Anto Aasa

Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn

This paper proposes a methodology for using mobile telephone-based sensor data for detecting spatial and temporal differences in everyday activities in cities. Mobile telephone-based sensor data has great applicability in developing urban monitoring tools and smart city solutions. The paper outlines methods for delineating indicator points of temporal events referenced as ‘midnight’, ‘morning start’, ‘midday’, and ‘duration of day’, which represent the mobile telephone usage of residents (what we call social time) rather than solar or standard time. Density maps by time quartiles were also utilized to test the versatility of this methodology and to analyze the spatial differences in cities. The methodology was tested with data from cities of Harbin (China), Paris (France), and Tallinn (Estonia). Results show that the developed methods have potential for measuring the distribution of temporal activities in cities and monitoring urban changes with georeferenced mobile phone data.

Mobile Positioning in Space–Time Behaviour Studies: Social Positioning Method Experiments in Estonia

The paper introduces methods and applications of the mobile positioning-based social positioning method in geography. The social positioning method (SPM) studies space–time behavior by analyzing the location coordinates of mobile phones and the social characteristics of the people carrying them. We describe the experience gained from the SPM pilot studies carried out in Estonia from 2003 to 2006. The results demonstrate that mobile positioning-based tracing is applicable in different geographical studies, as an analysis of temporal movement patterns and activity spaces. The biggest advantage of mobile positioning-based methods is that mobile phones are widespread, positioning works inside buildings, and collection of movement data is done by a third party at regular intervals. The disadvantage of mobile positioning today is relatively low spatial accuracy and surveillance fears. The boom in the generation of phones with A-GPS will improve positioning accuracy in networks.

Mobile Positioning Data in Tourism Studies and Monitoring: Case Study in Tartu, Estonia

We introduce mobile positioning based data sources in tourism studies using the case study of tourism in Tartu, Estonia. Mobile positioning data is a promising source for tourism geography as it is one easiest and most cost effective sources for investigating the flows of tourists with relatively good spatial and temporal coverage. Mobile positioning data allows one to link the digital track of visitors with visited events and locations retrospectively. The data also has potential for the development of real-time monitoring tools for tourism planning and management, as it has been tested in Estonia with “Positium Tourism Barometer”. The biggest problem with positioning data is privacy and surveillance, and those issues needs to be addressed and discussed very carefully.

Application of mobile phone location data in mapping of commuting patterns and functional regionalization: a pilot study of Estonia

The paper presents initial steps into the research of commuting patterns and functional regions using mobile phone location data. The main aim is to introduce and discuss the potential of mobile phone location data as an alternative data sources to censuses for mapping commuting flows and subsequent functional regionalization. A set of analytical maps covering various aspects of regular daily movements of population and functional regionalization is provided. Estonia is serving as a pilot laboratory for analyses based on commuting flows derived from mobile phone location data. The maps give to reader a synthetic overview of contemporary settlement system in Estonia and introduce the potential of mobile phone location data for research in this field.

The Spatial Accuracy of Mobile Positioning: Some experiences with Geographical Studies in Estonia

This paper describes some aspects of mobile positioning accuracy when using mobile positioning data in geographical studies. The method used in Estonia is named the Social Positioning Method (SPM) and uses locations of mobile phones and the personal characteristics of phone owners for studying human behaviour. Positioning experiments conducted in Tallinn and Tartu since 2003 have shown that SPM data facilitates the successful analysis of the space-time behaviour of society. The calculations of theoretical positioning error based on 180 000 positioning measurements in the Estonian GSM network (CGI+TA positioning method) in 2004 showed that 61 percent of positioning points are accurate to within 1000 meters in urban areas and 53 percent are accurate to within 3000 meters in rural areas. Accuracy checks conducted using GPS showed that 52 percent of positioning points are accurate to within 400 meters in urban areas and 50 percent are accurate to within 2600 meters in rural areas. While some of the research findings are limited because of the low accuracy of the positioning data, many research directions, which use a smaller scale such as commuting and regional development studies are nevertheless very promising.

