Introduction
\n\nThis dataset contains detailed data on over 42,500 apartments (250,000 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.
\n\nChangelog
\n\nlocation_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.location.csv has been updated to include the minimum and maximum temperatures for the locations in which the buildings are situated.locations.csv, has been included to provide information on the climatic and infrastructural characteristics of the locations in which each building is situatedunit_usage describes whether an area belongs to a commercial, residential, janitor or public part of the buildingelevation and height to geometries.csv to describe the elevation above the terrain surface and the height of objects.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 planProcurement
\n\nThe 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%).
\n\nGeometries
\n\nThe 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 ~1.5 million separators (walls, railings), ~670,000 openings (windows, doors), ca. 400,000 areas (rooms, bathrooms, kitchens, etc.), and ~290,000 features (sinks, toilets, bathtubs, etc.).
\n\nEach row contains:
\n\napartment_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 assumedAn example:
\n\n| column | \n\t\t\t\n\t\t |
|---|---|
| apartment_id | \n\t\t\t\n\t\t\t d4438f2129b30290845ce7eef98a5ba7 \n\t\t\t | \n\t\t
| site_id | \n\t\t\t127 | \n\t\t
| building_id | \n\t\t\t164 | \n\t\t
| plan_id | \n\t\t\t492 | \n\t\t
| floor_id | \n\t\t\t861 | \n\t\t
| unit_id | \n\t\t\t63777 | \n\t\t
| area_id | \n\t\t\t767676 | \n\t\t
| unit_usage | \n\t\t\tRESIDENTIAL | \n\t\t
| entity_type | \n\t\t\tarea | \n\t\t
| entity_subtype | \n\t\t\tLIVING_ROOM | \n\t\t
| geometry | \n\t\t\t\n\t\t\t POLYGON ((-6.1501158933490139 -4.8490786654693... \n\t\t\t | \n\t\t
| elevation | \n\t\t\t0 | \n\t\t
| height | \n\t\t\t2.6 | \n\t\t
Simulations
\n\nBesides 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
\n\nThe layout features represent simple features based on the geometry and composition of a room, the dataset provides the following information in an unaggregated form.
\n\nArea Basics / Geometry
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| layout_area_type | \n\t\t\tThe area’s area type | \n\t\t
| layout_net_area | \n\t\t\tThe area’s share of the apartment’s net area (e.g. 0 for a balcony) | \n\t\t
| layout_area | \n\t\t\tThe area’s actual area | \n\t\t
| layout_perimeter | \n\t\t\tThe area’s perimeter | \n\t\t
| layout_compactness | \n\t\t\tThe area’s compactness (the Polsby–Popper score) | \n\t\t
| layout_room_count | \n\t\t\tThe area’s share to the apartment’s room count | \n\t\t
| layout_is_navigable | \n\t\t\tTrue if the area is navigable by a wheelchair | \n\t\t
Area Features
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| layout_has_sink | \n\t\t\tTrue if the area has a sink | \n\t\t
| layout_has_shower | \n\t\t\tTrue if the area has a shower | \n\t\t
| layout_has_bathtub | \n\t\t\tTrue if the area has a bathtub | \n\t\t
| layout_has_toilet | \n\t\t\tTrue if the area has a toilet | \n\t\t
| layout_has_stairs | \n\t\t\tTrue if the area has stairs | \n\t\t
| layout_has_entrance_door | \n\t\t\tTrue if the area is directly leading to an exit of the apartment | \n\t\t
Area Windows / Doors
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| layout_number_of_doors | \n\t\t\tThe number of doors directly leading to the area | \n\t\t
| layout_number_of_windows | \n\t\t\tThe number of windows of the area | \n\t\t
| layout_door_perimeter | \n\t\t\tThe sum of all door lengths directly leading to the area | \n\t\t
| layout_window_perimeter | \n\t\t\tThe sum of all window lengths of the area | \n\t\t
Area Walls / Railings
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| layout_open_perimeter | \n\t\t\tThe sum of all of the boundaries of the area that are neither walls nor railings | \n\t\t
| layout_railing_perimeter | \n\t\t\tThe sum of all of the boundaries of the area that are railings | \n\t\t
| layout_mean_walllengths | \n\t\t\tThe mean length of the area’s sides | \n\t\t
| layout_std_walllengths | \n\t\t\tThe standard deviation of the lengths of the area’s sides | \n\t\t
Area Adjacency
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| layout_connects_to_bathroom | \n\t\t\tTrue if the area connects to a bathroom | \n\t\t
| layout_connects_to_private_outdoor | \n\t\t\tTrue if the area connects to an outside area that is private to the apartment | \n\t\t
View
\n\nThe 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.
