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.
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.
The corona virus pandemic hit Madagascar on March 20, 2020. The government stopped international flights on the same day and shut down the country’s three major cities (Antananarivo, Toamasina and Fianarantsoa). Protected areas have been closed for visitors since March 23rd 2020. The lockdown has been partially lifted since April 20th, but the protected areas remain closed from visitors.
We used satellite based data on active fires made openly available by NASA to see if there are signals of increased human activity inside the protected areas. This type of data has previously been used as a proxy for human caused bushfires (Nolte & Agrawal 2012). We were interested to follow temporal trends and investigate which areas are most affected. The aim was to provide a tool that can be useful for protected area managers. For a more detailed report on methods, see the end of the blog post.
We stress that the figures presented below are tentative results from an initial inspection of the data. The analyses are ongoing and we will follow the trends during the coming months to get a better understanding of the long-term effects the pandemic has on protected areas.
From the first of March 2020 until the 17th of May 2020 altogether 1013 active fires were detected inside protected areas in Madagascar (Fig. 1). This is an 81% increase compared to the same time period in 2019. However, not all protected areas are affected. There are many with no change compared to last year (n=64) and some with less fires this year (n=20). In total 29 protected areas had more fires this year than last year (Fig. 2), and of these 6 showed a drastic increase (more than 30 fires more this year).
Figure 1. Number of active fires/thermal anomalities (normal and high confidence) inside protected areas in Madagascar for March 1- May 17 for 2019 and 2020.
Figure 2. Map of Madagascar and the terrestrial protected areas. Graduated colours show the difference in fires this year compared to the corresponding period in 2019 (March 1- May 17). By clicking on the map you will be taken to an interactive version with more detailed information about the temporal trends for each protected area. NOTE: Opens best in Firefox or Chrome on a desktop https://blogs.helsinki.fi/digital-geography/files/2020/06/folium_map.html
Our preliminary results show clearly that the COVID-19 pandemic has affected the protected areas in Madagascar and the human pressures have increased. It is clear that the protected areas in the Western dry forests of Madagascar are the most affected so far. Fires are commonplace in Madagascar even during normal times. People often burn and clear land to plant crops before the start of the rainy season that runs from November to April. The fires usually appear in July, but this year they have started to increase early. Of the nine hardest hit protected areas, especially Tsingy de Bemaraha has been popular among visitors. According to MNP, Tsingy de Bemaraha was the fifth most visited park in the country last year. The dramatic increase of fires in Tsingy de Bemaraha could well be related to the loss of tourism revenue, meaning that people now have to rely on the land instead. The fires in the park extend all the way to the interior, not just around the borders. Ankarafantsika National Park was severely hit by fires last autumn and MNP was calling for international help to manage the fires already then, according to Mongabay. This makes the increased burning this year very troublesome. Bongolava, adjacent to Ankarafantsika national park, has had uprisings before, where farmers have demanded their right to grow corn in the area (Mongabay).
In conclusion, it seems the COVID-19 pandemic has caused an increase in bushfires inside the protected areas. To what extent this can be traced to the lack of tourists and the loss of tourism generated income is still unclear, but the tools developed will allow us to follow the long-term impacts of the crisis. Historically, many of the Malagasy living around protected areas have compared conservation organizations and tourists to the previous colonial masters, claiming the land for the benefit of outsiders. Efforts have been made to convince the locals that protected areas can bring alternative livelihoods and that they can profit from keeping nature intact. With tourism revenues now gone, it is easy to understand that some must feel betrayed by this promise.
This is ongoing work and we will continue to track the situation during the coming months. We will use the average of the last five years as a baseline, and not just last year as in this blog post. The development work will continue in collaboration with Madagascar National Parks and Forum LAFA, the network for terrestrial protected area managers in Madagascar.
We used NASA´s Visible Infrared Imaging Radiometer Suite (VIIRS) Corrected Reflectance Imagery data to detect signals of potential fires in the protected areas of Madagascar. VIIRS data is available on a daily basis and is therefore suited to explore time trends at fine resolution. We compared the active fires this year to the corresponding time last year. In our analyses we included the fires that were of high or normal confidence and omitted those that could have been other bright reflections than fires. We included the terrestrial protected areas listed in the Protected Planet Database for Madagascar. Some of these areas might not yet have full designated status, but were included to track trends also in proposed protected areas. We did not limit our analysis to only forested areas, but included fires in all land-use types.
The Digital Geography Lab at the University of Helsinki is an interdisciplinary research team focusing on spatial big data analytics for fair and sustainable societies. See previous blog posts about the effects of COVID-19 on mobility patterns in Finland here and here.
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.
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.
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.
The Digital Geography Lab recently analyzed changes in population distribution, and mobility patterns in Finland based on aggregated and anonymized mobile network data acquired from a local operator. The local newspaper Helsingin Sanomat had access to more detailed mobile network data allowing the comparison of activity in green spaces between spring 2019 and 2020, showing a significant increase of people, for example, in national parks in the Helsinki Region.
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.
Read the full article (written before the covid-crisis) at: https://authors.elsevier.com/sd/article/S0169204619313635
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
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.
