Olle Järv from the Digital Geography Lab attended as an expert panellist in the Nordic-Baltic Migration Conference “Cross-border Mobility in the Nordic-Baltic Region” organized by the Nordic Council of Ministers’ Office in Tallinn, Estonia on September 18, 2020. Olle participated in the second panel ”New Challenges in Cross-Border Mobility, Nordic-Baltic Region” together with Uffe Palludan (Palludan Fremtidsforskning), Jonas Wendel (Nordic Council of Ministers’), Rolle Alho (Uni Helsinki), and Saara Pellander as a moderator (Migration Institute of Finland). In the panel, Olle briefly introduced his BORDERSPACE research project on cross-border mobility and transnational people, and how these research topics benefit from novel data sources such as social media and mobile phone data.
Pääkaupunkiseudulla toimivan reittityökalun suositukset perustuvat reaaliaikaiseen tietoon ilmanlaadusta sekä kaupungilla tehtyihin melutasomittauksiin. Sovelluksen avulla liikkuja välttää vilkkaasti liikennöidyt kadut, mutta pääsee silti perille kohtuullisessa ajassa.
Helsingin yliopiston Digital Geography Lab -tutkimusryhmä on kehittänyt Green Paths -reittityökalun auttamaan jalankulkijoita ja pyöräilijöitä löytämään miellyttävimmät reitit. Nyt julkistettu sovellus on prototyyppi, joka toimii Helsingin, Espoon, Vantaan ja Kauniaisten alueella. Näissä kunnissa on saatavilla sovelluksen tarvitsemaa dataa.
Yleensä reittioppaat tarjoavat nopeimpia tai lyhimpiä reittejä, mutta miellyttävyyttä ja terveellisyyttä ei huomioida. Ohjaamalla pyöräilijät ja kävelijät miellyttävämpään matkaympäristöön reittiopas pyrkii edistämään kaupunkiliikkujien terveyttä ja hyvinvointia. Tutkimusryhmämme toivoo reittioppaan edistävän myös ilmastollisesti vastuullista ja kestävää kaupunkiliikennettä.
The new Green Paths routing tool helps pedestrians and cyclists to choose urban commuting routes with less air and noise pollution. The tool is a proof of concept of exposure-optimised routing. It functions in the Helsinki capital region where the necessary environmental data is available.
This novel routing tool suggests a set of routes to the user-defined destination that are less polluted than the shortest, often busy main road. This is different from the traditional routing concept that considers only distance or travel time for route choices.
The users of the Green Paths routing tool can follow their own preferences in making the final route choice that is quieter, has fresher air and feels more pleasant. As such, the user becomes an active decision-maker, empowered to choose the most appealing route with only a modest increase in travel time. The new Green Paths routing tool indeed aims to improve travel satisfaction. A pleasant travel experience makes commuters more likely to walk or cycle regularly. In this way, commuters using the Green Paths tool contribute to a more sustainable city with cleaner air.
We have just published our commentary regarding human mobility, mobile Big Data and the responses to COVID-19 pandemic in the Big Data & Societyjournal (see also the journal’s blog).
This commentary was motivated by our previous experience with mobile Big Data and recent work on changed human mobility derived from mobile phone data during the COVID-19 outbreak in Finland (e.g. Järv et al. 2020a; Järv etal. 2020b). It also draws from the knowledge and experiences of our peers around the globe.
In short, 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. We can benefit from the avalanche of mobile Big Data that makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. Yet, the current crisis also highlights serious limitations in the readiness to take the advantage of mobile Big Data for social good, at large, regarding technological, administrative and legislative aspects.
Exposure to noise pollution can cause various adverse health effects such as increased blood pressure and stress levels. Noise exposure has traditionally been assessed in terms of home location, as required by national and international policies. However, a substantial share of individuals’ total daily noise exposure is likely to happen while they are on the move. This evidently also affects the healthiness of active travel modes, walking or cycling, by reducing or even outweighing positive health effects of physical activity. Thus, there is a clear need for advancing exposure assessment beyond residential location to gain a true understanding of exposure profiles and their potential effects on health and well-being.
