Longitudinal spatial dataset on travel times and distances by different travel modes in Helsinki Region

Comparable data on spatial accessibility by different travel modes are frequently needed to understand how city regions function. Here, we present a spatial dataset called the Helsinki Region Travel Time Matrix that has been calculated for 2013, 2015 and 2018. This longitudinal dataset contains travel time and distance information between all 250 metres statistical grid cell centroids in the Capital Region of Helsinki, Finland. The dataset is multimodal and multitemporal by nature: all typical transport modes (walking, cycling, public transport, and private car) are included and calculated separately for the morning rush hour and midday for an average working day. We followed a so-called door-to-door principle, making the information between travel modes comparable. The analyses were based primarily on open data sources, and all the tools that were used to produce the data are openly available. The matrices form a time-series that can reveal the accessibility conditions within the city and allow comparisons of the changes in accessibility in the region, which support spatial planning and decision-making.Peer reviewe

Capturing time in space : Dynamic analysis of accessibility and mobility to support spatial planning with open data and tools

Understanding the spatial patterns of accessibility and mobility are a key (factor) to comprehend the functioning of our societies. Hence, their analysis has become increasingly important for both scientific research and spatial planning. Spatial accessibility and mobility are closely related concepts, as accessibility describes the potential to move by modeling, whereas spatial mobility describes the realized movements of individuals. While both spatial accessibility and mobility have been widely studied, the understanding of how time and temporal change affects accessibility and mobility has been rather limited this far. In the era of ‘big data’, the wealth of temporally sensitive spatial data has made it possible, better than ever, to capture and understand the temporal realities of spatial accessibility and mobility, and hence start to understand better the dynamics of our societies and complex living environment. In this thesis, I aim to develop novel approaches and methods to study the spatio-temporal realities of our living environments via concepts of accessibility and mobility: How people can access places, how they actually move, and how they use space. I inspect these dynamics on several temporal granularities, covering hourly, daily, monthly, and yearly observations and analyses. With novel big data sources, the methodological development and careful assessment of the information extracted from them is extremely important as they are increasingly used to guide decision-making. Hence, I investigate the opportunities and pitfalls of different data sources and methodological approaches in this work. Contextually, I aim to reveal the role of time and the mode of transportation in relation to spatial accessibility and mobility, in both urban and rural environments, and discuss their role in spatial planning. I base my findings on five scientific articles on studies carried out in: Peruvian Amazonia; national parks of South Africa and Finland; Tallinn, Estonia; and Helsinki metropolitan area, Finland. I use and combine data from various sources to extract knowledge from them, including GPS devices; transportation schedules; mobile phones; social media; statistics; land-use data; and surveys. My results demonstrate that spatial accessibility and mobility are highly dependent on time, having clear diurnal and seasonal changes. Hence, it is important to consider temporality when analyzing accessibility, as people, transport and activities all fluctuate as a function of time that affects e.g. the spatial equality of reaching services. In addition, different transport modes should be considered as there are clear differences between them. Furthermore, I show that, in addition to the observed spatial population dynamics, also nature’s own dynamism affects accessibility and mobility on a regional level due to the seasonal variation in river-levels. Also, the visitation patterns in national parks vary significantly over time, as can be observed from social media. Methodologically, this work demonstrates that with a sophisticated fusion of methods and data, it is possible to assess; enrich; harmonize; and increase the spatial and temporal accuracy of data that can be used to better inform spatial planning and decision-making. Finally, I wish to emphasize the importance of bringing scientific knowledge and tools into practice. Hence, all the tools, analytical workflows, and data are openly available for everyone whenever possible. This approach has helped to bring the knowledge and tools into practice with relevant stakeholders in relation to spatial planning

Capturing time in space : dynamic analysis of accessibility and mobility to support spatial planning with open data and tools

