DOI: 10.5281/zenodo.7477146
title: 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: <strong>Introduction</strong> This 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. <strong>Changelog</strong> <strong>v2.1.0 (2022-12-23)</strong>: A file, <code>locations.csv</code>, has been included to provide information on the climatic and infrastructural characteristics of the locations in which each building is situated <strong>v2.0.0 (2022-10-17):</strong> Additional to the residential units, we also include the commercial and public parts (such as staircases) of the models. The field <code>unit_usage</code> describes whether an area belongs to a commercial, residential, janitor or public part of the building Added the fields <code>elevation</code> and <code>height</code> to <em>geometries.csv</em> to describe the elevation above the terrain surface and the height of objects. Added the field <code>plan_id</code> 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 <strong>Procurement</strong> 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%). <strong>Geometries</strong> The dataset contains a file <code>geometries.csv</code> 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.). Each row contains: <code>apartment_id</code>: The ID of the apartment (for features, areas), <em>note</em>: an apartment id is only unique per site <code>site_id</code>: The ID of the site <code>building_id</code>: The ID of the building <code>floor_id</code>: The ID of the floor <code>plan_id</code>: The ID of the plan on which the floor is based, multiple floors of a building might be based on the same plan <code>unit_id</code>: The ID of the unit in which the element is spatially contained (for features, areas) <code>area_id</code>: The ID of the area in which the element is spatially contained (for features) <code>unit_usage</code>: The usage of the unit, possible values are: RESIDENTIAL, COMMERCIAL, PUBLIC, JANITOR <code>entity_type</code>: The entity type (<em>area, separator, opening, feature</em>) <code>entity_subtype</code>: The entity’s sub-type (e.g. <em>WALL</em>) <code>geometry</code>: 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. <code>elevation</code>: 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. <code>height</code>: The height of the entity in meters, <em>note</em>: 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 <strong>Simulations</strong> Besides the geometrical model, we also provide simulation data on the visual, acoustic, solar, layout, and connectivity-related characteristics of the apartments. The file <code>simulations.csv</code> contains the simulation data aggregated on a per-area basis. Each row contains the identifier columns <code>area_id</code>, <code>unit_id</code>, <code>apartment_id</code>, <code>floor_id</code>, <code>building_id</code>, <code>site_id</code> as defined above as well as 367 simulation columns. Each simulation column is formatted as: <pre><code>&lt;simulation_category&gt;_&lt;simulation_dimensions&gt;_&lt;aggregation_function&gt;</code></pre> For instance. the column <code>view_buildings_median</code> 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. <strong>Layout</strong> The <em>layout</em> 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 <strong>View</strong> 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' <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, the column <code>view_greenery_p20</code> 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 <strong>Sun</strong> 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' <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, column <code>sun_201806211200_median</code> 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 <strong>Noise / Window Noise</strong> 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 <code>min</code> and <code>max</code>. For instance, <code>window_noise_train_day_max</code> 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, <code>noise_traffic_night</code> 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 <br> <strong>Connectivity</strong> 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 <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, <code>connectivity_balcony_distance_min</code> 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-Cent