Swiss Dwellings: A large dataset of apartment models including aggregated geolocation-based simulation results covering viewshed, natural light, traffic noise, centrality and geometric analysis
Description
Introduction
This dataset contains detailed data on over 45,000 apartments (370,000 rooms) in ~3,100 buildings including their geometries, room typology as well as their visual, acoustical, topological, and daylight characteristics. Additionally, we have included location-specific characteristics for the buildings, including climatic data and points of interest within walking distance.
Changelog
- v3.0.0 (2023-03-31):
- Updated the dataset increasing the total number of apartments to 45176 and incorporating fixes to some of the sites. The update includes re-digitized apartments and thus alters some ID values.
- v2.2.1 (2023-03-10):
- A file,
location_ratings.csv
, has been included to provide ratings of the locations in which the buildings are situated. The ratings, provided by Fahrländer Partner AG, give insights into the living situation at the buildings' addresses. Details for the different dimensions are provided below. - The file
location.csv
has been updated to include the minimum and maximum temperatures for the locations in which the buildings are situated.
- A file,
- v2.1.0 (2022-12-23):
- A file,
locations.csv
, has been included to provide information on the climatic and infrastructural characteristics of the locations in which each building is situated
- A file,
- v2.0.0 (2022-10-17):
- Additional to the residential units, we also include the commercial and public parts (such as staircases) of the models. The field
unit_usage
describes whether an area belongs to a commercial, residential, janitor or public part of the building - Added the fields
elevation
andheight
to geometries.csv to describe the elevation above the terrain surface and the height of objects. - Added the field
plan_id
which allows identifying which floors are based on the same floor plan (in some cases multiple floors of a building share the same floor plan - Improved the ordering of fields in the CSV files (instead of alphabetic order)
- Minor changes to individual sites
- Additional to the residential units, we also include the commercial and public parts (such as staircases) of the models. The field
Procurement
The data is sourced from commercial clients of Archilyse AG specializing on the digitization and analysis of buildings. The existing building plans of clients are converted into a geo-referenced, semantically annotated representation and undergo a manual Q/A process to ensure the accuracy of the data and to ensure a maximum 5%-deviation in the apartments' areas (validated with a median deviation of 1.2%).
Geometries
The dataset contains a file geometries.csv
which contains the geometries of all areas, walls, railings, columns, windows, doors and features (sinks, bathtubs, etc.) of an apartment.
In total, the datasets contain the 2D geometry of ~1.7 million separators (walls, railings), ~715,000 openings (windows, doors), ca. 520,000 areas (rooms, bathrooms, kitchens, etc.), and ~315,000 features (sinks, toilets, bathtubs, etc.).
Each row contains:
apartment_id
: The ID of the apartment (for features, areas), note: an apartment id is only unique per sitesite_id
: The ID of the sitebuilding_id
: The ID of the buildingfloor_id
: The ID of the floorplan_id
: The ID of the plan on which the floor is based, multiple floors of a building might be based on the same planunit_id
: The ID of the unit in which the element is spatially contained (for features, areas)area_id
: The ID of the area in which the element is spatially contained (for features)unit_usage
: The usage of the unit, possible values are: RESIDENTIAL, COMMERCIAL, PUBLIC, JANITORentity_type
: The entity type (area, separator, opening, feature)entity_subtype
: The entity’s sub-type (e.g. WALL)geometry
: The element’s geometry as a WKT geometry in meters. The geometry is given in the site’s local coordinate system. I.e. the position between elements of the same site are correct in respect to each other. The +y direction points northwards, the +x direction points eastwards.elevation
: The object's elevation above the terrain surface in meters. We assume one terrain baseline per building, thus all walls in a given floor share the same elevation value. However, windows in particular might start at different elevations and have differing heights.height
: The height of the entity in meters, note: In many cases, a default height is assumed
An example:
column | |
---|---|
apartment_id |
d4438f2129b30290845ce7eef98a5ba7 |
site_id | 127 |
building_id | 164 |
plan_id | 492 |
floor_id | 861 |
unit_id | 63777 |
area_id | 767676 |
unit_usage | RESIDENTIAL |
entity_type | area |
entity_subtype | LIVING_ROOM |
geometry |
POLYGON ((-6.1501158933490139 -4.8490786654693... |
elevation | 0 |
height | 2.6 |
Simulations
Besides the geometrical model, we also provide simulation data on the visual, acoustic, solar, layout, and connectivity-related characteristics of the apartments. The file simulations.csv
contains the simulation data aggregated on a per-area basis. Each row contains the identifier columns area_id
, unit_id
, apartment_id
, floor_id
, building_id
, site_id
as defined above as well as 367 simulation columns. Each simulation column is formatted as:
<simulation_category>_<simulation_dimensions>_<aggregation_function>
For instance. the column view_buildings_median
describes the amount of building surface that can be seen from any point in a given room. The aggregation methods vary per simulation category and are described in detail below.