Understanding the spatial patterns of accessibility and mobility are a key (factor) to comprehend the functioning of our societies. Hence, their analysis has become increasingly important for both scientific research and spatial planning. Spatial accessibility and mobility are closely related concepts, as accessibility describes the potential to move by modeling, whereas spatial mobility describes the realized movements of individuals. While both spatial accessibility and mobility have been widely studied, the understanding of how time and temporal change affects accessibility and mobility has been rather limited this far. In the era of ‘big data’, the wealth of temporally sensitive spatial data has made it possible, better than ever, to capture and understand the temporal realities of spatial accessibility and mobility, and hence start to understand better the dynamics of our societies and complex living environment. In this thesis, I aim to develop novel approaches and methods to study the spatio-temporal realities of our living environments via concepts of accessibility and mobility: How people can access places, how they actually move, and how they use space. I inspect these dynamics on several temporal granularities, covering hourly, daily, monthly, and yearly observations and analyses. With novel big data sources, the methodological development and careful assessment of the information extracted from them is extremely important as they are increasingly used to guide decision-making. Hence, I investigate the opportunities and pitfalls of different data sources and methodological approaches in this work. Contextually, I aim to reveal the role of time and the mode of transportation in relation to spatial accessibility and mobility, in both urban and rural environments, and discuss their role in spatial planning. I base my findings on five scientific articles on studies carried out in: Peruvian Amazonia; national parks of South Africa and Finland; Tallinn, Estonia; and Helsinki metropolitan area, Finland. I use and combine data from various sources to extract knowledge from them, including GPS devices; transportation schedules; mobile phones; social media; statistics; land-use data; and surveys. My results demonstrate that spatial accessibility and mobility are highly dependent on time, having clear diurnal and seasonal changes. Hence, it is important to consider temporality when analyzing accessibility, as people, transport and activities all fluctuate as a function of time that affects e.g. the spatial equality of reaching services. In addition, different transport modes should be considered as there are clear differences between them. Furthermore, I show that, in addition to the observed spatial population dynamics, also nature’s own dynamism affects accessibility and mobility on a regional level due to the seasonal variation in river-levels. Also, the visitation patterns in national parks vary significantly over time, as can be observed from social media. Methodologically, this work demonstrates that with a sophisticated fusion of methods and data, it is possible to assess; enrich; harmonize; and increase the spatial and temporal accuracy of data that can be used to better inform spatial planning and decision-making. Finally, I wish to emphasize the importance of bringing scientific knowledge and tools into practice. Hence, all the tools, analytical workflows, and data are openly available for everyone whenever possible. This approach has helped to bring the knowledge and tools into practice with relevant stakeholders in relation to spatial planning. Keywords: Accessibility; Spatial mobility; Spatio-temporal; Multimodal; Travel time; Open data; Social media; GIS; Data science; Data mining; Spatial planning; National parks; Finland; South Africa; Peruvian Amazonia; Helsinki Region; TallinnAlueellisen saavutettavuuden ja ihmisten liikkumisen rakenteiden hahmottaminen on tärkeää yhteiskunnan toiminnan ymmärtämisessä. Saavutettavuusanalyyseistä on tullut yksi keskeisistä työkaluista alueellisen suunnittelun ja päätöksenteon tueksi. Käsitteinä saavutettavuus ja liikkuminen ovat lähellä toisiaan. Saavutettavuudella tarkoitetaan tyypillisesti