Findings: We compared the change in the number of people in municipalities and the relative distribution of summer cottages. We found a strong proof that urban dwellers did escape to their summer cottages amid the COVID-19 outbreak. Mobility flows to summer cottages started already after the first official guidance on 12th March, and people were in their second-homes already before travelling restrictions were made official by the government. Data indicates that on average some 370 additional people for every 1000 summer cottages in a municipality arrived to a municipality towards the end of March.
To analyse the phenomenon further, we chose six municipalities for further analysis. All of these municipalities are rich in summer cottages and showed increasing number of dynamic population during the COVID-19 outbreak. We analysed the flow of people using aggregated and anonymized trip data (see above).
The mobility flows show varying geographical catchment areas for different municipalities, indicating also different risk levels of potential virus spreading outbreak. Municipalities in Central Finland received people from more diverse directions, whereas in coastal areas and closer to country border origins of mobility is more concentrated to close by municipalities or other shoreline locations. Thus, from the perspective of human mobility, summer cottage areas in the central parts of the country play stronger mediating role for virus transmission between different regions in Finland.
Figure 1. The linear correlation between the increase of people compared to typical workdays and the relative amount of summer cottages per 1000 inhabitants at municipality level gives the correlation coefficient 0.75. Municipalities in the Åland Islands are excluded from the correlation as ferry passengers passing through the archipelago affect their data. On average, municipalities gained some 11 % of additional people in case of municipalities that increased the presence of people in the last week of March compared to the baseline week (n = 174).
Figure 2. Temporal dynamics of temporary population compared to the baseline working days (average from Mon 3.2. to Thu 6.2.) in case of six well-known ”summer cottage” municipalities in Finland. In addition to typical weekend visits to summer cottages, people stayed there also during the winter holiday season (15.2-8.3). After just a couple of typical workdays people started to move back to their summer cottages since Thursday (12.3.) regarding the COVID-19 outbreak, but with much higher volume.
Figure 3. We approximated the “occupancy” of summer cottages while people escaped cities due to the COVID-19 crisis. The amount of increased people per 1000 summer cottages in a municipality is considered as an indicator for the summer cottage “occupancy”. We examined municipalities that increased the presence of people in the last week of March compared to the baseline week (n = 174). These municipalities gained some 370 additional people per 1000 summer cottages in a municipality, on average. Half of these municipalities gained additional people from 230 to 530 per 1000 summer cottages. More specifically, we rank ordered municipalities by summer cottage count per inhabitants and selected top 25 “summer cottage” municipalities – half of these municipalities gained additional people from 230 to 400 per 1000 summer cottages in a municipality.
Figure 4. Three different geographical tales of human mobility to popular summer cottage municipalities in Finland: 1) Sysmä, 2) Puumala, and 3) Kustavi. The maps show the geographical catchment area of each observed municipality. In each selected municipality, most people arrive from the close neighboring municipalities; however, these also attract people widely from different parts of Finland.
Figure 5. Mobilities to Sysmä, Puumala, and Kustavi in the context of the COVID-19 infection situation. The mobility flows reveal varying geographical catchment areas for different municipalities, which also can indicate different risk levels of potential virus transmission for different municipalities. For example, Uusimaa region in South Finland (including the Capital of Helsinki) has been the hotspot of the COVID-19 outbreak in Finland and had the highest infection rate per 100 000 inhabitants at late March 2020 (data by healthcare districts in 29.3.2020). Considering all trips to a municipality between 12.3 – 29.3, only 13% (Sysmä), 9.5% (Puumala) and 7.3% (Kustavi) were from Uusimaa.
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.
Löydökset: Vertasimme muutosta väestön määrässä eri kunnissa sekä kesämökkien suhteellista jakaumaa kunnittain. Löysimme vahvaa näyttöä siitä, että kaupunkilaiset todella pakenivat kesämökeilleen COVID-19 epidemian seurauksena. Liikkumisvirrat kesämökeille alkoivat jo ensimmäisten hallituksen suositusten jälkeen 12. maaliskuuta. Ihmiset siis olivat jo kakkoskodeissaan ennen kuin matkustusrajoitteet astuivat voimaan. Aineiston perusteella jokaista 1000 kesämökkiä kohden, kuntiin saapui keskimäärin noin 370 ihmistä lisää maaliskuun loppua kohden.
Päästäksemme tähän ilmiöön paremmin käsiksi, otimme kuusi kuntaa tarkempaan tarkasteluun. Kaikki kunnat ovat kesämökkivaltaisia, joissa väestön määrä oli kasvanut COVID-19 kriisin aikana. Tarkastelimme ihmisvirtoja anonymisoidulla ja yleistetyllä matka-aineistolla (ks. ylempää).
Ihmisvirtojen perusteella tarkasteluilla kunnilla on varsin erilaiset vaikutusalueet, mitkä osaltaan vaikuttavat viruksen leviämisen riskiin. Keski-Suomen kuntiin saapuu väestöä useammasta eri suunnasta kun puolestaan rannikkoalueilla ja Itä-Suomessa liikkumisvirtojen lähtöalueet keskittyvät useammin lähialueiden kuntiin. Liikkumisen näkökulmasta keskellä maata sijaitsevilla kesämökkikunnilla oli siten suurempi riski levittää virusta saapuvien ja lähtevien ihmisten mukana eri puolille Suomea.
