Overview: Spatial Access to Financial Services
Distribution of distance to ATM
Share of households by travel distance to nearest ATM (in m)
Distribution of distance to bank branch
Share of households by travel distance to nearest bank branch (in m)
Distance to nearest ATM & municipality type
Share of households by travel distance to nearest ATM (in m)
Distance to nearest bank branch & municipality type
Share of households by travel distance to nearest bank branch (in m)
Municipality Overview: Spatial Access to Financial Services
Average travel distance on municipality level (in m)
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Zoom-In: Municipality Analysis
Average travel distance on hectare level (in m)
Swiss Money Map
The project “Swiss Money Map” has the aim to provide an analytical tool to monitor cash access points.
This is particularly relevant in the era of decreasing transactional cash usage and increasing costs
of cash withdrawals, respectively. We therefore hope to contribute to a comprehensive understanding
of optimal cash circulation.
The project is a collaboration between researchers at the University of St.Gallen and is led by Prof. Dr. Martin Brown and Dr. Tobias Trütsch.
The project was inspired by similar initiatives in other countries such as:
Austria: OeNB A spatial analysis of access to ATMs in Austria
UK: Access to cash coverage in the UK
Australia: How far do Australians need to travel to access cash?
The project is a collaboration between researchers at the University of St.Gallen and is led by Prof. Dr. Martin Brown and Dr. Tobias Trütsch.
The project was inspired by similar initiatives in other countries such as:
Austria: OeNB A spatial analysis of access to ATMs in Austria
UK: Access to cash coverage in the UK
Australia: How far do Australians need to travel to access cash?
Data, Definitions & Calculations
Data Sources
Households
The number of households in a hectare is based on the
BFS dataset STATPOP
(2020). The dataset provides information on the number of households residing within a specific hectare.
The sum of
the hectare values can slightly deviate from the total number of households in a municipality
as a) not all households of a municipality can always be clearly assigned to a hectare what
results in “residual households” and b) for data privacy reasons, the minimum displayed value
for the number of households in hectare is 3. These statistical properties of the data should not
have a strong impact on the overall results.
ATMs
The list of ATMs contains all ATMs listed by SIX, Travelex and Postfinance.
Time of update: 01.09.2021
Time of update: 01.09.2021
Bank Branches
Bank branches contain all banks from Google Maps, local.ch and OpenStreetMap.
We further pull branches directly from the financial institutions’ websites in case of Credit Suisse,
Postfinance, Raiffeisen and UBS. We overlay the different data sources, drop entries that are not regular
banks and investigate cases that do only show up in a single source. The analysis focuses on retail banks.
We therefore limit the bank branches to the following groups of banks: Cantonal
banks, big banks, Raiffeisen banks, regional and savings banks, selected banks
of the category “other banking institutions” that serve retail clients. Please
download the dataset below for the full overview.
Time of update: 01.09.2021
Time of update: 01.09.2021
Calculations of Distances
The distances are calculated from the center of the hectare to the closest ATM or bank branch.
For hectares within the radius of 500m around the ATM we display the Euclidean distance assuming that
people walk to the ATM. For hectares outside of the 500m radius, we use the TomTom travel API to calculate
the car travel distance to the closest ATM or bank branch. For travel distances that are at least
twice as long as the Euclidean distance, we obtain the travel distance to the second closest ATM or
bank branch. Afterwards, we run several smoothing algorithms to detect outliers that were not correctly
processed by TomTom’s routing API. Overall, the methodology allows for a very detailed analysis of the
results. We would like to point out that there are still specific cases where the methodology is not
fully able to capture the real circumstances and behavior of households.
Download Dataset
Contact
Dr. Tobias Trütsch:
tobias.truetsch@unisg.ch
Prof. Dr. Martin Brown: martin.brown@unisg.ch
Data preparation & Dashboard: Novalytica AG
Prof. Dr. Martin Brown: martin.brown@unisg.ch
Data preparation & Dashboard: Novalytica AG