Seasonal Indicators and Seasons of Estonian Landscapes

Seasonality and seasons of Estonian landscapes are analysed using selected natural and social indicators of urban and rural landscapes. Seasonality has a great influence on the ecological and visual features of landscapes; seasonal variability is especially great in temperate climate zones where relatively cold winters alternate with warm summers. The indicators that are suitable for describing the seasonality of landscapes are natural parameters such as air temperature, radiation regime, climatic seasons and snow cover, and social parameters of birthdays, alcohol consumption and state budget allocations. Because of the great seasonal differences in natural and socio-economic conditions, the differences between urban and rural landscapes having different seasonal rhythms are focused upon. One of the main differences is the change in lifestyle which is detected in the change in the seasonal variability of births. Seasonal differences between urban and rural landscapes are also confirmed by parameters of changing climate and some social indicators. The developing information society creates new jobs and a lifestyle that has its own seasonal rhythm. Periods of active work and social activity accumulate towards the deadlines preceding the Christmas and the summer period of vacations. A project-oriented information society has more flexibility to enjoy nature in rural landscapes during different seasons.

Climate-related change in terrestrial and freshwater ecosystems

Climate-related change in terrestrial and freshwater ecosystems. in: BACC Author Group, Assessment of Climate Change for the Baltic Sea Basin

Developing Comparative Phenological Calendars

Phenological calendars describe the beginning, duration, and mutual relationships among seasonal natural phenomena. They can be used to help describe seasonality of environmental conditions, ecosystems, and individual species. Calendars consist of various types of seasonal data, such as phenological phases of living organisms, seasonal phases of environmental conditions, and instrumentally measured climatic parameters. Phenological calendars are an integrative method for studying those seasonal phenomena and representing seasonality graphically. Series of phenological phases show variations in climate and natural processes in an integrated way, and therefore are a useful approach to climate change studies. At the same time, impacts on organisms and ecosystems are an important outcome of changes in climate (Penuelas and Filella 2001). Therefore, connecting phenology with long time-series has become crucial for studying climate change, and phenological calendars can help examine its variations in space and time.

Global Boundary Lines of N2O and CH4 Emission in Peatlands

Predicting N2O (nitrous oxide) and CH4 (methane) emissions from peatlands is challenging because of the complex coaction of biogeochemical factors. This study uses data from a global soil and gas sampling campaign. The objective is to analyse N2O and CH4 emissions in terms of peat physical and chemical conditions. Our study areas were evenly distributed across the A, C and D climates of the Koppen classification. Gas measurements using static chambers, groundwater analysis and gas and peat sampling for further laboratory analysis have been conducted in 13 regions evenly distributed across the globe. In each study area at least two study sites were established. Each site featured at least three sampling plots, three replicate chambers and corresponding soil pits and one observation well per plot. Gas emissions were measured during 2–3 days in at least three sessions. A log-log linear function limits N2O emissions in relation to soil TIN (total inorganic nitrogen). The boundary line of N2O in terms of soil temperature is semilog linear. The closest representation of the relationship between N2O and soil moisture is a local regression curve with its optimum at 60–70 %. Semilog linear upper boundaries describe the effects of soil moisture and soil temperature to CH4 best.

Nitrogen-rich organic soils under warm well-drained conditions are global nitrous oxide emission hotspots

Nitrous oxide (N2O) is a powerful greenhouse gas and the main driver of stratospheric ozone depletion. Since soils are the largest source of N2O, predicting soil response to changes in climate or land use is central to understanding and managing N2O. Here we find that N2O flux can be predicted by models incorporating soil nitrate concentration (NO3−), water content and temperature using a global field survey of N2O emissions and potential driving factors across a wide range of organic soils. N2O emissions increase with NO3− and follow a bell-shaped distribution with water content. Combining the two functions explains 72% of N2O emission from all organic soils. Above 5 mg NO3−-N kg−1, either draining wet soils or irrigating well-drained soils increases N2O emission by orders of magnitude. As soil temperature together with NO3− explains 69% of N2O emission, tropical wetlands should be a priority for N2O management.In a global field survey across a wide range of organic soils, the authors find that N2O flux can be predicted by models incorporating soil nitrate concentration (NO3–), water content and temperature. N2O emission increases with NO3– and temperature and follows a bell-shaped distribution with water content.