Journey-time exposure assessment can study spatially dynamic exposure to pollutants as people move through the urban environment. Furthermore, least-cost routing can be applied to find healthier paths with lower exposure levels to pollutants. This novel research field has a potential to support urban sustainability and equitability through increasing awareness of the qualities of travel environments, assessing population exposure, supporting individual mobility choices as well as planning healthier travel infrastructure throughout the urban fabric.
These tasks are not trivial. How to assess dynamic exposure to noise pollution? How to find routes with less noise than on the shortest route? How to develop a mobile navigation application for exposure-based route planning? These were the key questions I addressed in my master’s thesis: Quiet paths for people: developing routing analysis and Web GIS application, defended in May 2020 at the University of Helsinki.
Authors: Johanna Eklund, Ari-Pekka Jokinen, and Tuuli Toivonen
The coronavirus pandemic will have long lasting impacts on the conservation of biodiversity and protected areas. In many Nordic countries, people have been visiting green areas, protected areas included, more than ever. In the Global South however, the situation is almost the opposite, putting conservation at risk. In many developing countries, nature-based tourism has been important for financing biodiversity conservation. Tourism to protected areas has, however, ceased drastically during the global confinement strategies. The consequences of vanishing tourism are very visible for example in Madagascar, one of the world’s biodiversity hotspots, with the highest number of threatened species globally. While being rich in biodiversity, Madagascar is economically among the poorest nations in the world. Madagascar currently has protected 7.5 % of its terrestrial land area, but this protection relies on funding from abroad. Previous research (Eklund et al. 2016 & Eklund et al. 2019) have shown that the protected areas have the capacity to curb deforestation, but funding is needed for the maintenance of conservation actions.
In Madagascar, tourism contribute to approximately 30 % of the annual income of the protected area agency Madagascar National Parks (MNP). MNP is managing some of the most visited national parks in the country. Due to the pandemic there have been concerns raised that the loss of tourism revenue could make the protected areas more vulnerable to illegal encroachment, such as poaching and deforestation. Both Mongabay and National Geographic have reported about these concerns recently. So far the concerns have been based on expert opinions and anecdotal evidence. In this writing, we provide evidence that the protected areas in Madagascar are experiencing rapid increases in human induced fires during the corona crisis.
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.
Olle Järv, Elias Willberg, Tuomas Väisänen, Tuuli Toivonen
According to Statistics Finland (2018), there are more than half a million summer cottages around Finland. They are mostly located in rural areas close to waterbodies – lakes and seaside. In a country with 2.7 million households, such a high number of cottages may change the population patterns drastically once people would move to their cottages in large numbers. This is a potential risk during the epidemic outbreak such as the COVID-19 – health care services are designed mostly based on the permanent population and the cottage dwellers might break the balance in sparsely populated areas. Our preliminary findings already indicated that despite travelling restrictions due to the COVID-19 outbreak in Finland many people escaped from big cities to their summer cottage, see here. But was it really the lure of the summer cottages that explain the mobility patterns to more rural countryside?
Method: We used anonymised and aggregated mobile network data from Telia Crowd Insights such as activity location data as in our previous analyses, and further examined trip data between municipalities. Activity location data indicates the presence of people in a municipality – once a person stays at least 20 minutes in one municipality during one day, then his/her presence is counted as one activity location in given municipality. For example, when having a longer lunch break in a gas station during a long-distance travel. Thus, a person can have several activity locations in different municipalities each day. Trip data is calculated between the two consecutive activity locations at municipality level. In trip data, actual long-distance travel is counted as two different trips, if a longer break is taken during travelling. For example, travel from Helsinki to Rovaniemi by train via Jyväskylä is counted as two trips in data, because train stops at Jyväskylä longer than 20 minutes. These nuanced must be taken into account while reasoning findings.
