Mobile and Location Based Services

In a week-long seminar led by Prof. Anto Aasa, I delved deep into the fascinating world of "Mobile & Location Based Services". The seminar provided an extensive lecture on mobile and location-based services. My key task post-seminar was to craft an essay on “Privacy concerns in Germany (Europe) and the development of LBS”.

For the practical components, I undertook two projects, for which I implemented intaractive visualizations in python:

Analysis of Foreign Visitors in Estonia: Leveraging passive mobile positioning, I designed an interactive map with Plotly Scattermapbox, which provides insight into foreign visitors' movements and calling patterns within Estonia. The intriguing twist in this project was representing countries by wild animal names for privacy reasons.
Tourist Trip Analysis Based on GPS data: Using data from the "MobilityLog" Android app, I analyzed the movement patterns of a tourist in California. The resultant map showed their route, pinpointing overnight stays, meal stops, and more. The processing journey was not without challenges, given inconsistencies in the dataset, but overcoming them made the results even more rewarding.

All of my analytical work and the resultant visualizations are presented on this page. Feel free to explore the interactive maps and deep dive into the patterns, densities, and unique user experiences from both projects.

Link to my code on github: https://github.com/Leonieen/MLBS

Python

Keywords:

Data Analyticss

Plotly

Data visualization

Estonia MLBS

Estonia_UniqueUser

00:53

Estonia_Time

01:06

Estonia_MapFilters

00:24

Municipalities with the most activities and those with the most unique users

The table lists the 5 municipalities with the most activities and those with the most unique users.

Map Filters

Interacative Map with Filters: The map was created with a Plotly Scattermapbox and gets updated by a Dash callback. Via the filters certain spatial patterns can be explored. It seems that the least visitors are from Wolf and the month with most visits is July. Most Visitors are calling from Tallinn or Tartu. The density in the large cities is also particularly visible in the animation, also created with a Scattermapbox, as well as an increased density on the border with Russia.

Animated Map

Interacative Map with Filters: The map was created with a Plotly Scattermapbox and gets updated by a Dash callback. Via the filters certain spatial patterns can be explored. It seems that the least visitors are from Wolf and the month with most visits is July. Most Visitors are calling from Tallinn or Tartu. The density in the large cities is also particularly visible in the animation, also created with a Scattermapbox, as well as an increased density on the border with Russia.

Calling Activities

The figure shows the calling density of the municipalities. A drop-down menu can also be used to distinguish between the individual tourist countries in order to determine any differences.
Overall, most calling activities take place on the northern Baltic Sea coast. A similar picture emerges from the analysis of the foxes. In the case of bears, there is also increased activity on the border with Russia and in the case of wolves in the municipality with the number 392 on Hiiumaa.

Hexbin map calling activities

Since the previously created heat map was not entirely satisfactory, a Hexbin map was also implemented to visualise a better gradation. For this purpose, the range of activities based on quantiles was lowered in order to still illustrate the high density in Tallinn, but also to show a gradual gradation of the other regions. This was additionally animated to include temporal changes, such as increased activity in July.

Hexbin map calling activities animated

Since the previously created heat map was not entirely satisfactory, a Hexbin map was also implemented to visualise a better gradation. For this purpose, the range of activities based on quantiles was lowered in order to still illustrate the high density in Tallinn, but also to show a gradual gradation of the other regions. This was additionally animated to include temporal changes, such as increased activity in July.

Time analysis

To get an even better impression of the calling activities of the individual countries in the given time frame, they were listed individually in bar charts. Particularly noticeable here are Wednesdays in the wolves, where there seem to have been a particularly large number of calls. Beforehand, all time data were split into the corresponding classifications in Python. A particularly large number of callers were present and made calls in July and August. The temporal patterns of the wolves and their frequency, however, do not quite coincide with the spatial patterns.

Time analysis

To get an even better impression of the calling activities of the individual countries in the given time frame, they were listed individually in bar charts. Particularly noticeable here are Wednesdays in the wolves, where there seem to have been a particularly large number of calls. Beforehand, all time data were split into the corresponding classifications in Python. A particularly large number of callers were present and made calls in July and August. The temporal patterns of the wolves and their frequency, however, do not quite coincide with the spatial patterns.

Time analysis

To get an even better impression of the calling activities of the individual countries in the given time frame, they were listed individually in bar charts. Particularly noticeable here are Wednesdays in the wolves, where there seem to have been a particularly large number of calls. Beforehand, all time data were split into the corresponding classifications in Python. A particularly large number of callers were present and made calls in July and August. The temporal patterns of the wolves and their frequency, however, do not quite coincide with the spatial patterns.