\n\nEach 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 | \n\t\t\tdescription | \n\t\t
|---|---|
| view_buildings | \n\t\t\tThe amount of visible buildings | \n\t\t
| view_greenery | \n\t\t\tThe amount of visible greenery | \n\t\t
| view_ground | \n\t\t\tThe amount of visible ground | \n\t\t
| view_isovist | \n\t\t\tThe amount of visible isovist | \n\t\t
| view_mountains_class_2 | \n\t\t\tThe amount of visible mountains of UN mountain class 2 | \n\t\t
| view_mountains_class_3 | \n\t\t\tThe amount of visible mountains of UN mountain class 3 | \n\t\t
| view_mountains_class_4 | \n\t\t\tThe amount of visible mountains of UN mountain class 4 | \n\t\t
| view_mountains_class_5 | \n\t\t\tThe amount of visible mountains of UN mountain class 5 | \n\t\t
| view_mountains_class_6 | \n\t\t\tThe amount of visible mountains of UN mountain class 6 | \n\t\t
| view_railway_tracks | \n\t\t\tThe amount of visible railway_tracks | \n\t\t
| view_site | \n\t\t\tThe amount of visible site | \n\t\t
| view_sky | \n\t\t\tThe amount of visible sky | \n\t\t
| view_tertiary_streets | \n\t\t\tThe amount of visible tertiary_streets | \n\t\t
| view_secondary_streets | \n\t\t\tThe amount of visible secondary_streets | \n\t\t
| view_primary_streets | \n\t\t\tThe amount of visible primary_streets | \n\t\t
| view_pedestrians | \n\t\t\tThe amount of visible pedestrians | \n\t\t
| view_highways | \n\t\t\tThe amount of visible highways | \n\t\t
| view_water | \n\t\t\tThe amount of visible water | \n\t\t
Sun
\n\nSun 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.
\n\nEach 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
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| sun_201803210800 | \n\t\t\tDaylight at 08:00 on 21st of March | \n\t\t
| sun_201803211000 | \n\t\t\tDaylight at 10:00 on 21st of March | \n\t\t
| sun_201803211200 | \n\t\t\tDaylight at 12:00 on 21st of March | \n\t\t
| sun_201803211400 | \n\t\t\tDaylight at 14:00 on 21st of March | \n\t\t
| sun_201803211600 | \n\t\t\tDaylight at 16:00 on 21st of March | \n\t\t
| sun_201803211800 | \n\t\t\tDaylight at 18:00 on 21st of March | \n\t\t
Summer Solstice
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| sun_201806210600 | \n\t\t\tDaylight at 06:00 on 21st of June | \n\t\t
| sun_201806210800 | \n\t\t\tDaylight at 08:00 on 21st of June | \n\t\t
| sun_201806211000 | \n\t\t\tDaylight at 10:00 on 21st of June | \n\t\t
| sun_201806211200 | \n\t\t\tDaylight at 12:00 on 21st of June | \n\t\t
| sun_201806211400 | \n\t\t\tDaylight at 14:00 on 21st of June | \n\t\t
| sun_201806211600 | \n\t\t\tDaylight at 16:00 on 21st of June | \n\t\t
| sun_201806211800 | \n\t\t\tDaylight at 18:00 on 21st of June | \n\t\t
| sun_201806212000 | \n\t\t\tDaylight at 20:00 on 21st of June | \n\t\t
Winter Solstice
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| sun_201812211000 | \n\t\t\tDaylight at 10:00 on 21st of December | \n\t\t
| sun_201812211200 | \n\t\t\tDaylight at 12:00 on 21st of December | \n\t\t
| sun_201812211400 | \n\t\t\tDaylight at 14:00 on 21st of December | \n\t\t
| sun_201812211600 | \n\t\t\tDaylight at 16:00 on 21st of December | \n\t\t
Noise / Window Noise
\n\nNoise 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).
\n\nWindow Noise
\n\nThe 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 | \n\t\t\tdescription | \n\t\t
|---|---|
| window_noise_traffic_day | \n\t\t\tThe amount of noise received on the area’s windows from daytime car traffic | \n\t\t
| window_noise_traffic_night | \n\t\t\tThe amount of noise received on the area’s windows from night-time car traffic | \n\t\t
| window_noise_train_day | \n\t\t\tThe amount of noise received on the area’s windows from daytime train traffic | \n\t\t
| window_noise_train_night | \n\t\t\tThe amount of noise received on the area’s windows from night-time train traffic | \n\t\t
Area-Wise Noise
\n\nThe 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 | \n\t\t\tdescription | \n\t\t
|---|---|
| noise_traffic_day | \n\t\t\tThe amount of noise received in the area from daytime car traffic | \n\t\t
| noise_traffic_night | \n\t\t\tThe amount of noise received in the area from night-time car traffic | \n\t\t
| noise_train_day | \n\t\t\tThe amount of noise received in the area from daytime train traffic | \n\t\t
| noise_train_night | \n\t\t\tThe amount of noise received in the area from night-time train traffic | \n\t\t
\nConnectivity
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.