ihmisten mahdollisuutta saavuttaa eri paikkoja liikkumalla, kun ihmisten liikkumisen tutkimus keskittyy toteutuneeseen liikkumiseen. Saavutettavuuden ja todellisen liikkumisen alueellisia rakenteita on tutkittu melko paljon eri ympäristöissä. Rakenteiden ajallisten muutosten huomioiminen saavutettavuus- ja liikkumistutkimuksessa on ollut paljon vähäisempää. Nykyiset massiiviset digitaaliset tietoaineistot ovat mahdollistaneet yhteiskunnan eri toimintojen tarkastelun ennennäkemättömällä tarkkuudella niin ajallisesti kuin alueellisestikin. Väitöskirjassani pyrin kehittämään ja soveltamaan uusia lähestymistapoja sekä analyyttisia työkaluja alueellisen saavutettavuuden sekä ihmisten liikkumisen tutkimuksessa. Lisäksi pyrin ymmärtämään kuinka saavutettavuusrakenteet sekä ihmisten liikkumisen rakenteet vaihtelevat ajassa ja tilassa eri aikaperspektiiveillä ulottuen tunneista ja päivistä aina kuukausittaisiin ja vuosienvälisiin tarkasteluihin. Työni anti tieteelliseen keskusteluun on menetelmäpainotteinen, mutta tarjoan myös kontekstisidonnaisia havaintoja tutkimusalueiltani. Uusien tietolähteiden sekä menetelmien suhteen on tärkeää ymmärtää toisaalta niiden tarjoamat mahdollisuudet, mutta myös heikkoudet. Yksi väitöskirjani tavoite onkin tarkastella näitä tekijöitä eri aineistojen ja menetelmien suhteen. Väitöskirjani koostuu johdanto-osasta sekä viidestä tieteellisestä artikkelista. Artikkelit on toteutettu erilaisissa maantieteellisissä ympäristöissä: Perun Amazoniassa, Suomen ja Etelä-Afrikan kansallispuistoissa, Tallinnassa sekä Helsingin metropolialueella. Tulokseni osoittavat, että alueelliset saavutettavuuden ja liikkumisen rakenteet vaihtelevat merkittävästi eri ajankohtina ja niissä on selkeitä päivittäisiä ja kausittaisia vaihteluja. Ajallinen vaihtelu kohdistuu kaikkiin saavutettavuuden komponentteihin, sillä niin liikennejärjestelmä, palveluverkko, kuin ihmisten sijainnitkin vaihtelevat merkittävästi ajassa. Näiden yhteisvaikutuksesta saavutettavuus saattaa samalla alueellakin näyttäytyä eri ajankohtina hyvin erilaisena. Saavuttavuuden ajallinen vaihtelu olisikin tärkeää huomioida entistä paremmin esimerkiksi suunnittelussa. Lisäksi saavutettavuuden alueelliset rakenteet näyttäytyvät hyvin erilaisina riippuen siitä, millä kulkutavalla saavutettavuutta mallinnetaan. Autoilijan saavutettavuustodellisuus on toisenlainen kuin joukkoliikenteen käyttäjän. Tulokseni osoittavat, että myös luonnon dynamiikalla voi olla suuri merkitys saavutettavuuteen. Esimerkiksi Amazonian jokien vedenkorkeuden vuodenaikainen vaihtelu vaikuttaa suuresti navigoimiseen ja saavutettavuusrakenteisiin alueellisella tasolla. Liikenneverkon dynaamisuuden lisäksi myös ihmisten liike, ja ihmisten sijainnin dynaamisuus, tulisi ottaa huomioon saavutettavuusmallinnuksessa. Ihmisten vaihtelevien sijaintien tutkimus on perinteisesti vaikeaa, mutta uusilla aineistolähteillä, kuten sosiaalisella medialla, voidaan tuottaa tästä uutta tietoa. Toisaalta dynaaminen saavutettavuus on pitkälti riippuvainen kohdepisteiden, kuten palveluiden alueellisista rakenteista. Alueilla joissa liikenneverkko on melko vakiintunut, palveluiden rakennemuutoksilla saattaa olla liikennehankkeita suurempi vaikutus saavutettavuuteen. Menetelmällisesti väitöskirjani osoittaa eri datalähteiden ja menetelmien yhdistelyn tärkeyden datan spatiaalisen ja ajallisen tarkkuuden parantamisessa, yhdenmukaistamisessa, laadun arvioimisessa, ja rikastamisessa. Lopuksi haluan korostaa tieteellisen tiedon, menetelmien ja aineistojen avoimuuden tärkeyttä, sillä sen avulla tehty työ on mahdollista saada tehokkaasti osaksi käytännön suunnittelua ja päätöksentekoa. Väitöskirjassani kehittämäni työkalut ja sen aikana tuotetut datat onkin julkaistu pääsääntöisesti avoimesti. Tämän ansiosta niitä voidaan vapaasti hyödyntää ja niiden laatua voidaan arvioida kriittisesti. Avoimuuden seurauksena työssä kehitetyt menetelmät ovat jo päätyneet osaksi käytännön aluesuunnittelutyötä. Asiasanat: Saavutettavuus; Liikkuminen; Aika-tilallisuus; Multimodaalisuus; Matka-aika; Avoin data; GIS; Sosiaalinen media; Datatiede; Tiedonlouhinta; Alueellinen suunnittelu; Kansallispuistot; Suomi; Etelä-Afrikka; Perun Amazonia; Pääkaupunkiseutu; Tallinn

