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 42,207 apartments (242,257 rooms) in 3,093 buildings including their geometries, room typology as well as their visual, acoustical, topological and daylight characteristics.
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 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 contains the 2D geometry of ~1.2 million separators (walls, railings), ~550,000 openings (windows, doors), ca. 400,000 areas (rooms, bathrooms, kitchens, etc.) and ~240,000 features (sinks, toilets, bathtubs, etc.).
Each row contains:
entity_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. 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.area_id
: The ID of the area in which the element is spatially contained (for features)unit_id
: The ID of the unit in which the element is spatially contained (for features, areas)apartment_id
: The ID of the apartment (for features, areas)floor_id
: The ID of the floorbuilding_id
: The ID of the buildingsite_id
: The ID of the site
An example:
column | |
---|---|
entity_type | area |
entity_subtype | ROOM |
geometry | POLYGON ((-2.10406 4.02039… |
site_id | 127 |
building_id | 164 |
floor_id | 12864 |
apartment_id | d4438f2129b30290845ce7eef98a5ba7 |
unit_id | 76643 |
area_id | 684674 |
Simulations
Beside 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 areas boundaries that are neither walls nor railings |
layout_railing_perimeter | The sum of all of the areas boundaries 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 Adjecency
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 certain object category occupies in the spherical field of view.
Each of the following dimension 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 the 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 sun 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 summer solstice, winter solstice and vernal equinox.
Each of the following dimension 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 helps 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 as 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 gridcell 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 |
Files
geometries.csv
Files
(2.3 GB)
Name | Size | Download all |
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md5:2c78951982ba87f0a28ef6ca0724e48a
|
792.5 MB | Preview Download |
md5:463d318e7224cf25dc3cf673caca79d3
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1.5 GB | Preview Download |
Additional details
Related works
- Is referenced by
- Other: https://archilyse.github.io/ (URL)