Time analysis

To get an even better impression of the calling activities of the individual countries in the given time frame, they were listed individually in bar charts. Particularly noticeable here are Wednesdays in the wolves, where there seem to have been a particularly large number of calls. Beforehand, all time data were split into the corresponding classifications in Python. A particularly large number of callers were present and made calls in July and August. The temporal patterns of the wolves and their frequency, however, do not quite coincide with the spatial patterns.

Time analysis

To get an even better impression of the calling activities of the individual countries in the given time frame, they were listed individually in bar charts. Particularly noticeable here are Wednesdays in the wolves, where there seem to have been a particularly large number of calls. Beforehand, all time data were split into the corresponding classifications in Python. A particularly large number of callers were present and made calls in July and August. The temporal patterns of the wolves and their frequency, however, do not quite coincide with the spatial patterns.

Time analysis

To get an even better impression of the calling activities of the individual countries in the given time frame, they were listed individually in bar charts. Particularly noticeable here are Wednesdays in the wolves, where there seem to have been a particularly large number of calls. Beforehand, all time data were split into the corresponding classifications in Python. A particularly large number of callers were present and made calls in July and August. The temporal patterns of the wolves and their frequency, however, do not quite coincide with the spatial patterns.

Calling Activities

The figure shows the calling density of the municipalities. A drop-down menu can also be used to distinguish between the individual tourist countries in order to determine any differences.
Overall, most calling activities take place on the northern Baltic Sea coast. A similar picture emerges from the analysis of the foxes. In the case of bears, there is also increased activity on the border with Russia and in the case of wolves in the municipality with the number 392 on Hiiumaa.

Calling Activities

The figure shows the calling density of the municipalities. A drop-down menu can also be used to distinguish between the individual tourist countries in order to determine any differences.
Overall, most calling activities take place on the northern Baltic Sea coast. A similar picture emerges from the analysis of the foxes. In the case of bears, there is also increased activity on the border with Russia and in the case of wolves in the municipality with the number 392 on Hiiumaa.

Map with the atennas

Interacative Map with Filters: The map was created with a Plotly Scattermapbox and gets updated by a Dash callback. Via the filters certain spatial patterns can be explored. It seems that the least visitors are from Wolf and the month with most visits is July. Most Visitors are calling from Tallinn or Tartu. The density in the large cities is also particularly visible in the animation, also created with a Scattermapbox, as well as an increased density on the border with Russia.

Calling Activities

The figure shows the calling density of the municipalities. A drop-down menu can also be used to distinguish between the individual tourist countries in order to determine any differences.
Overall, most calling activities take place on the northern Baltic Sea coast. A similar picture emerges from the analysis of the foxes. In the case of bears, there is also increased activity on the border with Russia and in the case of wolves in the municipality with the number 392 on Hiiumaa.

GPS MLBS

GPS_ThematicMap

01:09

GPS_Days_Route

00:52

GPS_Mileage

00:07

3D visualization of the route

To get a feeling for the spatial nature of the route, I plotted the GPS points in a 3D scatterplot and added the floor plan of California for spatial location. According to my internet research, California is on average 884m above sea level, which is why I also placed the data from the geojson there in the scatterplot.

I also tried to identify temporal patterns for this data set. This turned out to be rather not so purposeful. In the data set of the previous project, there were only points and time data, if an activity was also committed, here continuous data.
These visualizations were nevertheless included to see whether there are certain temporal trends or whether, for example, lunch breaks or overnight stays also become visible.
However, this visualization method is not without problems for the analysis of this question. For example, all data in the dataset were previously thinned where there were duplicate time entries, these could significantly affect the results.
Similarly, the frequency of GPS data is probably not the best measure, but rather the distances traveled between time points.

Daily Routes

Unfortunately the dataset showed some difficulties for the processing in Python. First, there were incorrectly displayed values in the timestamps, which is why the dataset could not be read in at first. After correcting this data, the commas for example in the coordinates were replaced by dots, and the separators of the dataset, originally tabs, were replaced by semicolons for better readability in Python. Here, however, difficulties arose with the time columns. Here the dates were displayed in different formats within one column. Some of the dates were in the format year-month-day or all may values were in the format year-day-month, which had to be corrected and adjusted with appropriate queries and loops in Python. However, this always led to problematic classifications. In the map shown below, the individual days can be selected and the corresponding route section is displayed, but here there can be inconsistencies due to the date problems already mentioned.

Thematic Map

Thematic Map: In the interactive map, the route was first displayed and, due to the large amount of data, all time points that occur more than once were removed to improve performance. It was noticeable that my route looks slightly different from the examples provided. Based on the time points and location properties, overnight stops, restaurants and shopping stops could be identified. In addition, markers were added to special sights and places.