Geographic knowledge discovery from sparse GPS-data : Revealing spatio-temporal patterns of Amazonian river transports

A vast amount of spatio-temporal data has become available with the fast development of information technology and different monitoring systems over the last two decades. Position-aware devices are one of the most dominant sources for collecting movement data. Spatio-temporal information that is derived from the tracking devices enable to build movement patterns from the targets, and to calculate measurable motion parameters such as speed, change of speed or the direction of movement. This study utilized a specific pilot GPS-based monitoring system called Amazonian Riverboat Observation System (AROS) that was built to collect movement data of the local riverboats on the departments of Loreto and Ucayali in Peruvian Amazonia. AROS provides real-time GPS-data with coordinates and timestamp that indicate where and when the collaborating vessels are navigating. As an outcome of this thesis a specific analytical tool called Trajectory Reconstruction and Analysis Tool (TRAT) was developed. TRAT utilizes variety of geographic knowledge discovery methods to extract knowledge from movement data provided by AROS. Also spatio-temporal transportation characteristics in the study area were analyzed based on AROS data from the year 2012 and utilizing TRAT. This thesis focused on studying if there is seasonal and directional variation in transportation characteristics along the Amazonian rivers, and if river morphology affects the navigation. Also connection between water height of the rivers and travel speed of individual journeys was studied. Results of the thesis suggest that navigation along the rivers has seasonal and directional variation, and also the river morphology seems to affect the movement patterns of the vessels. On navigation route that was mostly meandering by river morphology, the downstream navigation was over 40% faster than upstream navigation during high water and intermediate, but during low water there was no difference between navigation directions. Seasonal variation was over 30% faster during high water compared to low water (on downstream direction). On upstream direction the navigation was fastest during low water but seasonal differences were considerably lower compared to downstream navigation. On navigation route that was mostly anastomosing by river morphology, the downstream navigation was approximately 20 % faster during the entire year. Results suggest that there is no seasonal difference in navigation characteristics along the larger and wider rivers, since the travel speeds were quite similar throughout the year. Fitting simple regression model between average travel speed of the journeys and water levels of the river revealed that there seems to be strong connection between travel speed and river height on the route along Ucayali river when travelled downstream (R2=0.73). On other cases that were studied, the results suggest that there is not connection between travel speed characteristics and river height. Comparing the results with earlier studies implied that the results of this thesis seemed to be fairly accurate. However, it is necessary to validate the results by doing cross-validations between data from different years observed with AROS. Transportation is in a key role when trying to find the factors affecting on development of a certain location. Thus transportation as means of accessibility has significant role in variety of contexts such as conversation, land use changes and