A new paper about identifying large ecological networks for regional planning with Zonation

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.

Kuvan esikatselu

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.

Mobility research in areas with seasonal population changes

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

Can we use Twitter data to estimate population distribution in Finland?

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).



Figure 1. The comparison of Finnish population distribution by municipality between the official residential register by Statistics Finland and population estimation based on Twitter data using quadratic regression model.

What can we see from the comparison?

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!




Done with the teaching! Overview of autumn 2019 courses

Autumn term is coming to an end!

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.

Quantitative research in Human Geography

GEOG-H302 Quantitative research techniques and analysis in human geography

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.”

Conservation Planning and Zonation 

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.”

Automating GIS processes 

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

Congratulations Claudia, Hertta and Elias for the City of Helsinki Master’s thesis award!

Yearly award for the best Master’s thesis have been again presented by the city of Helsinki on Monday the 9th of December 2019. We at the Digital Geography Lab would like to give special congrats to three awarded GIS wizards from the Department of Geography and Geosciences, Univerisity of Helsinki: Claudia Bergroth, Hertta Sydänlammi and Elias Willberg!

Bergroth Claudia (2019): Uncovering population dynamics using mobile phone data: the case of Helsinki Metropolitan Area.

The estimated hourly distribution of people on an average weekday in the Finnish Capital Region based on network-driven cellular mobile phone data. Read more about Claudia’s work in this blog post. 

Sydänlammi Hertta (2019): Strategic districting for the mitigation of educational segregation: A pilot model for school district optimization in Helsinki.

Original and re-calculated school districts.  Read more about Hertta’s work from Helsingin Sanomat (in Finnish).

Willberg Elias (2019): Bike sharing as part of urban mobility in Helsinki: a user perspective.

Bike_sharing 15.5.2017

All the trips (n~7200) made by Helsinki bike-sharing system bikes on Monday 15.5.2017. Routes have been modeled. Read more about Elias’ work in this blog post.

You can find more information, and other recipients of the price  (in Finnish) in here:  https://www.hel.fi/uutiset/fi/kaupunginkanslia/helsinki-palkitsi-opinnaytetoita  Congratulations to all 9 recipients!

Mapping the linguistic landscape of the Helsinki metropolitan area

The discipline of linguistics has a long-standing interest in researching language use in cities, because they bring together speakers of different languages from different backgrounds. Examples range from William Labov’s classic empirical research on the relation of social class and pronunciation of American English in New York City to modern theories of multilingualism in cities by Alastair Pennycook and Emi Otsuji. Regardless of the scope or focus, the consensus is that interactions between different languages and their speakers drive linguistic variation and change, whose effect is particularly strong in densely populated cities.

Within sociolinguistics, a subfield of linguistics broadly concerned with language in society, one emerging approach to the study of languages in cities concerns linguistic landscapes. The study of linguistic landscapes mainly focuses on the visibility and presence of languages in the built environment, performing qualitative analyses of languages in signs, advertisements, billboards and other media in built environments.

However, the physical spaces in which languages exist are being rapidly transformed due to technological development. These spaces increasingly extend into the digital realm due to the widespread use of positioning technology in smartphones and other mobile devices, which allow users to create and associate content with physical locations via geotagging. Social media platforms with geotagged content are a hallmark example of this development, which also offer new opportunities for linguistic research.

In our new project, funded by a three-year project grant from the Emil Aaltonen Foundation, we will map the linguistic landscape of the Helsinki metropolitan area using register and social media data. Whereas the register data provides a static view into the linguistic landscape, social media data provides a dynamic view into how speakers of different languages move around the city and when. We tackle this combination of data using methods from geoinformatics and natural language processing, which is expected to provide a new, quantitative perspective on linguistic landscapes, and complements the previous qualitative approaches.

Our initial analyses of social media data show that the Helsinki metropolitan area is indeed multilingual.

The distribution of unique languages into a 250 metre grid in the Helsinki metropolitan a
The number of unique languages in the Helsinki metropolitan area, as observed in monolingual Instagram captions from 2014–2016, which have been aggregated into 250x250m cells. The languages were detected automatically using fastText. Some cells in downtown Helsinki feature up to 55 unique languages during this period. What is also worth noting is that the majority of the grids feature more than one language.
The diversity of the linguistic landscape as measured using Shannon entropy
The diversity of the linguistic landscape, as measured using Shannon entropy, based on observations of unique languages and their respective counts in Instagram captions from 2014–2016. The clusters, which have been detected using Moran’s I, reveal areas with high linguistic diversity in downtown Helsinki, the Aalto University campus and in multicultural suburbs of eastern Helsinki and Espoo. Clusters with low linguistic diversity, in turn, can be observed mainly in Espoo and Vantaa.

By the end of the project in 2022, we hope to have learned something new about urban multilingualism in the Helsinki metropolitan area, but also hope that the results can inform when planning city services. The current estimate is that by 2030 almost 25% of the population will speak a first language other than Finnish, but this does not mean that these speakers would not speak Finnish at all, as they are likely to speak Finnish as a second language.