Kuva 1. Lineaarinen korrelaatio ihmisten määrän lisääntymisen (verrattuna normaaliin työpäivään) sekä kesämökkien suhteellisen osuuden jokaista 1000 asukasta kohden välillä. Kuntatason vertailu antaa korrelaatiokertoimeksi 0,75. Ahvenanmaan kunnat on jätetty tarkastelun ulkopuolelle, koska ohikulkevat risteilymatkustajat vaikuttavat kuntien lukuihin. Keskimäärin ne kunnat, joissa väestömäärä kasvoi maaliskuun viimeisellä viikolla, kasvattivat väestöään 11 % verrattuna tarkastelujaksoon (n=174).
Kuva 2. Väliaikaisen väestön ajallinen vaihtelu verrattuna tarkastelujakson arkipäiviin (keskiarvo maanantai 3.2. – torstai 6.2.) kuudessa kesämökkikunnassa. Tyypillisten viikonloppuvierailujen lisäksi hiihtolomakausi erottuu ajanjaksona, jolloin ihmiset viettivät aikaa näissä kunnissa ennen COVID-19 epidemiaa. Epidemian alettua, ihmiset alkoivat siirtymään takaisin kesämökkikuntiin 12. maaliskuuta alkaen entistä suuremmissa määrin.
Kuva 3. Arvioimme myös kesämökeillä oleilua COVID-19 kriisin aikaan. Lisääntyvän väestön määrä 1000 kesämökkiä kohden kuntatasolla toimi tässä tapauksessa indikaattorinamme kesämökkioleilusta. Tarkastelimme kuntia, joissa väestön määrä kasvoi maaliskuun viimeisellä viikolla verrattuna tarkastelujaksoon. Nämä kunnat vastaanottivat keskimäärin 370 ihmistä lisää jokaista 1000 kunnan alueella sijaitsevaa mökkiä kohden. Puolet näistä kunnista saivat 230-530 ihmistä lisää 1000 kesämökkiä kohden. Valitsimme vielä 25 kuntaa, joissa on eniten kesämökkejä ja tarkastelimme niitä erikseen. Puolet näistä kunnista saivat 230-400 ihmistä lisää 1000 kesämökkiä kohden.
Kuva 4. Kolme erilaista maantieteellistä tarinaa ihmisten liikkumisesta suosittuihin kesämökkikuntiin: 1) Sysmä, 2) Puumala ja 3) Kustavi. Kartat osoittavat maantieteellisen vaikutusalueen jokaiselle tarkastelulle kunnalle. Jokaisessa niistä valtaosa ihmisistä on saapunut läheisistä kunnissa, mutta ne houkuttelevat ihmisiä myös eripuolilta Suomea.
Kuva 5. Liikkuminen Sysmään, Puumalaan ja Kustaviin suhteessa COVID-19 epidemian tilanteeseen. Liikkumisvirrat paljastavat erilaisia vaikutusalueita eri kunnille, joka myös indikoi erilaista riskiä viruksen leviämiselle. Esimerkiksi Uudellamaalla, joka on ollut suurin epidemian keskittymä Suomessa, oli korkein tartuntatapausten määrä 100 000 asukasta kohden maaliskuun lopulla (epidemiatilanne 29.3.2020). Kaikki kuntien väliset matkat huomioiden välillä 12.3 ja 29.3, kuitenkin vain 13% Sysmään saapuneesta väestöstä saapui Uudeltamaalta. Vastaavat luvut Puumalaan ja Kustaviin saapuneen väestön osalta olivat 9.5% ja 7.3%
Olle Järv, Elias Willberg, Tuomas Väisänen, Tuuli Toivonen
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.
Our inspection shows, for example:
Figure 1. Inter-municipality mobility in Finland dives from mid-March. The activity location data shows the presence of people in a municipality. During typical times, people visit and spend time in several municipalities during a day. Extra mobility between municipalities takes place during the winter holiday season in Finland (17.2 – 6.3. or 15.2-8.3.). The government gave a recommendation to avoid travelling and promoting remote working since at the beginning of the Covid-19 outbreak 12.3. That recommendation was taken seriously – people’s activity spaces were rapidly limited. The governmental recommendation was a clear turning point, the later mobility restrictions by law influenced less.
Figure 2. The animation shows the relative change in number of people by municipality during March. People are accounted for each municipality they spend time in during the day. The baseline period for the daily comparisons is the first week of February (1.2 – 7.2.). (If the video does not show, see the animation here: https://bit.ly/2YGCMoO)
Figure 3. Inter-municipality mobility in the Helsinki metropolitan (HMA) area follows the national pattern of declining mobility, but with much greater effect. Typically, the HMA has a stable mobility between municipalities. Every year February is special as people travel during the school winter holiday week. Then almost 10% of people left the region (many to Lapland). After the governmental recommendation to avoid travelling and restrict social interactions, the presence of people in the HMA decreased drastically. This happened already before the lockdown of the Uusimaa region (28.3.). Before the Uusimaa lockdown inter-municipality mobility had decreased some 25% compared to a typical week (1.2 – 7.2.) as people stay home and avoid social activities.
