Citizen Perceptions of Government’s Resistance to Shared Parking

Abstract

CBC News explicit what is legal and what is illegal (Uzunca & Borlenghi, 2019).According to Hofmann et al. (2019), there is a need to understand the tensions between citizen users and the authorities, which have arisen from government's negative attitude toward sharing economies. Further, prior studies (for example, May et al., 2017;Ganapati & Reddick, 2018), call for more research on the barriers and opportunities related to specific sectors and integrations of sharing economies. In urban research, shared mobility, particularly ride hailing (for example, Uber and Lyft) is among the most studied and debated of sharing economy services in the context of cities (Ganapati & Reddick, 2018). However, Novikova (2017) argues that there is still need for research in a related sector, namely, at the intersection of shared mobility and physical infrastructure, such as buildings, roads, and parking. In fact, shared parking, which refers to matching seekers with available parking spaces on demand by lending or renting out unoccupied parking space such as residential driveways and private parking spots (Boysen et al., 2019;Kim et al., 2019), is an exemplary but under-researched area of sharing economies (Xu et al., 2020).

A Machine-Learning Analysis of the Impacts of the COVID-19 Pandemic on Small Business Owners and Implications for Canadian Government Policy Response

“…The comments were most frequently between one and 50 words, with an emphasis on the range between four and 30 words. These results are in line with those of previous research using online posts ( Westerlund 2020 ). For example, Sobkowicz et al (2013) found that the message-length distributions of various forms of Internet-based written communications follow a similar pattern.…”

“…We applied topic modelling, a probabilistic machine-learning method that discovers hidden thematic structures, by means of a group of inductive computational techniques, in a large corpus of documents, in our case common topics in small business owner narratives ( Hannigan et al 2019 ; Kim and Chen 2018 ; Westerlund 2020 ). Topic modelling uses statistical associations of words in a text to generate latent topics on the basis of co-occurring words that jointly represent higher-order concepts, without the aid of predefined, explicit dictionaries or interpretive rules; hence, topic modelling is an unsupervised approach ( Hannigan et al 2019 ; Murakami et al 2017 ).…”

“…Topic modelling uses statistical associations of words in a text to generate latent topics on the basis of co-occurring words that jointly represent higher-order concepts, without the aid of predefined, explicit dictionaries or interpretive rules; hence, topic modelling is an unsupervised approach ( Hannigan et al 2019 ; Murakami et al 2017 ). Topic modelling makes no presumptions about the meanings of the words and is suitable for discovering hidden patterns in texts in any discipline ( Westerlund 2020 ). It relies on interpretation and language-oriented rules but is also unique in its emphasis on the role of human researchers in generating and interpreting specific groups of topics ( El-Assady et al 2018 ) on the basis of the social contexts in which they are embedded.…”