Swiss Dwellings: A large dataset of apartment models including aggregated geolocation-based simulation results covering viewshed, natural light, traffic noise, centrality and geometric analysis

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 floor

building_id: The ID of the building

site_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:

__

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