Figure 4. During the winter holiday season (15.2 – 8.3.) the presence of people in the in six municipalities with a skiing resort in Lapland (Inari, Kittilä, Kolari, Kuusamo, Muonio, Pelkosenniemi) is booming as usual. Working days show some 40% increase of people compared to the baseline week (red line). Furthermore, Saturdays show even 60-80% increase of people compared to a baseline Saturday (1.2.). Interestingly, the governmental guidance to avoid travelling since 12.3. did not affect Lapland – some people wanted to have their skiing holiday! Only after the early closure of skiing resorts since 22.-23.3. visitors are sharply decreasing from Lapland – by the last Sunday (29.3.) there are 35% less people than the baseline Sunday (2.2.).
Figure 5. The relative change in the number of people spending time in municipality on 18th and 22nd March, compared to the baseline week. Declaring state of emergency starts to limit travelling to big cities in the south. Visitors leave the northern municipalities only when the ski resorts are to be closed.
Figure 6. The relative change in the presence of people between the last week of March (22.3. – 29.3.) and the baseline week (1.2. – 7.2.). At the end of March, the most severe restrictions were in place, including the closure of Uusimaa and the Lapland skiing resorts. The biggest decrease in the presence of people is seen in the smallest municipalities of the Åland archipelago, as municiplaities in Åland Islands had 25 % – 35 % less people compared to the baseline. Partly the change is explained by the stopping of the passing ferry traffic. The biggest increase in the presence of people took place in smaller municipalities that have a large amount of summer cottages. Despite travelling restrictions, some people escaped from big cities to their summer cottage. For example, Kustavi municipality hosted some 70-80 % more people during the last week of March compared to baseline. The summer cottage municipalities such as Puumala, Sysmä, Luhanka, Kuhmoinen and Taivassalo also gained people.
Figure 7. The presence of people in 30 biggest municipalities in Finland is clearly negative during the last week of March, compared to normal. During the Covid-19 restrictions, cities having the biggest commuting flows (Helsinki, Vantaa, Tampere, Turku, Vaasa) are the ones losing the most people. Oulu seems to be an exception here, but for a reason: Oulu municipality covers a very large area and thus commuters living within the municipality do not cross municipality borders. Some of the biggest municipalities have remained with the same number of people, including Kouvola, Salo, Lohja and Mikkeli. These municipalities have, however, also significant amount of summer cottages. In all cases, the presence of people during weekends is lower during the last week of March compared to the baseline week in the beginning of February.
Figure 8. Uusimaa lockdown, day 1: The number of people in major cities and their surrounding regions is lower while many summer cottage municipalities are having more people than normally. If comparing to previous maps, the Uusimaa lockdown does not change the pattern drastically anymore at national level. People have limited their travelling already earlier.
Olle Järv, Elias Willberg, Tuomas, Väisänen, Tuuli Toivonen
Koronavirus mullisti elämämme maaliskuussa 2020. Vapaa liikkuminen loppui ja ihmiset linnoittautuivat koteihinsa mahdollisuuksien mukaan. Torstaina 12.3. hallitus antoi ensimmäiset konkreettiset suositukset liikkumiseen liittyen: etätöitä suositaan ja isot tapahtumat perutaan. Jo seuraavan viikon alussa julistettiin poikkeustila, jonka turvin suljettiin koulut, kirjastot, museot ja vierailut vanhainkoteihin keskeytettiin. Matkailu kehotettiin rajaamaan minimiin yleisesti ja rajantarkistukset otettiin käyttöön kaikilla Suomen rajoilla, myös Ruotsin suuntaan. Tämä rajoitti myös monien työmatkoja Tornionjokilaaksossa ja Ahvenanmaalla. Pohjoisen hohtavat hanget houkuttelivat vielä väkeä, joten hiihtokeskusten sulkemisesta pikavauhdilla ilmoitettiin 22.3. Merkittävimpänä toimena vapaan liikkumisen Suomessa nähtiin Uudenmaan maakunnan rajan sulkeminen. Tieto sulusta annettiin maaliskuun viimeisellä viikolla ja varsinainen sulku astui voimaan keskiyöllä 28.3. Mitä liikkumiselle oikeastaan tapahtui näiden viikkojen aikana? Jäivätkö suomalaiset kotiin ja missä niin tapahtui ja koska?
Digital Geography Lab tutkimusryhmämme sai käyttöönsä matkapuhelinoperaattori Telian anonymisoitua ja yleistettyä mobiiliverkkodataan perustuvaa kuntatasoista tilastotietoa helmi-maaliskuulta 2020. Data on poiminta Telian Crowd Insights datapalvelusta, ja se kuvaa väkijoukkojen oleskelua tietyssä kunnassa aina yhden päivän aikana vähintään 20 minuuttia kerrallaan. Alla näet joitakin nostoja vertailuistamme eri alueiden ja ajanjaksojen välillä. Vertailuajankohtana käytämme helmikuun ensimmäistä viikkoa, jolloin korona oli vasta kaukomaiden uutinen eivätkä koulujen lomat vielä vaikuttaneet ihmisten liikkumiseen.
