Parks and other green spaces are an important part of sustainable, healthy and socially equal urban environment. Urban planning and green space management benefit from information about green space use and values, but such data are often scarce and laborious to collect. Temporally dynamic geographic information generated by users of different mobile devices and social media platforms are a promising source of data for studying green spaces.
In a recent article published in the Landscape and Urban Planning journal we compare the ability of different user-generated data sets to provide information on where, when and how people use and value urban green spaces. We compare four types of data: social media, sports tracking, mobile phone operator and public participation geographic information systems (PPGIS) data in a case study from Helsinki, Finland, and ask: 1) where the spatial hot-spots of green space use are, 2) when people use green spaces, 3) what activities are present in green spaces and 4) who are using green spaces based on available sample data sets.
Being in, moving through and perceiving urban green spaces
Our results show that user-generated geographic information provide dynamic information about the use of urban green spaces. Social media data highlight patterns of leisure time activities and allow further content analysis. Language detection allows further understanding of the different user groups. Sports tracking data and mobile phone data capture green space use at different times of the day, including commuting through the parks. PPGIS studies allow asking specific questions, including relevant background information from active participants.
Each data source has its limitations which need to be acknowledged in further analyses. For example, social media data are mainly produced by young adults and the popularity of different platforms might change quickly over time. Sports tracking data is dominantly produced by men and focuses on physical activities such as biking and jogging. Mobile network data is often difficult to access at fine granularity, and PPGIS data are limited in duration and extent. In all cases, user-generated data should be processed and reported in a privacy-preserving manner.
Despite evident limitations, these data might often be the best available information about the use of green spaces. Combining information from multiple user-generated data sets complements traditional data sources and provides a more comprehensive understanding of green space use and preferences.
New data have become available during the COVID-19 pandemic
Near-real time information about human activities in different areas have become extremely relevant during the COVID-19 pandemic in spring 2020, and new data sources about people’s mobility patterns have become available for research.
Also Google, and Apple have shared previously inaccessible information about people’s mobility patterns openly online (for a limited time period). These mobility data sets show a general trend of increased activity in green spaces in the Helsinki Region, and decreased activity in transit, retail and workplaces. Data from Google and Apple contribute to understanding questions related to where, when and what with most accurate information about the temporal dimension.
These data sets are highly aggregated and anonymized – individual users, or even individual parks are not visible in these mobility data sets.
Main limitations for using these data are still the same as we highlight in our paper (Heikinheimo et al. 2020): the representativeness of, access to and ethical use of user-generated content.
Overall, there is clearly an increasing need for versatile information about crowded (and silent) places in urban areas, and the various benefits of urban green spaces to people.
Heikinheimo, V. , Tenkanen, H., Bergroth, C., Järv, O., Hiippala, T., & Toivonen, T. (2020). Understanding the use of urban green spaces from user-generated geographic information. Landscape and Urban Planning, 201, . https://doi.org/10.1016/j.landurbplan.2020.103845
Yearly award for the best Master’s thesis have been again presented by the city of Helsinki on Monday the 9th of December 2019. We at the Digital Geography Lab would like to give special congrats to three awarded GIS wizards from the Department of Geography and Geosciences, Univerisity of Helsinki: Claudia Bergroth, Hertta Sydänlammi and Elias Willberg!
Bergroth Claudia (2019): Uncovering population dynamics using mobile phone data: the case of Helsinki Metropolitan Area.
The estimated hourly distribution of people on an average weekday in the Finnish Capital Region based on network-driven cellular mobile phone data. Read more about Claudia’s work in this blog post.
Sydänlammi Hertta (2019): Strategic districting for the mitigation of educational segregation: A pilot model for school district optimization in Helsinki.
Urban vegetation has traditionally been mapped through traditional ways of remote sensing like laser scanning and aerial photography. However, it has been stated that the bird view examination of vegetation cannot fully represent the amount of green vegetation that the citizens observe on street level. Recent studies have raised human perspective methods like street view images and measuring of green view next to more traditional ways of mapping vegetation. Green view index (GVI) states the percentage of green vegetation in street view on certain location. The purpose of my thesis was to create a green view dataset of Helsinki city using Google street view (GSV) imagery and to reveal the differences between human perspective and aerial perspective in vegetation mapping.
Figure 1. Summertime Google street view panoramas of Helsinki were downloaded in six horizontal images. The GVI value of a panorama is the average of these 6 images.
Street view imagery of Helsinki was downloaded from GSV application programming interface. The spatial extent of the data was limited by the availability of street view images of summer months. Every GSV panorama was downloaded in six images (Figure 1). The amount of vegetation in the images was calculated based on the spectral characteristics of green vegetation (Figure 2). The GVI value of each panorama image is an average of all the six images constructing the panorama.
Figure 2. From left to right: original, classified and overlay image. GVI was calculated based on the spectral characteristics of green surfaces. The GVI value of this street view is 43.97%.
Several green view maps of Helsinki were created based on the calculated GVI values (Figure 3). In order to understand the differences between human perspective and the aerial view, the GVI values were compared with the regional land cover dataset of Helsinki using linear regression. Areas with big differences between the datasets were examined visually through the street view imagery. Helsinki green view was also compared internationally with other cities with same kind of data available at the Treepedia website of Seanseable City Lab, MIT.
Figure 3. GVI values aggregated to YKR statistical grid. The downtown and industrial areas are easily recognized form the rest of the city with their lower GVI values.
It appealed that the green view of Helsinki is divided unequally across the city area. The lowest green view values can be found in downtown, industrial areas and the business centers of the suburbs. Highest values were located at the housing suburbs. Especially the older areas of lower housing like Kuusisaari, Lehtisaari and Laajasalo stand out with relatively high GVI values. Younger housing areas like Arabianranta, Latokartano and Herttoniemenranta have relatively low GVI values because of their yet undeveloped greenery.
When compared with the land cover data, it was found that the green view has a weak correlation with low vegetation and relatively high correlation with taller vegetation such as trees. Differences between the datasets were mainly concentrated on areas where the vegetation was not visible from the street by several reasons. Main sources of errors were the oldest street view images and the flaws in image classification caused by other green objects and shadows.
Even though Helsinki has many parks and other green spaces, the greenery visible to the streets isn’t always that high. The green view dataset created in this study helps to understand the spatial distribution of street greenery and brings human perspective next to more traditional ways of mapping city vegetation. When combined with previous city greenery datasets, the green view dataset can help to build up more holistic understanding of the city greenery in Helsinki.
The event was organized by the Forum for Environmental Information (Ympäristötiedon foorumi in Finnish), which is a non-profit organization that aims at increasing interaction between the producers and users of environmental information in order to support national policy making in Finland, while keeping in mind the global significance of environmental problems.
Read also Henna’s blog about getting better at supporting urban biodiversity (in Finnish):
In this blog, we share latest news about our research activities, teaching, GISviz and so on! For more information about Digital Geography Lab, please visit our webpages at helsinki.fi/digital-geography.