Layout
The layout features represent simple features based on the geometry and composition of a room, the dataset provides the following information in an unaggregated form.
Area Basics / Geometry
dimension | description |
---|---|
layout_area_type | The area’s area type |
layout_net_area | The area’s share of the apartment’s net area (e.g. 0 for a balcony) |
layout_area | The area’s actual area |
layout_perimeter | The area’s perimeter |
layout_compactness | The area’s compactness (the Polsby–Popper score) |
layout_room_count | The area’s share to the apartment’s room count |
layout_is_navigable | True if the area is navigable by a wheelchair |
Area Features
dimension | description |
---|---|
layout_has_sink | True if the area has a sink |
layout_has_shower | True if the area has a shower |
layout_has_bathtub | True if the area has a bathtub |
layout_has_toilet | True if the area has a toilet |
layout_has_stairs | True if the area has stairs |
layout_has_entrance_door | True if the area is directly leading to an exit of the apartment |
Area Windows / Doors
dimension | description |
---|---|
layout_number_of_doors | The number of doors directly leading to the area |
layout_number_of_windows | The number of windows of the area |
layout_door_perimeter | The sum of all door lengths directly leading to the area |
layout_window_perimeter | The sum of all window lengths of the area |
Area Walls / Railings
dimension | description |
---|---|
layout_open_perimeter | The sum of all of the boundaries of the area that are neither walls nor railings |
layout_railing_perimeter | The sum of all of the boundaries of the area that are railings |
layout_mean_walllengths | The mean length of the area’s sides |
layout_std_walllengths | The standard deviation of the lengths of the area’s sides |
Area Adjacency
dimension | description |
---|---|
layout_connects_to_bathroom | True if the area connects to a bathroom |
layout_connects_to_private_outdoor | True if the area connects to an outside area that is private to the apartment |
View
The views from an object help to understand the impact of the surroundings on the object. The view simulation calculates the visible amount of buildings, greenery, water, etc. on each individual hexagon from the analyzed object. The values are expressed in steradians (sr) and represent the amount a particular object category occupies in the spherical field of view.
Each of the following dimensions is provided using the room-wise aggregations' min, max, mean, std, median, p20, and p80. For instance, the column view_greenery_p20
describes the amount of greenery that can be seen from at least 20% of the positions in the area.
dimension | description |
---|---|
view_buildings | The amount of visible buildings |
view_greenery | The amount of visible greenery |
view_ground | The amount of visible ground |
view_isovist | The amount of visible isovist |
view_mountains_class_2 | The amount of visible mountains of UN mountain class 2 |
view_mountains_class_3 | The amount of visible mountains of UN mountain class 3 |
view_mountains_class_4 | The amount of visible mountains of UN mountain class 4 |
view_mountains_class_5 | The amount of visible mountains of UN mountain class 5 |
view_mountains_class_6 | The amount of visible mountains of UN mountain class 6 |
view_railway_tracks | The amount of visible railway_tracks |
view_site | The amount of visible site |
view_sky | The amount of visible sky |
view_tertiary_streets | The amount of visible tertiary_streets |
view_secondary_streets | The amount of visible secondary_streets |
view_primary_streets | The amount of visible primary_streets |
view_pedestrians | The amount of visible pedestrians |
view_highways | The amount of visible highways |
view_water | The amount of visible water |
Sun
Sun simulations help to understand the impact of solar radiation on the object. The outcome of the sun simulations helps to identify surfaces that have great solar potential. Sun simulations are defined by the amount of solar radiation on each individual hexagon from the analyzed object. The sun simulation not only includes direct sun but also considers scattered light. The sun simulation values are given in Kilolux (klx). Simulations are performed for the days of the summer solstice, winter solstice, and the vernal equinox.