Ensimmäiset tuloksemme osoittavat, että
Kuva 1. Kuntien välinen liikkuminen notkahtaa reilusti maaliskuun puolivälissä. Käyttämämme lähtöaineisto kertoo kunnan alueella oleskelevien ihmisten määrän. Tavallisena aikana sama ihminen saattaa oleskella päivän aikana usean kunnan alueella. Hallituksen suosituksen vaikutus näkyy selvästi aineistossamme: maaliskuun puolivälissä kunnanraan ylittäminen harvinaistuu. Suurin muutos käynnistyy heti ensimmäisten suositusten jälkeen. Siihen verrattuna lainsäädännöllisillä sulkutoimilla on paljon vähemmän vaikutusta kuntien rajat ylittävään liikkumiseen.
Kuva 2. Animaatio näyttää kunnissa aikaa viettävän väestön muutokset maaliskuussa. Vertailukohtana on helmikuun ensimmäinen viikko (1.2 – 7.2.). (Jos video ei näy, katso animaatio täällä: https://bit.ly/2YGCMoO)
Kuva 3. Kuntien välinen liikkuminen putoaa pääkaupunkiseudulla paljon muuta maata voimakkaammin. Talvilomaviikoilla helmikuussa nähdään tavallista vaihtelua: monet suuntaavat lomille ja pääkaupunkiseudun väkimäärä putoaa noin 10 %. Hallituksen ensimmäisten suositusten jälkeen alkaa korona-alamäki. Uudenmaan rajan sulkeminen ei enää suuremmin muuta tilannetta pääkaupunkiseudun kunnissa (Helsinki, Espoo, Vantaa, Kauniainen).
Kuva 4. Talvilomakaudella (15.2 – 8.3.) ihmisten määrä Lapin hiihtokunnissa (Inari, Kittilä, Kolari, Kuusamo, Muonio, Pelkosenniemi) kasvaa huimasti helmikuun alkuun verrattuna. Arkipäivien väestönlisäys on lähellä 40 %. Lauantain vaihtopäivänä väkimäärä melkein tuplaantuu. Hallituksen ohjeistus liikkumisen rajoittamisesta ei juuri vaikuta Lappiin, vaan lomailijat suuntaavat edelleen pohjoiseen. Nopea pudotus seuraa Lapissa vasta 22.3. kun hiihtokeskukset julistetaan suljettaviksi jo heti 23.3. Maaliskuun viimeiseen sunnuntaihin mennessä lapin kuntien väkimäärä on painunut reilusti helmikuun alun lukemista.
Kuva 5. Väkimäärä kunnissa poikkeustilan alkaessa (18.3.) ja hiihtokeskusten sulkeutumisen tienoilla (22.3.). Isot kaupungit alkavat hiljentyä heti poikkeustilan myötä. Pohjoisen hiihtokeskuskunnissa muutos on hitaampi.
Kuva 6. Maaliskuun viimeinen viikko tuo melkoisia muutoksia kunnissa vierailevien ihmisten määrään. Lapin hiihtokeskusten sulkeminen ja Uudenmaan rajan sulku näkyvät kuntien väkimäärissä. Yksi suuren muutoksen alueista on Ahvenanmaa, jonka kunnissa oleilevasta väestä putoaa pois 25-35 % normaaliin verrattuna. Muutosta selittää kuitenkin Ahvenanmaan ohikulkevan laivaliikenteen pysähtyminen. Muuten pienten mökkikuntien tilanne on toinen: Kehotuksista huolimatta mökkikuntien väkimäärä kasvaa. Esimerkiksi Kustavissa on väkeä 70-80% enemmän kuin vertailuviikolla. Samaa trendiä näkyy muissakin mökkikunnissa: Puumalassa, Sysmässä, Lunhangalla, Kuhmoisissa ja Taivassalossa.
Kuva 7. Suomen 30 suurimmassa kunnassa väkimäärä on selvästi normaalia alhaisempi maaliskuun viimeisellä viikolla. Liikkumisrajoitukset pitävät ihmiset kotona ja se näkyy työpaikkakunnissa aikaa viettävien ihmisten vähenemisenä. Erityisen selvä tilanne on Helsingissä, Vantaalla, Tampereella, Turussa ja Vaasassa. Oulu poikkeaa muista, mutta sen alue on niin laaja, ettei päivittäisten työmatkaajien puuttuminen muuta kuntatasoista tilastoa.
Kuva 8. Uudenmaan sulku voimassa. Väestön jakautuminen on aika samanlainen kuin jo viikkoa aiemmin: isoissa kaupungeissa väkeä on normaalia vähemmän, mökkikunnissa normaaliviikkoa enemmän. Suurimmat muutokset tapahtuivat jo ennen sulkua.
A new paper out! Study “Identification of ecological networks for land-use planning with spatial conservation prioritization” by Joel Jalkanen, Tuuli Toivonen, and Atte Moilanen has just published a study in the Landscape Ecology journal. In the paper, we describe the work where we identified large well-connected ecological networks and ecological corridors for the Regional Council of Uusimaa, an authority responsible for regional planning in the Uusimaa province in Southern Finland. We used the Zonation spatial prioritization software in a novel way for identifying large well-connected structures, and the rarely-used corridor retention tool in Zonation for identifying ecological corridors. Zonation has been previously used to support regional zoning in Uusimaa, and dozens on layers of biodiversity data was available in the area.
It is quite straightforward to identify local high-priority areas (such as areas of high habitat quality) from Zonation results. In the case of Uusimaa, biodiversity is greatly scattered and concentrated in the top-20% priority areas. Ensuring the regional-level connectivity would be, indeed, highly important.
