Overview: Spatial Access to Financial Services

Distribution of distance to ATM

Share of households by travel distance to nearest ATM (in m)

Distance to nearest ATM & municipality type

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 bank branch & municipality type

Share of households by travel distance to nearest bank branch (in m)

Distribution of distance to post branch

Share of households by travel distance to nearest post branch (in m)

Distance to nearest post branch & municipality type

Share of households by travel distance to nearest post branch (in m)

Travel Time by Public Transport in 2024

Distribution of travel time to ATM

Share of households by travel time to nearest ATM (in min)

Travel time to nearest ATM & municipality type

Share of households by travel time to nearest ATM (in min)

Distribution of travel time to bank branch

Share of households by travel time to nearest bank branch (in min)

Travel time to nearest bank branch & municipality type

Share of households by travel time to nearest bank branch (in min)

Distribution of travel time to post branch

Share of households by travel time to nearest Post (in min)

Travel time to nearest post branch & municipality type

Share of households by travel time to nearest Post (in min)

Travel Time by Car in 2024

Distribution of travel time to ATM

Share of households by travel time to nearest ATM (in min)

Travel time to nearest ATM & municipality type

Share of households by travel time to nearest ATM (in min)

Distribution of travel time to bank branch

Share of households by travel time to nearest bank branch (in min)

Travel time to nearest bank branch & municipality type

Share of households by travel time to nearest bank branch (in min)

Distribution of travel time to post branch

Share of households by travel time to nearest Post (in min)

Travel time to nearest post branch & municipality type

Share of households by travel time to nearest Post (in min)

Difference Over Time: Change in distance from 2021 to 2024

Comparison of distribution of distance to ATM

Share of households by travel distance to nearest ATM (in m)

Difference over time ATM: 2021 - 2024

Share of households by travel distance to nearest ATM (in m)

Comparison of distribution of distance to bank branch

Share of households by travel distance to nearest Bank (in m)

Difference over time bank branch: 2021 - 2024

Share of households by travel distance to nearest Bank (in m)

Municipality Overview: Spatial Access to Financial Services

Average travel distance on municipality 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 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 . At the time of update, the most recent version of STATPOP is used. 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: 22.11.2024
Version 2023: 22.08.2023
Version 2021: 01.09.2021

Bank Branches

Bank branches contain all banks from Google Maps. 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. The first version of the dashboard (2021) also used local.ch and OpenStreetMap as a source. To avoid digital leftovers of closed branches we use Google Maps as the only maps source as it is frequently updated by banks.
Time of update: 22.11.2024
Version 2023: 01.09.2023
Version 2021: 01.09.2021

Post Branches

Post branches contains all post subsidiaries listed in the opendata.swiss portal.
Time of update: 08.02.2024

Calculations of Distances/Travel Times

The distances are calculated from the center of the hectare to the closest ATM, bank branch or post 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, bank branch or post 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, bank branch or post 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. We assume that the impact of these specific cases on the overall analysis is minor. For the calculation of all travel times using public transport (first time for 2023), we used the Google API at 8 am on a working day. Fixing the departure time enables the comparison of travel time to cash access points whilst avoiding potential 'time-of-day effects'. The travel time includes the time walking to the station as shown for any request on Google Maps.

Download Dataset

Contact

Dr. Tobias Trütsch: tobias.truetsch@unisg.ch

Data preparation & Dashboard: Novalytica AG