeavour to many disciplines and application domains, e.g., geography, ecology, urban planning, epidemiology. Revealing the ...processes involved, in relation to one or more phenomena, is often the first step before elaborating spatial functioning theories and specific planning actions, e.g., epidemiological modelling, urban planning. To do so, the spatio-temporal distributions of meaningful variables from a decision-making viewpoint, can be explored, analysed separately or jointly from an information viewpoint. Using metrics based on the measure of entropy has a long practice in these domains with the aim of quantification of how uniform the distributions are. However, the level of embedding of the spatio-temporal dimension in the metrics used is often minimal. This paper borrows from the landscape ecology concept of patch size distribution and the approach of permutation entropy used in biomedical signal processing to derive a spatio-temporal entropy analysis framework for categorical variables. The framework is based on a spatio-temporal structuration of the information allowing to use a decomposition of the Shannon entropy which can also embrace some existing spatial or temporal entropy indices to reinforce the spatio-temporal structuration. Multiway correspondence analysis is coupled to the decomposition entropy to propose further decomposition and entropy quantification of the spatio-temporal structuring information. The flexibility from these different choices, including geographic scales, allows for a range of domains to take into account domain specifics of the data; some of which are explored on a dataset linked to climate change and evolution of land cover types in Nordic areas.
We contribute a system design and a generalized formal methodology to segment tourists based on their geolocated blogging behaviour according to their interests in identified tourist hotspots. Thus, ...it is possible to identify and target groups that are possibly interested in alternative destinations to relieve overtourism. A pilot application in a case study of Chinese travel in Switzerland by analysing Qyer travel blog data demonstrates the potential of our method. Accordingly, we contribute four conclusions supported by empirical data. First, our method can enable discovery of plausible geographical distributions of tourist hotspots, which validates the plausibility of the data and its collection. Second, our method discovered statistically significant stochastic dependencies that meaningfully differentiate the observed user base, which demonstrates its value for segmentation. Furthermore, the case study contributes two practical insights for tourism management. Third, Chinese independent travellers, which are the main target group of Qyer, are mainly interested in the discovered travel hotspots, similar to tourists on packaged tours, but also show interest in alternative places. Fourth, the proposed user segmentation revealed two clusters based on users’ social media activity level. For tourism research, users within the second cluster are of interest, which are defined by two segmentation attributes: they blogged about more than just one location, and they have followers. These tourists are significantly more likely to be interested in alternative destinations out of the hotspot axis. Knowing this can help define a target group for marketing activities to promote alternative destinations.
Tourism is becoming a significant contributor to medium and long range travels in an increasingly globalized world. Leisure traveling has an important impact on the local and global economy as well ...as on the environment. The study of touristic trips is thus raising a considerable interest. In this work, we apply a method to assess the attractiveness of 20 of the most popular touristic sites worldwide using geolocated tweets as a proxy for human mobility. We first rank the touristic sites based on the spatial distribution of the visitors’ place of residence. The Taj Mahal, the Pisa Tower and the Eiffel Tower appear consistently in the top 5 in these rankings. We then pass to a coarser scale and classify the travelers by country of residence. Touristic site’s visiting figures are then studied by country of residence showing that the Eiffel Tower, Times Square and the London Tower welcome the majority of the visitors of each country. Finally, we build a network linking sites whenever a user has been detected in more than one site. This allow us to unveil relations between touristic sites and find which ones are more tightly interconnected.
PDF