Uusimaa has a strong human influence, and high-priority areas are scattered. Picture from the article.
Ecological networks are more than just core patches and links in-between them. Connectivity essentially means variation of habitat quality in space: some parts of the landscape can be optimal for reproduction, other parts support dispersal but no reproduction, and some parts are more or less useless or hostile for given species. In human-modified landscapes (such as Uusimaa), human activities can be considered to decrease local habitat quality so much that some parts of the landscape hardly support even dispersal of the species at large. Therefore, instead of drawing links between top-priority patches, we wanted to see where the matrix around the top-priority patches has sufficient habitat quality so that supports biodiversity in general. The question then is how to separate those parts of the landscape that are so human-influenced and low in habitat quality that they do not support regional connectivity from Zonation results. As we all know, Zonation priority rank is always a map with linear values from 0.0 to 1.0, and the map itself does not tell whether it is the highest 20, 50, or 80% of the landscape that is relevant for biodiversity at large.
To answer that problem, we combined the two spatial outputs of Zonation: traditional rank map and the weighted range-size corrected richness map (WRSCR) that describes the richness of habitats and species in Uusimaa. Compared to the rank map, WRSCR map tells where there are no biodiversity values. However, WRSCR does not include any kind of complementarity element unlike the rank map. Thus, spatial combination of the two maps allowed us to identify large structures where there are much biodiversity and high-priority areas and that are separated by more degraded parts of the landscape. Those structures are the large-scale ecological networks in the region.
Workflow for identifying networks from Zonation outputs. Picture from the article.
Seven large ecological networks in Uusimaa. Picture based on the original Finnish report.
We used the corridor retention tool of Zonation to identify ecological corridors that combine large connected structures via human-modified parts of the landscape. The interpretation of the corridors is that they act as bottlenecks in-between well-connected landscapes. Those bottlenecks should not be narrowed.
Ecological corridors are connectivity bottlenecks between e.g. large networks. Picture from the article.
We express our gratitude to the Regional Council of Uusimaa for their support and interest in developing the use of spatial prioritization and Zonation in regional land-use planning in Finland! Our history of fruitful collaboration already goes back for many years.
In the end of 2019, SYKE, the Finnish Environmental Institute, arranged a miniseminar on mobility research in urban, rural and touristic settings. The seminar addressed mobility research interrelations with spatial planning and governance, stakeholder engagement in spatial and transportation planning, sustainable mobility challenges in remote and touristic settings, and various methods for acquiring, processing and analysing mobility data.
The seminar was held in Helsinki and was part of the InterReg Baltic Sea-funded project MARA. The overall aim of the project is to address mobility and accessibility challenges in rural areas. Project activities and the perspectives and challenges of various mobility data were presented in the seminar by Kari Oinonen (SYKE), Age Poom (Digital Geography Lab, University of Helsinki & Mobility Lab, University of Tartu), Daniel Brandt and Tobias Heldt (both from CeTLeR, Dalarna University). SYKE researchers introduced their studies on GIS use in urban and rural planning (Ville Helminen), rural mobility, accessibility and travel related to second homes (Antti Rehunen), public participatory GIS (Elina Nyberg) and the architecture of spatial data infrastructure in SYKE (Kaisu Harju).
Spatial planning and governance require data on people’s mobility for smart decision-making: on local and regional, daily and seasonal, regular and irregular spatial behaviour. Countries that conduct national travel surveys collect data on regular travel patterns of local residents. This information is very powerful for addressing a number of goals, as also demonstrated by Antti Rehunen, SYKE, in the seminar. However, it tends to uncover travel that takes place occasionally, such as seasonal leisure travel. Remote touristic areas may face temporary population flows that reach the magnitude of a mid-sized city in spatial conditions that have not been optimised for serving such an amount of people smoothly and sustainably.
Several remote touristic areas such as Nordic ski resorts are facing the above-mentioned problem. Within MARA project, they find out ways how to gain more meaningful data on mobility needs as well as on current mobility patterns in their region. The project also looks into the question on how to better manage local temporary travel flows. This involves both transportation as well as service infrastructure covering the full mobility behaviour of tourists, for example accessibility to cultural and natural amenities, sport facilities, accommodation, dining facilities or stores.
Currently, most regions lack explicit information on domestic and foreign tourist flows and their detailed mobility within the destination region. Apart from official statistics from accommodation service providers, traveller counts in local airports or data on ticket sales from touristic hotspots, questionnaires have been a convenient approach to address the mobility or activity of tourists in a region (Heldt and Mortazavi 2016). Spatiotemporally more explicit method is arranging GPS-supported tourist tracking campaigns during their stay in the region (Shoval and Ahas 2016). As GPS campaigns may be costly and require large managerial effort, other geocoded data collection methods such as use of destination card (Zoltan and McKercher 2015) or public participatory GIS (Kantola et al. 2018; Salonen et al. 2018) are used in tourism studies. The latter method was also applied in the MARA project within the Kymeenlaakso regional survey of non-resident population (Vierikko et al. 2019). Subject to the survey design, the above-mentioned methods may reveal individual activity locations and times, mobility chains and travel modes, as well as semantic meaning and reasons behind individual mobility decisions. At the same time, a drawback with these methods is that they cover either rather small number of volunteering visitors or involve sample biases due to sample management and enrolment issues.