deforestation. Results of this study could provide more accurate data for studies focusing on previously mentioned topics in the study area. Also utilization of TRAT in other contexts, such as studying global transportation patterns of professional vessels, could be possible by making few modifications to the tool.Informaatioteknologian ja erilaisten seurantajärjestelmien nopea kehitys viimeisten kahden vuosikymmenen aikana on mahdollistanut massiivisten spatio-temporaalisten tietovarantojen keräämisen. Paikannusteknologioilla varustetut laitteet ovat keskeisimpiä datalähteitä spatio-temporaalisen liikkumistiedon keräämiseen, ja tällainen data mahdollistaa erilaisten kohteiden (liikennevälineet, ihmiset jne.) liikkumisrakenteiden tutkimisen sekä erilaisten liikkumisparametrien kuten nopeuden, ja nopeuden sekä kulkusuunnan muutoksen laskemisen. Tässä tutkimuksessa hyödynnetään eristyistä pilotti-seurantajärjestelmää (AROS), joka on kehitetty keräämään jokilaivojen liikkumisdataa Loreton ja Ucayalin seuduilla Perun Amazoniassa. AROS mahdollistaa reaaliaikaisten laivojen sijantitietojen (koordinaatit) sekä aikatiedon (aikaleima) keräämisen. Tässä tutkimuksessa kehitettiin erityinen liikkumistiedonlouhintaan tarkoitettu analyysityökalu (TRAT), joka hyödyntää useita spatiaalisen tiedonlouhinnan menetelmiä informaation louhimiseksi AROS datasta. Tutkimuksessa tutkittiin, onko AROS datan perusteella jokinavigoinnissa nähtävissä vuodenaikaista vaihtelua vuoden 2012 aikana, ja vaikuttaako kulkusuunta sekä jokimorfologia navigointinopeuksiin. Tutkimuksessa tutkittiin myös, onko jokien vedenkorkeuksilla yhteyttä navigointinopeuksiin. Tutkimuksen tulokset osoittivat, että navigointi vaihtelee riippuen vuodenajasta sekä kulkusuunnasta, ja myös viitteitä jokimorfologian vaikutuksesta navigointiin oli paikoittain nähtävissä. Meanderoivilla jokiosuuksilla navigoiminen alavirtaan oli n. 40 % nopeampaa korkeanveden aikaan, mutta matalanveden aikaan eroa nopeuksissa ei ollut juuri nähtävissä. Vuodenaikaisvaihtelu oli selkeintä alavirtaan kuljettaessa, jolloin navigointi korkeanveden aikaan oli n. 30 % nopeampaa verrattuna matalanveden aikaan. Anastomoivilla jokiosuuksilla erot nopeuksissa eri kulkusuuntiin olivat vähäisemmät, ja navigointi oli keskimäärin 20 % nopeampaa alavirtaan (verrattuna ylävirtaan). Vuodenaikaisvaihtelua ei ollut juurikaan nähtävissä. Lineaarien regressiomalli jokikorkeuksien ja yksittäisten osareittien navigointinopeuksien välille osoitti, että yhteys oli selkeä (R2=0.73) osareiteillä, jotka kulkivat Ucayali-jokea alavirtaan. Muissa tutkituissa tapauksissa selkää yhteyttä ei löytynyt. Vertailemalla työn tuloksia aiempiin tutkimuksiin osoitti, että tulokset vaikuttavat olevan linjassa muiden tutkimusten tulosten kanssa. Työn tuloksia tulee jatkossa tosin vielä validoida vertailemalla vuoden 2012 tuloksia muiden vuosien tuloksiin AROS datan perusteella. Liikennejärjestelmät ovat keskeisiä tekijöitä, jotka vaikuttavat alueiden yleiseen kehitykseen. Yksi tapa kuvata liikennerakenteita on tarkastella paikkojen välistä saavutettavuutta, jolla on todettu olevan merkitystä lukuisiin eri yhteyksissä kuten maankäytön muutoksessa, deforestaatiossa sekä luonnonsuojelussa. Tämän tutkimuksen tulokset voivat tarjota tarkempaa dataa ja informaatiota liittyen edellämainittujen aiheiden tutkimiseen Perun Amazoniassa ja mahdolllisesti muillakin Amazonin alueilla. Kehitettyä analyysityökalua (TRAT) on myös mahdollista hyödyntää laajemmissa yhteyksissä, kuten globaalin laivaliikenteen tutkimuksessa, tekemällä pieniä muutoksia työkalun algoritmeihin