Each of the following dimensions is provided using the room-wise aggregations' min, max, mean, std, median, p20, and p80. For instance, column sun_201806211200_median
describes the median amount of direct daylight received on the positions in the area.
Vernal Equinox
dimension | description |
---|---|
sun_201803210800 | Daylight at 08:00 on 21st of March |
sun_201803211000 | Daylight at 10:00 on 21st of March |
sun_201803211200 | Daylight at 12:00 on 21st of March |
sun_201803211400 | Daylight at 14:00 on 21st of March |
sun_201803211600 | Daylight at 16:00 on 21st of March |
sun_201803211800 | Daylight at 18:00 on 21st of March |
Summer Solstice
dimension | description |
---|---|
sun_201806210600 | Daylight at 06:00 on 21st of June |
sun_201806210800 | Daylight at 08:00 on 21st of June |
sun_201806211000 | Daylight at 10:00 on 21st of June |
sun_201806211200 | Daylight at 12:00 on 21st of June |
sun_201806211400 | Daylight at 14:00 on 21st of June |
sun_201806211600 | Daylight at 16:00 on 21st of June |
sun_201806211800 | Daylight at 18:00 on 21st of June |
sun_201806212000 | Daylight at 20:00 on 21st of June |
Winter Solstice
dimension | description |
---|---|
sun_201812211000 | Daylight at 10:00 on 21st of December |
sun_201812211200 | Daylight at 12:00 on 21st of December |
sun_201812211400 | Daylight at 14:00 on 21st of December |
sun_201812211600 | Daylight at 16:00 on 21st of December |
Noise / Window Noise
Noise level and the distribution of elements from an area help to understand how an object is exposed to the acoustics of this area. The acoustic simulation calculates the noise intensity on each individual hexagon from the analyzed object considering traffic and train noise datasets. Adjacent buildings are considered noise-blocking elements. The values are expressed in dBA (decibels).
Window Noise
The noise per window of a given area is aggregated via min
and max
. For instance, window_noise_train_day_max
represents the maximum amount of noise received on any window of the area.
dimension | description |
---|---|
window_noise_traffic_day | The amount of noise received on the area’s windows from daytime car traffic |
window_noise_traffic_night | The amount of noise received on the area’s windows from night-time car traffic |
window_noise_train_day | The amount of noise received on the area’s windows from daytime train traffic |
window_noise_train_night | The amount of noise received on the area’s windows from night-time train traffic |
Area-Wise Noise
The area-wise noise describes the amount of noise received from a noise source aggregated over the whole area in an unaggregated form. For instance, noise_traffic_night
describes the dBA of noise received in the area from car traffic at night when propagating noise from all windows.
dimension | description |
---|---|
noise_traffic_day | The amount of noise received in the area from daytime car traffic |
noise_traffic_night | The amount of noise received in the area from night-time car traffic |
noise_train_day | The amount of noise received in the area from daytime train traffic |
noise_train_night | The amount of noise received in the area from night-time train traffic |
Connectivity
Centrality simulations help to analyze a floor plan, whether it’s a shopping mall and you want to identify prominent areas in order to select the most prominent spot or it’s an interior design circulation path and you want to determine open floor plan areas. Centrality simulations are done using topological measures that score grid cells by their importance as a part of a grid cell network.
The distances and centralities are aggregated via min, max, mean, std, median, p20, and p80. For instance, connectivity_balcony_distance_min
describes the shortest distance to the next balcony from the point closest to the balcony in the area.
Distances
dimension | description |
---|---|
connectivity_room_distance | Distance to the next area of type ROOM |
connectivity_living_dining_distance | Distance to the next area of type LIVING_DINING |
connectivity_bathroom_distance | Distance to the next area of type BATHROOM |
connectivity_kitchen_distance | Distance to the next area of type KITCHEN |
connectivity_balcony_distance | Distance to the next area of type BALCONY |
connectivity_loggia_distance | Distance to the next area of type LOGGIA |
connectivity_entrance_door_distance | Distance to the next apartment exit |
Centralities
dimension | description |
---|---|
connectivity_eigen_centrality | The Eigen-Centrality value |
connectivity_betweenness_centrality | The Betweenness-Centrality value |
connectivity_closeness_centrality | The Closeness-Centrality value |
Location Properties
In addition to the apartment-related data, we also provide simulation data on the climatic, and infrastructural characteristics of the locations. The file locations.csv
contains the simulation data aggregated on a per-building basis. Each row contains the identifier building_id
corresponding to the building ids referenced in geometries.csv
and simulations.csv
.