To cover larger population flows in the region, other digital mobility data sources would be handy. There is an increasing body of studies applying social media (Toivonen et al. 2019) or mobile phone data (Ahas et al. 2014) in tourism related research. Passive mobile phone data has proven to be a rich data source for analysing the spatiotemporal behaviour of large anonymous population groups. The University of Tartu has extensive experience in applying mobile phone data also in tourism studies (Ahas et al. 2007; Nilbe et al. 2014; Raun et al., 2016; Saluveer et al. forthcoming). In the seminar, Age Poom gave insights to mobile phone based research conducted in Estonia. The MARA project involves development of a Population Mobility Monitor that among other data sources applies mobile phone data to visualise regional population flows.
There are many regulatory and operational issues to be solved before passive mobile phone data can be used in research, for example to secure privacy protection of individual subscribers who serve as anonymous data providers. As mobile phone data becomes more and more accessible elsewhere, including Sweden (Östh et al. 2016) and Finland (Bergroth 2018), there are strong perspectives of using it also in the mobility management of remote touristic areas.
Disclosure: The blog post is adjusted based on the original post published on the MARA project website.
Ahas, R., Aasa, A., Mark, Ü., Pae, T., Kull, A. 2007. Seasonal tourism spaces in Estonia: Case study with mobile positioning data. Tourism Management, 28(3), 898–910. https://doi.org/10.1016/j.tourman.2006.05.010
Ahas, R., Armoogum, J., Esko, S., Ilves, M., Karus, E., Madre, J.-L., Nurmi, O., Potier, F., Schmücker, D., Sonntag, U., Tiru, M. 2014. Feasibility Study on the Use of Mobile Positioning Data for Tourism Statistics Report 3a. Feasibility of Use: Methodological Issues. https://ec.europa.eu/eurostat/documents/747990/6225717/MP-Consolidated-report.pdf
Bergroth, C. 2018. The 24-h population dynamics of the Finnish Capital Region uncovered! https://blogs.helsinki.fi/accessibility/2018/10/09/the-24-h-population-dynamics-of-the-finnish-capital-region-uncovered/
Heldt, T., Mortazavi, R. 2016. Estimating and comparing demand for a music event using stated choice and actual visitor behaviour data. Scandinavian Journal of Hospitality and Tourism, 16(2), 130–142. https://doi.org/10.1080/15022250.2015.1117986
Kantola, S., Uusitalo, M., Nivala, V., Tuulentie, S. 2018. Tourism resort users’ participation in planning: Testing the public participation geographic information system method in Levi, Finnish Lapland. Tourism Management Perspectives, 27, 22–32. https://doi.org/10.1016/j.tmp.2018.04.001
Nilbe, K., Ahas, R., Silm, S. 2014. Evaluating the Travel Distances of Events Visitors and Regular Visitors Using Mobile Positioning Data: The Case of Estonia. Journal of Urban Technology, 21(2), 91–107. https://doi.org/10.1080/10630732.2014.888218
Östh, J., Reggiani, A., Schintler, L. 2016. Resilience in Spatial and Urban Systems 2. Presentation at Advanced Brainstorm Carrefour (ABC): ‘Smart People in Smart Cities’ Matej Bel University, Banská Bystrica, Slovakia (August, 2016). https://www.slideshare.net/regionalscienceacademy/resilience-in-spatial-and-urban-systems-2
Raun, J., Ahas, R., Tiru, M. 2016. Measuring tourism destinations using mobile tracking data. Tourism Management, 57, 202–212. https://doi.org/10.1016/j.tourman.2016.06.006
Salonen, M., Broberg, A., Kyttä, M., Toivonen, T. 2014. Do suburban residents prefer the fastest or low-carbon travel modes? Combining public participation GIS and multimodal travel time analysis for daily mobility research. Applied Geography, 53, 438–448. https://doi.org/10.1016/j.apgeog.2014.06.028
Saluveer, E., Raun, J., Tiru, M., Altin, L., Kroon, J., Snitsarenko, T., Aasa, A., Silm, S. n.d. Methodological framework for producing national tourism statistics from mobile positioning data. Annals of Tourism Research.
Shoval, N., Ahas, R. 2016. The use of tracking technologies in tourism research: the first decade. Tourism Geographies, 18(5), 587–606. https://doi.org/10.1080/14616688.2016.1214977
Toivonen, T., Heikinheimo, V., Fink, C., Hausmann, A., Hiippala, T., Järv, O., Tenkanen, H., Di Minin, E. 2019. Social Media Data for Conservation Science: a Methodological Overview. Biological Conservation, 233(January), 1–18. https://doi.org/10.1016/j.biocon.2019.01.023
Zoltan, J., McKercher, B. 2015. Analysing intra-destination movements and activity participation of tourists through destination card consumption. Tourism Geographies, 17(1), 19–35. https://doi.org/10.1080/14616688.2014.927523
One of the main data processing steps before making use of novel data sources (e.g. Twitter data) for better understanding social processes and phenomena is the detection of users’ origins – be it at country, municipality or neighborhood level. This allows us to know whose Tweets in some geographical area (say, in a certain city or a neighborhood) we investigate. The most basic way is to distinguish locals from non-locals when examining mobility and activity locations of people. A more advanced analysis would require knowledge about origin countries in tourism studies and origin neighborhoods in segregation studies, for example.