\n\nThe 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
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| connectivity_room_distance | \n\t\t\tDistance to the next area of type ROOM | \n\t\t
| connectivity_living_dining_distance | \n\t\t\tDistance to the next area of type LIVING_DINING | \n\t\t
| connectivity_bathroom_distance | \n\t\t\tDistance to the next area of type BATHROOM | \n\t\t
| connectivity_kitchen_distance | \n\t\t\tDistance to the next area of type KITCHEN | \n\t\t
| connectivity_balcony_distance | \n\t\t\tDistance to the next area of type BALCONY | \n\t\t
| connectivity_loggia_distance | \n\t\t\tDistance to the next area of type LOGGIA | \n\t\t
| connectivity_entrance_door_distance | \n\t\t\tDistance to the next apartment exit | \n\t\t
Centralities
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| connectivity_eigen_centrality | \n\t\t\tThe Eigen-Centrality value | \n\t\t
| connectivity_betweenness_centrality | \n\t\t\tThe Betweenness-Centrality value | \n\t\t
| connectivity_closeness_centrality | \n\t\t\tThe Closeness-Centrality value | \n\t\t
Location Properties
\n\nIn 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
\n\nThe 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
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| climate_tnorm_year | \n\t\t\tThe yearly mean temperature in degrees Celsius of the current norm period from 1991 to 2020 (TnormY9120) | \n\t\t
| climate_tnorm_january | \n\t\t\tThe monthly mean temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120) | \n\t\t
| climate_tnorm_februry | \n\t\t\tThe monthly mean temperature in February in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120) | \n\t\t
| ... | \n\t\t\t... | \n\t\t
| climate_tnorm_december | \n\t\t\tThe monthly mean temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120) | \n\t\t
| climate_tminnorm_january | \n\t\t\tThe monthly minimum temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TminnormM9120) | \n\t\t
| ... | \n\t\t\t\n\t\t |
| climate_tminnorm_december | \n\t\t\tThe monthly minimum temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120) | \n\t\t
| climate_tmaxnorm_january | \n\t\t\tThe monthly maximum temperature in January in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120) | \n\t\t
| ... | \n\t\t\t\n\t\t |
| climate_tmaxnorm_december | \n\t\t\tThe monthly maximum temperature in December in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120) | \n\t\t
Sunshine Duration Normals
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| climate_snorm_year | \n\t\t\tThe 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 | \n\t\t
| climate_snorm_january | \n\t\t\tThe monthly mean relative sunshine duration for January in percent of the current norm period | \n\t\t
| climate_snorm_februry | \n\t\t\tThe monthly mean relative sunshine duration for February in percent of the current norm period | \n\t\t
| ... | \n\t\t\t... | \n\t\t
| climate_snorm_december | \n\t\t\tThe monthly mean relative sunshine duration for December in percent of the current norm period | \n\t\t
Precipitation Normals
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| climate_rnorm_year | \n\t\t\tThe yearly mean precipitation in mm of the current norm period (RnormY9120) | \n\t\t
| climate_rnorm_january | \n\t\t\tThe monthly mean precipitation for January in mm of the current norm period (RnormM9120) | \n\t\t
| climate_rnorm_februry | \n\t\t\tThe monthly mean precipitation for February in mm of the current norm period (RnormM9120) | \n\t\t
| ... | \n\t\t\t... | \n\t\t
| climate_rnorm_december | \n\t\t\tThe monthly mean precipitation for December mm of the current norm period (RnormM9120) | \n\t\t
10-Minute Walkshed Infrastructure
\n\nBased 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.
\n\nshop: antique, art, ...amenity: art, atm, ...tourism: alpine, attraction, ...leisure: amusement, beach, ...healthcare: clinic, dentist, ...historic: archaeological, battlefield, ...ariaelway: stationLocation Ratings
\n\nThe 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:
\n\n| dimension | \n\t\t\tdescription | \n\t\t
|---|---|
| location_rating_MIKRAT_W | \n\t\t\tLiving situation - Overall (1.0=worst, 5.0=best) | \n\t\t
| location_rating_IMAGE_W | \n\t\t\tLiving situation - Image (1.0=worst, 5.0=best) | \n\t\t
| location_rating_DL_W | \n\t\t\tLiving situation - Service Quality (1.0=worst, 5.0=best) | \n\t\t
| location_rating_FZ_W | \n\t\t\t\n\t\t\t Living situation - Leisure Quality (1.0=worst, 5.0=best) \n\t\t\t | \n\t\t
| location_rating_NASE_W_DOM | \n\t\t\tThe most dominant segment of demand: \n\t\t\t \n\t\t\t1 Rural-traditional \n\t\t\t2 Modern worker \n\t\t\t3 Transitional-alternative \n\t\t\t4 Traditional middle class \n\t\t\t5 Liberal middle class \n\t\t\t6 Established alternative \n\t\t\t7 Upper middle class \n\t\t\t8 Professional elite \n\t\t\t9 Urban elite \n\t\t\t10 Unknown \n\t\t\t \n\t\t\tMore Information | \n\t\t
| location_rating_FGFRQZ | \n\t\t\t\n\t\t\t The mean number of pedestrians per hour throughout a day between 7 am and 8 pm of an average working day. | \n\t\t