Olle Järv, Elias Willberg, Tuomas Väisänen, Tuuli Toivonen
Tilastokeskuksen mukaan (2018) Suomessa on yli puoli miljoonaa kesämökkiä. Pääosin ne sijaitsevat harvemmin asutuilla alueilla lähellä vesistöjä. Maassa, jossa on yhteensä 2.7 miljoonaa kotitauloutta, kesämökkien määrä voi vaikuttaa merkittävästi väestön sijoittumiseen, etenkin jos ihmiset siirtyvät kesämökeilleen massoina, kuten tapahtui COVID-19 kriisin alettua. Kun terveyspalvelut ovat pääosin suunniteltu vakituisen väestön pohjalta, kesämökkeilijät voivat merkittävästi lisätä terveyspalveluihin kohdistuvaa taakkaa. Alustavat tuloksemme viittasivat siihen, että moni siirtyi kesämökilleen isoista kaupungeista COVID-19 epidemian seurauksena huolimatta hallituksen matkustusrajoituksista (linkkI). Mutta selittikö kesämökkien houkutus todella löytämämme liikkumisrakenteet?
Menetelmä: Kuten aiemminkin, käytimme Telian Crowd Insights-palvelun anonymisoitua ja yleistettyä mobiiliverkkodataa, kuten aktiviteettisijainteja ja matka-aineistoa. Tilastotasoisista aineistoista ei ole mahdollista tunnistaa yksittäisien ihmisten tai pienten ryhmien liikkeitä. Aktiviteettiaineisto kuvaa väestön oleilua kunnan alueella, jossa jokainen 20 minuutin oleilu yhden päivän aikana tallennetaan aktiviteetiksi kyseisen kunnan tilastoon. Tällöin esimerkiksi pidempi kahvitauko huoltoasemalla lasketaan aktiivisuussijainniksi ja niitä voi olla useampia päivän aikana eri kuntien alueella. Matka-aineisto puolestaan lasketaan kuntatasolla kahden akvitiviteettisijainnin väliksi. Tällöin matka-aineistossa pitkät matkat voivat tulla lasketuksi kahdeksi tai useammaksi matkaksi riippuen pitkien taukojen määrästä. Esimerkiksi junamatka Helsingistä-Rovaniemellä lasketaan useaksi matkaksi mikäli juna pysähtyy osalla asemista, kuten Tampereella, yli 20 minuutiksi. Nämä tekijät tulee ottaa huomioon tuloksia tulkitessa.
Olle Järv, Elias Willberg, Tuomas Väisänen, TuuliToivonen
Finland, like all other countries in Europe, was hit by the emergence of the covid-19 in March 2020. On Thursday 12th of March, the government gave the first serious recommendations of cancelling big public gatherings and encouraging distant working. On Monday 16th March the government presented a state of emergency. The schools and public services like museums and libraries were closed and visits to elderly care homes were prohibited. Travel was to be limited to minimum. Historically, the border between Sweden and Finland was closed on the 19th March, restricting the workforce to move across the border in the northern areas and Åland islands. As the glittering snow of Lapland was still attracting tourists from the south, the ski resorts were closed on the 23rd March. As the strongest and an unprecedented measure, the border of the Helsinki-Uusimaa region was closed for personal traffic on the 28th March.
We at Digital Geography Lab received data from mobile phone operator Telia to analyse how these recommendations and restrictions were followed. Below are some first views on the results, showing how the presence of people changed in the 310 municipalities of Finland during February – March 2020. We concentrate on March and compare the presence of people in different municipalities to the first week of February (1.-7.2.2020) – a baseline week before the start of the school winter holidays and the emergence of Covid 19. The analysis is based on anonymised and aggregated mobile network data (Telia Crowd Insights activity data). A single activity is recorded of every 20 minute stay in one location. Some February days (12.2., 13.2, 25.2.) are missing in the dataset.