Climate
The climate features represent 39 simple features based on the spatial climate analysis of Meteo Swiss as derived from MeteoSwiss. Each column is formatted as climate_<category>_<period>.
For instance, the column climate_tnorm_january
describes the monthly mean temperature in degrees Celsius (from the norm period of 1991-2020) at the location of the building. The aggregation methods vary per simulation category and are described in detail below.
Temperature Normals
dimension | description |
---|---|
climate_tnorm_year | The yearly mean temperature in degrees Celsius of the current norm period from 1991 to 2020 (TnormY9120) |
climate_tnorm_january | The monthly mean temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120) |
climate_tnorm_februry | The monthly mean temperature in February in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120) |
... | ... |
climate_tnorm_december | The monthly mean temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120) |
climate_tminnorm_january | The monthly minimum temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TminnormM9120) |
... | |
climate_tminnorm_december | The monthly minimum temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120) |
climate_tmaxnorm_january | The monthly maximum temperature in January in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120) |
... | |
climate_tmaxnorm_december | The monthly maximum temperature in December in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120) |
Sunshine Duration Normals
dimension | description |
---|---|
climate_snorm_year | The yearly mean relative sunshine duration in percent of the current norm period from 1991 to 2020 (SnormY9120). Relative sunshine duration (RSD) is the ratio between the effective sunshine duration and the duration maximally possible if no clouds were covering the sun. A period with sunshine is defined as a period when the direct solar irradiance exceeds 200 W/m² |
climate_snorm_january | The monthly mean relative sunshine duration for January in percent of the current norm period |
climate_snorm_februry | The monthly mean relative sunshine duration for February in percent of the current norm period |
... | ... |
climate_snorm_december | The monthly mean relative sunshine duration for December in percent of the current norm period |
Precipitation Normals
dimension | description |
---|---|
climate_rnorm_year | The yearly mean precipitation in mm of the current norm period (RnormY9120) |
climate_rnorm_january | The monthly mean precipitation for January in mm of the current norm period (RnormM9120) |
climate_rnorm_februry | The monthly mean precipitation for February in mm of the current norm period (RnormM9120) |
... | ... |
climate_rnorm_december | The monthly mean precipitation for December mm of the current norm period (RnormM9120) |
10-Minute Walkshed Infrastructure
Based on OpenStreetMap data and its tagging system we counted all 465 tags (key and value tuples as listed here: https://wiki.openstreetmap.org/wiki/Map_features) which can be reached within a 10-minute walk from the location of the building. Each column is formatted as walkshed_<poi_category>_<poi_type>.
For instance, the column walkshed_shop_coffee
describes the number of coffee shops located within 10 minutes of walking from the building.
The following is an excerpt of support categories and their corresponding types.
shop: antique, art, ...
amenity: art, atm, ...
tourism: alpine, attraction, ...
leisure: amusement, beach, ...
healthcare: clinic, dentist, ...
historic: archaeological, battlefield, ...
ariaelway: station
Location Ratings
The location ratings, provided by Fahrländer Partner AG, give insights into the living situation at locations in which the buildings are situated. The file location_ratings.csv provides the following information:
dimension | description |
---|---|
location_rating_MIKRAT_W | Living situation - Overall (1.0=worst, 5.0=best) |
location_rating_IMAGE_W | Living situation - Image (1.0=worst, 5.0=best) |
location_rating_DL_W | Living situation - Service Quality (1.0=worst, 5.0=best) |
location_rating_FZ_W |
Living situation - Leisure Quality (1.0=worst, 5.0=best) |
location_rating_NASE_W_DOM | The most dominant segment of demand: 1 Rural-traditional 2 Modern worker 3 Transitional-alternative 4 Traditional middle class 5 Liberal middle class 6 Established alternative 7 Upper middle class 8 Professional elite 9 Urban elite 10 Unknown More Information |
location_rating_FGFRQZ |
The mean number of pedestrians per hour throughout a day between 7 am and 8 pm of an average working day. |
Files
swiss-dwellings-v3.0.0.zip
Files
(931.9 MB)
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Additional details
Related works
- Is compiled by
- Software: https://github.com/Archilyse/Archilyse (URL)
- Is referenced by
- Other: https://archilyse.github.io/ (URL)