Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents an improved methodology of using social media (Twitter) data to extract spatial and temporal patterns of park visits for urban planning purposes, along with the sentiment of the tweets, focusing on frequent Twitter users. We analyzed the spatiotemporal park visiting behavior of more than 4000 users for almost 1700 parks, examining 78,000 tweets in London, UK. The novelty of the research is in the combination of spatial and temporal aspects of Twitter data analysis, applying sentiment and emotion extraction for park visits throughout the whole city. This transferable methodology thereby overcomes many of the limitations of traditional research methods. This study concluded that people tweeted mostly in parks 3–4 km away from their center of activity and they were more positive than elsewhere while doing so. In our analysis, we identified four types of parks based on their visitors’ spatial behavioral characteristics, the sentiment of the tweets, and the temporal distribution of the users, serving as input for further urban planning-related investigations.

Livability reflects the quality of the person–environment relationship, namely how well the built environment or the available services in a city fulfill the residents’ needs and expectations. We argue that livability assessment can aid the implementation of certain New Urban Agenda (NUA) goals by providing a flexible way to assess urban environments and their quality. However, a reliable and transferable assessment framework requires the key elements of livability to be defined in such a way that measurable factors adequately represent the person–environment relationship. As an innovative approach, we determined key livability elements accordingly and asked over 400 residents worldwide to evaluate their urban environments using these parameters. Thereby, we could calibrate the livability assessment workflow by including personal aspects and identifying the most relevant livability factors through an ordinal regression analysis. Next, we performed relational-statistical learning in order to define the individual and combined contribution of these statistically significant factors to the overall livability of a place. We found that urban form and mobility-related factors tend to have the highest influence on residential satisfaction. Finally, we tested the robustness of the assessment by using geospatial analysis to model the livability for the city of Vienna, Austria. We concluded that the workflow allows for a reliable livability assessment and for further utilization in urban planning, improving urban quality by going beyond simple city rankings.

The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because their characteristics often contradict daily planning routines. Due to the availability of large amounts of data, social media offer the possibility for fine-scale spatial and temporal analysis in this context, especially regarding public emotions related to varied topics. Thus, this article proposes a combined approach for analyzing large sports events considering event days vs comparison days (before or after the event) and different user groups (residents vs visitors), as well as integrating sentiment analysis and topic extraction. Our results based on various analyses of tweets demonstrate that different spatial and temporal patterns can be identified, clearly distinguishing both residents and visitors, along with positive or negative sentiment. Furthermore, we could assign tweets to specific urban events or extract topics related to the transportation infrastructure. Although the results are potentially able to support urban planning processes of large events, the approach still shows some limitations including well-known biases in social media or shortcomings in identifying the user groups and in the topic modeling approach.

Around the globe, Geographic Information Systems (GISs) are well established in the daily workflow of authorities, businesses and non-profit organisations. GIS can effectively handle spatial entities and offer sophisticated analysis and modelling functions to deal with space. Only a small fraction of the literature in Geographic Information Science—or GIScience in short—has advanced the development of place, addressing entities with an ambiguous boundary and relying more on the human or social attributes of a location rather than on crisp geographic boundaries. While the GIScience developments support the establishment of the digital humanities, GISs were never designed to handle subjective or vague data. We, an international group of authors, juxtapose place and space in English language and in several other languages and discuss potential consequences for Geoinformatics and GIScience. In particular, we address the question of whether linguistic and cultural settings play a role in the perception of place. We report on some facts revealed by this multi-language and multi-cultural dialogue, and what particular aspects of place we were able to discern regarding the few languages addressed.

Walking as a transport mode is still often underrepresented in the overall transport system. Consequently, pedestrian mobility is usually not recorded statistically in the same manner as it is performed for motorised traffic which leads to an underestimation of its importance and positive effects on people and cities. However, the integration of walkability assessments is potentially a valuable complement in urban planning processes through considering important quantitative and qualitative aspects of walking in cities. Recent literature shows a variety of approaches involving discrepancies in the definition of walkability, the factors which contribute to it, and methods of assessing them. This paper provides a new understanding of the concept of walkability in the European context. Our approach relies on the extension of methodological competence in transportation, spatial planning and geography by linking new measurement methods for evaluating walkability. We propose an integrated approach to assessing walkability in a comprehensive methodology that combines existing qualitative and GIS-based methods with biosensor technologies and thus captures the perceptions and emotions of pedestrians. This results in an increased plausibility and relevance of the results of walkability analysis by considering the spatial environment and its effect on people.

Livability is a popular term for describing the satisfaction of residents with living in a city. The assessment of livability can be of high relevance for urban planning; however, existing assessment methods have various limitations, especially in terms of transferability. In our main research article, we developed a conceptual framework and an assessment workflow to provide a transferable way of assessing livability, also considering intra-urban differences of the identified livability assessment factors to use for further geospatial analysis. As a key part of this assessment, we developed a survey to investigate residential preference and satisfaction concerning different urban factors. The current Data Descriptor introduces the questionnaire we used, the distribution of the responses, and the most important findings for the socioeconomic and demographic parameters influencing urban livability. We found that the development of an area, the number of persons in the household, and the income level are significant circumstances in assessing how satisfied a person would be with living in a given city.

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement.

Analysis of urban green space (UGS) provision is becoming increasingly important from an urban-planning perspective, as processes related to climate change tend to worsen the urban heat-island effect. In the present study, we aimed to map the UGS provision of Szeged, Hungary, using a GIS-based complex approach. Different age groups, especially the elderly, have different demands on the ecosystem services and infrastructure of UGSs. To provide an in-depth assessment of UGS provision for planners, we analysed the UGS availability and accessibility, using subblock-level population data, which includes not only the total number of residents but also provides information about the age-group distribution for each building of the city. We delineated areas having different UGS provision levels (called provision zones) and assessed the age distribution of the residents living in each zone. We found that the residents within 2-min walking distance to public green spaces are older than expected by comparison to the age distribution of Szeged. In provision zones with abundant locally available UGSs (measured as UGS per capita within 50-m buffers), we found that the youngest (0–18 years) and oldest (≥ 61 years) inhabitants are overrepresented age groups, while the age group 19–40 has the lowest overall UGS provision within the city of Szeged. Our research, which has the potential to be adapted to other settlements, contributes to the identification of UGS-deficit areas in a city, thereby providing essential information for urban planners about where increases in UGS are most needed and helping to assess infrastructural enhancements that would be adequate for the locally most-dominant age groups.