Hard and soft data integration in geocomputation: mixed methods for data collection and processing in urban planning

Planning support science: Developments and challenges

“…In terms of challenge, several experts refer to the need for a widening of the knowledge base for extant PSS. For instance, Silva et al (2020) identify the need for extra knowledge on behavioural theory for incorporation in geocomputation: 'It is difficult to extract detailed rules from behavioural theories that can be represented in mathematical equations or language-based rules such as if-then/else statements' (53). In the same vein, Hamerlinck (2020) identifies an urgent need for better understanding and more object-based knowledge about rural environments, about their unique cultural, economic and institutional characteristics and about associated rural planning.…”

“…Besides the growing diversity of instruments in PSScience, one can identify growing attention to methodology. After distinctive periods in time when planning has been dominated either by qualitative methods, like social discourse analysis, or by quantitative methods, like mathematical modelling, now it seems that with the increase in data availability, mixed methods approaches are gaining substantial ground (Silva et al, 2020). An important example of such a mixed method approach in planning is the upcoming field of geodesign where both modelling exercises (quantitative), like those adopted for forecasting future trends, and deliberative design approaches (qualitative), like those used for developing future spatial scenarios, are combined to deliver enhanced outcomes.…”

Mapping the Landscape of Behavioral Theories: Systematic Literature Review

“…Theories enable unstructured data such as text and human language and semi-structured data such as XML, JSON, and RSS 3 feeds to be rigorously included in the analysis to extract meaningful outcomes by providing the explanatory power. For example, in the field of urban and environmental planning, behavioral theories can be used to make the meaning of crowdsourcing data such as social media feeds to better understand the movement behavior of certain groups (Silva et al 2020). Also, behavioral theories can be used to explain the psychological and sociological reasons behind the movement patterns and processes in the big data provided by smart transport cards.…”

Measuring policy debate in a regrowing city by sentiment analysis using online media data: A case study of Leipzig 2030

“…As crowdsourced online data is increasingly used in urban planning, researchers have applied many data analytic techniques to harvest new data such as social media in recent years. Online media data is one kind of new data sources that show its unique value in providing quasi‐real‐time information (Silva, Liu, Kwon, Niu, & Chen, 2020). Guided by collaborative planning, urban planning nowadays aims at gathering more opinion from different stakeholders in many different types of media.…”

“…Sentiment analysis has been applied to quasi‐real‐time datasets for providing quick analytical results. Thus, it can contribute to smart governance through effectively revealing the opinion towards policies in cities (Niu & Silva, 2020; Silva et al, 2020). In policy analysis, sentiment analysis can be used to quantify the satisfactory level of policy discussants and identify hot topics in urban policy debates.…”