Utilization of movement data from mobile sports tracking applications is affected by its inherent biases and sensitivity, which need to be understood when developing value-added services for, e.g., application users and city planners. We have developed a method for generating a privacy-preserving heat map with user diversity (ppDIV), in which the density of trajectories, as well as the diversity of users, is taken into account, thus preventing the bias effects caused by participation inequality. The method is applied to public cycling workouts and compared with privacy-preserving kernel density estimation (ppKDE) focusing only on the density of the recorded trajectories and privacy-preserving user count calculation (ppUCC), which is similar to the quadrat-count of individual application users. An awareness of privacy was introduced to all methods as a data pre-processing step following the principle of k-Anonymity. Calibration results for our heat maps using bicycle counting data gathered by the city of Helsinki are good (R2>0.7) and raise high expectations for utilizing heat maps in a city planning context. This is further supported by the diurnal distribution of the workouts indicating that, in addition to sports-oriented cyclists, many utilitarian cyclists are tracking their commutes. However, sports tracking data can only enrich official in-situ counts with its high spatio-temporal resolution and coverage, not replace them

The aim of our study was to characterize statistical and spatial details of the errors in a fine toposcale DEM derived by contour data. The fine toposcale DEMs are typically represented in a 5–50 m grid and used in the application scale 1:10 000–1:50 000. The errors were determined by using high‐quality reference data covering the entire study area from an airborne laser scanner. The work was motivated because of the essential role played by the correct characterization of DEM error in error‐propagation studies. The results showed that the spatial autocorrelation of the fine toposcale DEM error was the result of a complex combination of random and systematic‐like components, and its appropriate modelling by geostatistical methods is problematic because of the small extent of the areas in which the assumption of stationarity is valid. In addition, describing the shape of the DEM error distribution was impossible with a single parameter of dispersion. This was due to a large number of outliers, which suggests that more robust descriptors of the error should be used in addition to conventional error statistics.

GIS analysis‐based drainage basin delineation has become an attractive alternative to traditional manual delineation methods since the availability and accuracy of Digital Elevation Models (DEMs) and topographic databases has been improved. To investigate the uncertainty in the automatic delineation process, the present study represents a process‐convolution‐based Monte Carlo simulation tool that offers a powerful framework for investigating DEM error propagation with thousands of GIS‐analysis repetitions. Monte Carlo‐based probable drainage basin delineations and manual delineations performed by five experts in hydrology or physical geography were also compared. The results showed that automatic drainage basin delineation is very sensitive to DEM uncertainty. The model of this uncertainty can be used to find out the lower bound for the size of drainage basins that can be delineated with sufficient accuracy.

Recent studies have incorporated human perspective methods like making use of street view images and measuring green view in addition to more traditional ways of mapping city greenery [1]. Green view describes the relative amount of green vegetation visible at street level and is often measured with the green view index (GVI), which describes the percentage of green vegetation in a street view image or images of a certain location [2]. The green view dataset of Helsinki was created as part of the master's thesis of Akseli Toikka at the University of Helsinki [3]. We calculated the GVI values for a set of locations on the streets of Helsinki using Google Street View (GSV) 360° panorama images from summer months (May through September) between 2009 and 2017. From the available images, a total of 94 454 matched the selection criteria. These were downloaded using the Google application programming interface (API). We calculated the GVI values from the panoramas based on the spectral characteristics of green vegetation in RGB images. The result was a set of points along the street network with GVI values. By combining the point data with the street network data of the area, we generated a dataset for GVI values along the street centre lines. Streets with GVI points within a threshold distance of 30 meters were given the average of the GVI values of the points. For the streets with no points in the vicinity (∼67%), the land cover data from the area was used to estimate the GVI, as suggested in the thesis [3]. The point and street-wise data are stored in georeferenced tables that can be utilized for further analyses with geographical information systems.

This article investigates how workout trajectories from a mobile sports tracking application can be used to provide automatic route suggestions for bicyclists. We apply a Hidden Markov Model (HMM)‐based method for matching cycling tracks to a “bicycle network” extracted from crowdsourced OpenStreetMap (OSM) data, and evaluate its effective differences in terms of optimal routing compared with a simple geometric point‐to‐curve method. OSM has quickly established itself as a popular resource for bicycle routing; however, its high‐level of detail presents challenges for its applicability to popularity‐based routing. We propose a solution where bikeways are prioritized in map‐matching, achieving good performance; the HMM‐based method matched correctly on average 94% of the route length. In addition, we show that the extremely biased nature of the trajectory dataset, which is typical of volunteered user‐generated data, can be of high importance in terms of popularity‐based routing. Most computed routes diverged depending on whether the number of users or number of tracks was used as an indicator of popularity, which may imply varying preferences among different types of cyclists. Revising the number of tracks by diversity of users to surmount local biases in the data had a more limited effect on routing.