Valuing Recreation in Italy's Protected Areas Using Spatial Big Data

Sinclair, Michael; Ghermandi, Andrea; Signorello, Giovanni; Giuffrida, Laura; Salvo, Maria De

doi:10.1016/j.ecolecon.2022.107526

Cited by 18 publications

(9 citation statements)

References 49 publications

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“…Research in this theme has viewed social media primarily as a data source to assess phenomena that are otherwise difficult or impossible to measure. Thus, in this research strand, the focus of the investigation is not social media as a phenomenon per se but rather the data that social media provide to study other phenomena (e.g., Barbos & Kaisen, 2022; Boegershausen et al., 2022; Bourreau et al., 2022; Chau et al., 2020; Dugoua et al., 2022; Ge et al., 2016; Jiang et al., 2018; Kuchler et al., 2022; Lee et al., 2022; Makridis, 2022; Peng, Teoh, et al., 2022; Sinclair et al., 2022; Tambe, 2014; Zhang & Ram, 2020).…”

Section: Resultsmentioning

confidence: 99%

The many faces of social media in business and economics research: Taking stock of the literature and looking into the future

Tumasjan

2023

Journal of Economic Surveys

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Since their inception more than 15 years ago, social media have become a vibrant research topic in business and economics research. This article presents an integrative literature review taking stock of and showing the many faces of social media in extant research. Based on N = 1419 articles published in the leading peer‐reviewed business and economics journals in the years 2008–2022, we identify and describe seven overarching research themes, namely, social media as a: (1) market‐oriented interaction hub, (2) resource‐oriented interaction hub, (3) information market, (4) innovation and business venturing hub, (5) societal challenge, (6) political hub, and (7) data source. Finally, we derive a research agenda to stimulate future research on this increasingly important topic.

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Section: Resultsmentioning

confidence: 99%

The many faces of social media in business and economics research: Taking stock of the literature and looking into the future

Tumasjan

2023

Journal of Economic Surveys

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“…As a tool to overcome the above-stated challenges, recreational studies have rapidly applied various types of ICT big data, specifically, social media and crowdsourced big data, mobile phone App based GPS data, and mobile phone network data ( Ghermandi and Sinclair, 2019 , Kolstoe and Cameron, 2017 , Kubo et al, 2020 ). Evidence of green space use from ICT big data analysis is also abundant (e.g., Mears et al, 2021 ; Sinclair et al, 2022 ). Jaung and Carrasco (2020) indicate that mobile phone data plays an important role in robustly monitoring ecosystem services change in urban green space over time.…”

Section: Introductionmentioning

confidence: 99%

Changes in visits to green spaces due to the COVID-19 pandemic: Focusing on the proportion of repeat visitors and the distances between green spaces and visitors’ places of residences

Kim

Shoji

Mameno

et al. 2023

Urban Forestry & Urban Greening

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“…To identify and measure environmental injustice, researchers traditionally draw upon existing datasets collected by outside sources or set out to collect new data. On the one hand, relying on existing datasets provides a larger quantity of information to be collected over a greater area for a lower price point, often at the cost of temporal relevance [20][21][22][23][24]. On the other hand, producing new datasets allows researchers to control the frequency, methodology, and overarching intention of data collection given they have the resources required to commit to collection efforts.…”

Section: Introductionmentioning

confidence: 99%

“…While still in its early stages of development, scholars have begun to refer to big data like social media as a new form of remote sensing data, labeled social sensing data, with individuals serving as the ultimate "sensors" [37][38][39]. These socially sensed datasets have been utilized for environmental justice investigations before, drawing from pictures posted online to social media [22], locations associated with cellphone use [36], and general social media application activity [40][41][42]. Authors Xu, Jiang, Li, Zhang, Zhao, Abbar, and González [36], for example, analyzed exposure to particulate matter by incorporating cellphone data into traditional pollution modelling strategies to bolster analytical capabilities.…”

Section: Introductionmentioning

confidence: 99%

Bridging the Gap: Analyzing the Relationship between Environmental Justice Awareness on Twitter and Socio-Environmental Factors Using Remote Sensing and Big Data

Knoble,

2023

Remote Sensing

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Mounting awareness of the discriminatory distribution of environmental factors has increasingly placed environmental justice at the forefront of discussions on sustainable development, but responses to these disparities are often too little, too late. Remote sensing has emerged as a potential solution to this problem, capitalizing on the ability to capture high-resolution, spatially explicit data in near-real time. However, a conventional reliance on physical measurements and surface-level analyses risks overlooking the experiences and perceptions of affected communities. It is against this backdrop that the potential integration of remote sensing imagery and socially sensed big data such as social media data assumes a novel and promising role. This study aims to discern the feasibility, opportunities, and implications of integrating the spatial insights provided by remote sensing with the experiential narratives shared on social media platforms, bridging the gap between objective environmental data and community-driven perspectives. We explore this subject in two ways, analyzing the geographic relationship between environmental justice Tweets and environmental justice factors, and reviewing Tweets produced during an extensive wildfire. Remote sensing indexes for green and blue space were reviewed and tested, selecting the measures of best fit to act as independent variables alongside traditional environmental justice factors in the broader analysis. Results from regression models indicate a negative relationship between the number of Tweets utilizing environmental justice relevant terms and the presence of ecosystem services as captured by an NDMI, suggesting a broad awareness of injustice and a relationship between remote sensing and social media. However, there is simultaneously a negative relationship between socially vulnerable populations and Tweets with environmental justice words. This suggests that generally, there is discussion on Twitter about injustice when resources are not present, but the voices of vulnerable populations are often less visible, either as a result of urban bias or a lack of concern for injustices due to habitual ignorance. Our study demonstrates the potential for integrating remote sensing imagery and social sensing data to play a substantial role in detecting injustices and corroborating data collected through community science initiatives.

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Valuing Recreation in Italy's Protected Areas Using Spatial Big Data

Cited by 18 publications

References 49 publications

The many faces of social media in business and economics research: Taking stock of the literature and looking into the future

The many faces of social media in business and economics research: Taking stock of the literature and looking into the future

Changes in visits to green spaces due to the COVID-19 pandemic: Focusing on the proportion of repeat visitors and the distances between green spaces and visitors’ places of residences

Bridging the Gap: Analyzing the Relationship between Environmental Justice Awareness on Twitter and Socio-Environmental Factors Using Remote Sensing and Big Data

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