Exploring the linguistic landscape of geotagged social media content in urban environments

This article explores the linguistic landscape of social media posts associated with specific geographic locations using computational methods. Because physical and virtual spaces have become increasingly intertwined due to location-aware mobile devices, we propose extending the concept of linguistic landscape to cover both physical and virtual environments. To cope with the high volume of social media data, we adopt computational methods for studying the richness and diversity of the virtual linguistic landscape, namely, automatic language identification and topic modelling, together with diversity indices commonly used in ecology and information sciences. We illustrate the proposed approach in a case study covering nearly 120,000 posts uploaded on Instagram over 4.5 years at the Senate Square in Helsinki, Finland. Our analysis reveals the richness and diversity of the virtual linguistic landscape, which is also shown to be susceptible to continuous change.Peer reviewe

Detecting country of residence from social media data : a comparison of methods

Identifying users' place of residence is an important step in many social media analysis workflows. Various techniques for detecting home locations from social media data have been proposed, but their reliability has rarely been validated using ground truth data. In this article, we compared commonly used spatial and Spatio-temporal methods to determine social media users' country of residence. We applied diverse methods to a global data set of publicly shared geo-located Instagram posts from visitors to the Kruger National Park in South Africa. We evaluated the performance of each method using both individual-level expert assessment for a sample of users and aggregate-level official visitor statistics. Based on the individual-level assessment, a simple Spatio-temporal approach was the best-performed for detecting the country of residence. Results show why aggregate-level official statistics are not the best indicators for evaluating method performance. We also show how social media usage, such as the number of countries visited and posting activity over time, affect the performance of methods. In addition to a methodological contribution, this work contributes to the discussion about spatial and temporal biases in mobile big data.Peer reviewe

A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland

In this article, we present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. An hourly population distribution dataset is provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The dataset is validated by comparing population register data from Statistics Finland for night hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city, and examine population variations relevant to spatial accessibility analyses, crisis management, planning and beyond.Peer reviewe

User-Generated Geographic Information for Visitor Monitoring in a National Park : A Comparison of Social Media Data and Visitor Survey

Protected area management and marketing require real-time information on visitors’ behavior and preferences. Thus far, visitor information has been collected mostly with repeated visitor surveys. A wealth of content-rich geographic data is produced by users of different social media platforms. These data could potentially provide continuous information about people’s activities and interactions with the environment at different spatial and temporal scales. In this paper, we compare social media data with traditional survey data in order to map people’s activities and preferences using the most popular national park in Finland, Pallas-Yllästunturi National Park, as a case study. We compare systematically collected survey data and the content of geotagged social media data and analyze: (i) where do people go within the park; (ii) what are their activities; (iii) when do people visit the park and if there are temporal patterns in their activities; (iv) who the visitors are; (v) why people visit the national park; and (vi) what complementary information from social media can provide in addition to the results from traditional surveys. The comparison of survey and social media data demonstrated that geotagged social media content provides relevant information about visitors’ use of the national park. As social media platforms are a dynamic source of data, they could complement and enrich traditional forms of visitor monitoring by providing more insight on emerging activities, temporal patterns of shared content, and mobility patterns of visitors. Potentially, geotagged social media data could also provide an overview of the spatio-temporal activity patterns in other areas where systematic visitor monitoring is not taking place.Peer reviewe

Social media reveal that charismatic species are not the main attractor of ecotourists to sub-Saharan protected areas

Charismatic megafauna are arguably considered the primary attractor of ecotourists to sub-Saharan African protected areas. However, the lack of visitation data across the whole continent has thus far prevented the investigation of whether charismatic species are indeed a key attractor of ecotourists to protected areas. Social media data can now be used for this purpose. We mined data from Instagram, and used generalized linear models with site- and country-level deviations to explore which socio-economic, geographical and biological factors explain social media use in sub-Saharan African protected areas. We found that charismatic species richness did not explain social media usage. On the other hand, protected areas that were more accessible, had sparser vegetation, where human population density was higher, and that were located in wealthier countries, had higher social media use. Interestingly, protected areas with lower richness in non-charismatic species had more users. Overall, our results suggest that more factors than simply charismatic species might explain attractiveness of protected areas, and call for more in-depth content analysis of the posts. With African countries projected to develop further in the near-future, more social media data will become available, and could be used to inform protected area management and marketing.Peer reviewe

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

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 different mobile devices and social media platforms are a promising source of data for studying green spaces. User-generated data have, however, platform specific characteristics that limit their potential use. In this article, 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. Our results show that user-generated geographic information sources provide useful insights about being in, moving through and perceiving urban green spaces, as long as evident limitations and sample biases are acknowledged. Social media data highlight patterns of leisure time activities and allow further content analysis. 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 from active participants, but might be limited in spatial and temporal extent. Combining information from multiple user-generated data sets complements traditional data sources and provides a more comprehensive understanding of green space use and preferences.Peer reviewe