Understanding the use of urban green spaces from user-generated geographic information

Heikinheimo, Vuokko; Tenkanen, Henrikki; Bergroth, Claudia; Järv, Olle; Hiippala, Tuomo; Toivonen, Tuuli

doi:10.1016/j.landurbplan.2020.103845

Cited by 145 publications

(85 citation statements)

References 69 publications

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“…However, estimations based on population densities may fail to identify popular recreational areas within cities, as demonstrated in the current study by the mismatch of visitor count and population density data from the Seurasaari study site. Instead, various user‐generated observations such as mobile phone and/or social media data may provide a useful alternative for determining the usage of urban green spaces and, consequently, for identifying high‐risk areas (Heikinheimo et al., 2020). Nevertheless, the more people living close to a risk area there are, the more people may be expected to enter them.…”

Section: Discussionmentioning

confidence: 99%

Enhanced threat of tick‐borne infections within cities? Assessing public health risks due to ticks in urban green spaces in Helsinki, Finland

Sormunen

Kulha

Klemola

et al. 2020

Zoonoses and Public Health

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Section: Discussionmentioning

confidence: 99%

Enhanced threat of tick‐borne infections within cities? Assessing public health risks due to ticks in urban green spaces in Helsinki, Finland

Sormunen

Kulha

Klemola

et al. 2020

Zoonoses and Public Health

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“…All terms with a frequency of more than * 1% in at least one land cover class are shown (Jackson et al 2008;Hewlett et al 2017). It thus illustrates the need for integrated approaches to user-generated content analysis combining data sources and analytical approaches (Heikinheimo et al 2020;Jeawak et al 2020). The analysis of text offers opportunities for research on people-landscape interactions across potentially large spatial and temporal scales that are often difficult to explore using more traditional social science engagement methods such as surveys or fieldbased interviews.…”

Section: Discussionmentioning

confidence: 97%

Assessing experienced tranquillity through natural language processing and landscape ecology measures

2021

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Context Identifying tranquil areas is important for landscape planning and policy-making. Research demonstrated discrepancies between modelled potential tranquil areas and where people experience tranquillity based on field surveys. Because surveys are resource-intensive, user-generated text data offers potential for extracting where people experience tranquillity. Objectives We explore and model the relationship between landscape ecological measures and experienced tranquillity extracted from user-generated text descriptions. Methods Georeferenced, user-generated landscape descriptions from Geograph.UK were filtered using keywords related to tranquillity. We stratify resulting tranquil locations according to dominant land cover and quantify the influence of landscape characteristics including diversity and naturalness on explaining the presence of tranquillity. Finally, we apply natural language processing to identify terms linked to tranquillity keywords and compare the similarity of these terms across land cover classes. Results Evaluation of potential keywords yielded six keywords associated with experienced tranquillity, resulting in 15,350 extracted tranquillity descriptions. The two most common land cover classes associated with tranquillity were arable and horticulture, and improved grassland, followed by urban and suburban. In the logistic regression model across all land cover classes, freshwater, elevation and naturalness were positive predictors of tranquillity. Built-up area was a negative predictor. Descriptions of tranquillity were most similar between improved grassland and arable and horticulture, and most dissimilar between arable and horticulture and urban. Conclusions This study highlights the potential of applying natural language processing to extract experienced tranquillity from text, and demonstrates links between landscape ecological measures and tranquillity as a perceived landscape quality.

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“…Analysis with these data has provided insights on a wide variety of social phenomena and socio-spatial processes, including crisis situations. Examples include, e.g., analysis on population mobility and commuting ( Ahas et al., 2015 ; Järv et al., 2012 ), detecting functional economic regions ( Novak et al., 2013 ; OECD, 2020 ), the provision and accessibility to state services ( Järv et al., 2018 ), identifying migration flows ( Kamenjuk et al., 2017 ) and cross-border mobility ( Silm et al., 2020a ), analyzing (in)equity between population groups and spatial segregation ( Mooses et al., 2016 ; Shelton et al., 2015 ; Silm et al., 2018 ), supporting transport solutions ( Positium, 2019 ) and environmental management ( Heikinheimo et al., 2020 ; Poom et al., 2017 ), characterizing tourist behavior ( Campagna et al., 2015 ; Raun et al., 2016; Saluveer et al., 2020 ), or reflecting the lived experiences of people in case of disruptions ( Shelton et al., 2014 ). Much of this research is conducted in countries where access to mobile Big Data has been relatively easy.…”

Section: Pre-covid-19 Mobile Big Data Researchmentioning

confidence: 99%

COVID-19 is spatial: Ensuring that mobile Big Data is used for social good

et al. 2020

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The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. The avalanche of mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. However, the current crisis also highlights serious limitations in the readiness to take the advantage of mobile Big Data for social good, both within and beyond the interests of health sector. We propose two strategical pathways for the future use of mobile Big Data for societal impact assessment, addressing access to both raw mobile Big Data as well as aggregated data products. Both pathways require careful considerations of privacy issues, harmonized and transparent methodologies, and attention to the representativeness, reliability and continuity of data. The goal is to be better prepared to use mobile Big Data in future crises.

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Understanding the use of urban green spaces from user-generated geographic information

Cited by 145 publications

References 69 publications

Enhanced threat of tick‐borne infections within cities? Assessing public health risks due to ticks in urban green spaces in Helsinki, Finland

Enhanced threat of tick‐borne infections within cities? Assessing public health risks due to ticks in urban green spaces in Helsinki, Finland

Assessing experienced tranquillity through natural language processing and landscape ecology measures

COVID-19 is spatial: Ensuring that mobile Big Data is used for social good

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