This data processing step is also a prerequisite for cross-border mobility research – we need to know origins of people in order to categorize and analyze movements across country borders extracted from geotagged Tweets. Hence, it is the priority for our cross-border project. See, for example, the recent recent cross-border mobility analysis in the case of the Greater Region of Luxembourg from the MSc thesis by Samuli Massinen.
For analyzing cross-border mobility between Finland and Estonia, we first collected publicly available geotagged Tweets from Twitter Streaming API. We also got additional data from prof. Matthew Zook and Ate Poorthuis who collect Twitter data via the DOLLY project at Uni Kentucky. For extracting individual mobility trajectories and analyzing movements between Finland and Estonia, we used the Twitter Search API to collect Tweet histories (up to 3200 latest Tweets) for each Twitter user who had geotagged a Tweet in Finland and Estonia at least once. Finally, we collected digital traces of roughly 80,000 Twitter users (public accounts) between 2012 and 2019. Using Samuli’s algorithm to detect the country of origin, we found that some 34,000 Twitter users live in Finland.
Now we were excited to know where the Finnish Twitter users in our sample come from and does their spatial distribution at municipality level make sense. So, we slightly modified Samuli’s algorithm to detect their origin municipality in Finland and mapped the result. To evaluate our results, we applied simple regression model to estimate population derived from Twitter data and compared it with the official residential statistics at municipality level (Figure 1).
First, the positive side. The population estimation from Twitter at municipality level has very high correlation with the official residential registration statistics – the correlation coefficient is 0.98! This is a great outcome and gives the confidence to continue using the data extracted from Twitter for national scale analyses. In particular, given the applied simplistic modelling, there is a huge potential to further develop estimation models. For example, one could now evaluate domestic tourism between different regions or examine the network of central cities with their catchment areas based on people’s mobility derived from Tweets.
Second, the challenge. The exceptionally high concentration of Twitter users in the capital city of Helsinki compared to other municipalities is a challenge for the regression model – basically, it is an outlier in the model. According to our approach, 33% of all origins are located in Helsinki, although the proportion of Finnish population living in Helsinki is only 12% according to the residential register. We were able to minimize the over representation of Helsinki to some extent by using the best fit quadratic regression, instead of simple linear regression model (R2 = 0.83). In both models we also weighted Twitter data with age and gender according to Twitter users’ profile, but this increased the model performance marginally.
The city of Helsinki as an outlier can be explained by two issues, at least: 1) certain (other than age and gender related) social group using Twitter is more represented in Helsinki than in other municipalities; 2) information extraction from Twitter data (data enrichment) has not yet taken into account the detection of work locations – one of the main anchor points in our daily lives. Luckily, these issues can be tackled in future by advancing the data enrichment. For the former, one could enrich data to provide additional background attributes to profile Twitter users. One could also weight Twitter data with the proportion of higher education and/or university students in a municipality. For the latter issue, one could apply the framework of the anchor point model by Ahas et al. (2010) to reveal both the home and work (school) locations to better pinpoint residential locations. Currently, we believe that our simplistic model has assigned many Twitter users’ residential locations to Helsinki who actually are commuting from the Helsinki wider metropolitan area to Helsinki for work.
In conclusion, this comparison gives us confidence that we can detect users’ origins from social media data, and that we can use it as one background attribute in our work-in-progress cross-border mobility analysis between Finland and Estonia. We also continue with the origin detection algorithm development. Stay tuned!
Course teaching at the University of Helsinki is ending this week for the winter break. This autumn, researchers at the Digital Geography Lab have been working hard to share their knowledge and train the next generation. Innovative teaching approaches and teaching materials have also been developed further for the use of the University of Helsinki Geography programmes.
We congratulate and thank Vuokko Heikinheimo for leading and updating the hugely successful Automating GIS processes course together with Henrikki Tenkanen, Olle Järv for running and improving the Quantitative research techniques and analysis in human geography course, and Joel Jalkanen for putting lots of effort to re-inventing the Conservation Planning and Zonation course, together with Enrico Di Minin.
Olle : “Quantitative research in Human Geography is about telling geographical stories with quantitative data! To explore, explain and understand society at a range of geographical scales, and over time – societal processes and phenomena, differences and regularities between places and people. For this, it is important to handle quantitative research techniques and analysis, however, it is crucial to keep in mind that (human) geography is more about geography, space, place and people than about math, statistics and spatial analytics.”
Where are the most important biodiversity areas in the Uusimaa region? Zonation tells that!
Joel: “The Conservation Planning and Zonation course familiarizes students with current topics, theories, methods and data sources of spatial conservation planning. The students get an overview of recent research and practice of spatial conservation planning and get hands-on experience on analyzing spatial conservation planning problems with GIS-based Zonation software.”
100 enthusiastic geography and geology students started to learn Python this autumn!
Vuokko: “During the Automating GIS processes course, the students learn to analyze geospatial data efficiently and systematically using the Python programming language. The students also learn to use a version control system (git) and online repositories (GitHub) for documenting and communicating their analysis workflow. Course materials are openly available at geo-python.github.io and autogis.github.io“