For more information about the project, contact Tuomo Hiippala.

Geotagged Twitter Data to Reveal Cross-Border Mobility of People

Overview of Samuli Massinen’s MSc thesis:

How can Big Data sources, such as georeferenced social media, be used in cross-border research? What kind of cross-border mobility patterns can be detected geographically over time? How can daily cross-border movements be separated from other movements? These were the main questions I was trying to find answers for in my Master’s thesis “Modeling Cross-Border Mobility Using Geotagged Twitter in the Greater Region of Luxembourg”.

The Greater Region of Luxembourg is the largest cross-border labor market in the European Union with the greatest number of cross-border workers in the area. European integration, the Schengen Area, and socio-economical divergences between neighboring countries have been the main factors facilitating human cross-border movements in the region and thus the emergence and expansion of the borderland community. Despite the freedom of movement, country borders still exist as well as their socio-economic differences. We witness the growing trend of people migrating to the other side of the country border while still working in Luxembourg. This actuates daily cross-border mobilities, which are not well known, to date. Thus, there is a distinct need to understand cross-border mobility dynamics in the region, especially border crossings on a daily basis.

Thus far, cross-border mobility studies have mainly leaned on national registers, surveys, and census data. However, these datasets have mostly been too scarce in trying to understand the complexities of cross-border mobility in time and space. Many studies have only focused on aggregate-level movement patterns, and the viewpoint of individuals has been missing due to a lack of suitable data. One promising option to provide individual-level data to cross-border mobility research is the implementation of novel data sources, such as mobile phone positioning and social media data.

In my thesis, I employed a person-based approach using geotagged Twitter data to study cross-border mobility in the Greater Region of Luxembourg. The aim was to examine how to implement social media in cross-border mobility research and how to move beyond aggregate-level inspections. Being one of the first studies of its kind, I utilized a heuristic programmatic approach. To my knowledge, social media data sources have not been previously applied to distinguish different cross-border mobility types. Thus, I have published all scripts and algorithms developed in the study on Digital Geography Lab’s GitHub -pages.

Figure 1. Daily cross-border mover activity location density distribution in

The results show that social media can be implemented in cross-border mobility research, and social media data can provide a relatively good proxy for daily cross-border mobility of people on a regional level. Aggregate-level cross-border mobility patterns and activity location densities (Figure 1) correspond closely with previous studies, and outcomes from temporal frequency inspections indicate that it’s a promising approach for identifying and classifying border crossers  – Twitter users classified as daily cross-border movers are more mobile on working days whereas infrequent border crossers (potentially leisure and tourism-related) are mobile on weekends (Figure 2). Daily cross-border mobility patterns also provided new information about the spatial extent of the movements, indicating home-work commuting (Figure 3).

Figure 2. Weekday variation of cross-border trips to Luxembourg (both directions) by cross-border mover type within the Greater Region of Luxembourg in 2010–2018.

To obtain meaningful information from a Big Data source, several data processing steps are required, one crucial stage being the origin detection of social media users. I used a heuristic approach in home location detection which resulted in high accuracy – the “unique weeks” algorithm introduced in this study gave an accuracy of 88.6 % concerning the ground truth (i.e. Twitter user-given information about the individual’s home location).

Figure 3. Cross-border trips for identified daily cross-border movers in 2010–2018. Movements cover both ways between Luxembourg and neighboring areas. Note: each map has a different flow intensity scale.

Although the applied approach is promising in providing new knowledge about spatio-temporal patterns and trends, the results should be considered with slight caution. For example, this study did not consider population densities or Twitter use activity that might cause bias – both attributes vary spatio-temporally and effect where and when Twitter data is being recorded. Also, the sample size was rather limited in this study (~3200 Twitter users).

Hence, further research and method development are needed with larger sample sizes to draw more sound conclusions about cross-border mobility. Still, in my research, I was able to identify that the coverage of geotagged Twitter data is dependent on data acquisition processes and that Twitter Streaming API can provide valuable information for cross-border mobility research. In future studies, I recommend multi-level data acquisition processes to be applied jointly with a person-based approach combining both spatio-temporal and content analysis methodologies.

My research was part of the cross-border mobilities project at the Digital Geography Lab.


Text by: Samuli Massinen

My MSc thesis can be found here: E-Thesis

All my developed scripts and algorithms can be found here: DGL@GitHub

How to infer complex dynamics from present-day landscape patterns? New publication provides a method

Present-day landscape patterns may provide information on past dynamics of the landscape. Spatial ecologists have taken advantage of this for a long time, for instance to infer colonization rates and dispersal distances of species from their present-day spatial distributions and occurrence patterns.

Inferring past dynamics from present-day patterns gets more complicated if multiple landscape elements have been simultaneously on the move. In this case, it may be helpful to reconstruct and simulate past landscape dynamics, to understand how different dynamic elements must have interacted in the past to produce the present-day pattern. Methods that reconstruct past interactions may help us to understand complex dynamics, without having to wait for years for the accumulation of time series data.

In our recent publication in the field of spatial ecology, we tested this by using data on a well-studied epiphytic lichen, Lobaria pulmonaria. For our study area, fire scar data existed on the timings and locations of forest fires for a 400-year time period. Given this known landscape disturbance history, we simulated and calibrated the dynamics of L. pulmonaria host trees and L. pulmonaria colonizations so that they resulted in patterns that match with present-day data (locations of L. pulmonaria occurrences and host trees). Our resulting colonization model of L. pulmonaria performed well against a model fitted to time series data.

We hope to inspire further studies on complex dynamics that utilize multiple types of information contained in present-day landscapes.

Fabritius et al. (2019): Estimation of metapopulation colonization rates from disturbance history and occurrence pattern data. Ecology 100: e2814.

How green are the streets of Helsinki?

Overview of Akseli Toikka’s MSc thesis: Mapping the green view of Helsinki through Google street view images

Interactive webmap for the Green View Index across Helsinki

Click to browse the interactive GVI map of Helsinki.

Urban vegetation has traditionally been mapped through traditional ways of remote sensing like laser scanning and aerial photography. However, it has been stated that the bird view examination of vegetation cannot fully represent the amount of green vegetation that the citizens observe on street level. Recent studies have raised human perspective methods like street view images and measuring of green view next to more traditional ways of mapping vegetation. Green view index (GVI) states the percentage of green vegetation in street view on certain location. The purpose of my thesis was to create a green view dataset of Helsinki city using Google street view (GSV) imagery and to reveal the differences between human perspective and aerial perspective in vegetation mapping.

Toikka (2019): Downloading Google street view panoramas.

Figure 1. Summertime Google street view panoramas of Helsinki were downloaded in six horizontal images. The GVI value of a panorama is the average of these 6 images.

Street view imagery of Helsinki was downloaded from GSV application programming interface. The spatial extent of the data was limited by the availability of street view images of summer months. Every GSV panorama was downloaded in six images (Figure 1). The amount of vegetation in the images was calculated based on the spectral characteristics of green vegetation (Figure 2). The GVI value of each panorama image is an average of all the six images constructing the panorama.

Figure 2. From left to right: original, classified and overlay image. GVI was calculated based on the spectral characteristics of green surfaces. The GVI value of this street view is 43.97%.

Several green view maps of Helsinki were created based on the calculated GVI values (Figure 3). In order to understand the differences between human perspective and the aerial view, the GVI values were compared with the regional land cover dataset of Helsinki using linear regression. Areas with big differences between the datasets were examined visually through the street view imagery. Helsinki green view was also compared internationally with other cities with same kind of data available at the Treepedia website of Seanseable City Lab, MIT.

Figure 3. GVI values aggregated to YKR statistical grid. The downtown and industrial areas are easily recognized form the rest of the city with their lower GVI values.

It appealed that the green view of Helsinki is divided unequally across the city area. The lowest green view values can be found in downtown, industrial areas and the business centers of the suburbs. Highest values were located at the housing suburbs. Especially the older areas of lower housing like Kuusisaari, Lehtisaari and Laajasalo stand out with relatively high GVI values. Younger housing areas like Arabianranta, Latokartano and Herttoniemenranta have relatively low GVI values because of their yet undeveloped greenery.

When compared with the land cover data, it was found that the green view has a weak correlation with low vegetation and relatively high correlation with taller vegetation such as trees. Differences between the datasets were mainly concentrated on areas where the vegetation was not visible from the street by several reasons.  Main sources of errors were the oldest street view images and the flaws in image classification caused by other green objects and shadows.

Even though Helsinki has many parks and other green spaces, the greenery visible to the streets isn’t always that high. The green view dataset created in this study helps to understand the spatial distribution of street greenery and brings human perspective next to more traditional ways of mapping city vegetation. When combined with previous city greenery datasets, the green view dataset can help to build up more holistic understanding of the city greenery in Helsinki.

My thesis was a produced as a cooperation between the department of Geoinformatics and Cartography at the Finnish Geospatial research Institute and the Digital Geography Lab from the University of Helsinki. In the computing we made use of geospatial computing resources provided by CSC and the Open Geospatial Information Infrastructure for Research (oGIIR, urn:nbn:fi:research-infras-2016072513) funded by the Academy of Finland.

Text by: Akseli Toikka

Akseli’s thesis (only in Finnish) can be found in here.
The data processing scripts are available at Geoportti GitHub.

Environmental dialogues: how to plan for urban biodiversity?

Earlier this week, Henna Fabritius took part in the environmental dialogues -event  to discuss biodiversity-related modelling tools in urban greenery planning.

More information, and a recording of the event is available here (in Finnish):

Ympäristödialogeja: Miten suunnitella monimuotoista luontoa?

The event was organized by the Forum for Environmental Information (Ympäristötiedon foorumi in Finnish), which is a non-profit organization that aims at increasing interaction between the producers and users of environmental information in order to support national policy making in Finland, while keeping in mind the global significance of environmental problems.

Read also Henna’s blog about getting better at supporting urban biodiversity (in Finnish):

Minä väitän: Luonnon monimuotoisuutta voitaisiin tukea kaupungeissa nykyistä enemmän