Swiss Dwellings: A large dataset of apartment models including aggregated geolocation-based simulation results covering viewshed, natural light, traffic noise, centrality and geometric analysis
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<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. <strong>Changelog</strong> <strong>v2.0.0 (2022-10-17):</strong> Additional to the residential units, we also include the commercial and public parts (such as stair cases) 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 to identify 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 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 contains 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 on, multiple floors of a building might be 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, COMMERICAL, 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> Beside 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><simulation_category>_<simulation_dimensions>_<aggregation_function></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 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 <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 certain object category occupies in the spherical field of view. Each of the following dimension 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 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 <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 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 <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 gridcell 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-Centrality value connectivity_closeness_centrality The Closeness-Centrality value
**简介** 本数据集涵盖约3100栋建筑中的42500余套公寓（共250000个房间）的详细数据，包含其几何形态、房间类型，以及视觉、声学、拓扑与日照特性。 **更新日志** **v2.0.0（2022-10-17）：** 除居住单元外，本次更新还新增了模型中的商业与公共区域（如楼梯间）。新增`unit_usage`字段，用于标识区域属于建筑的商业、居住、保洁或公共部分；在`geometries.csv`中新增`elevation`与`height`字段，分别描述物体距地表的高程及物体高度；新增`plan_id`字段，可用于识别基于同一平面图的楼层（部分建筑的多层可共享同一平面图）；优化CSV文件内字段的排序方式（不再采用字母序）；对部分站点数据进行了小幅调整。 **数据获取** 本数据集来源于专注于建筑数字化与分析的Archilyse AG公司的商业客户。客户提供的现有建筑平面图将被转换为地理参考、带有语义标注的格式，并经过人工质量审核流程，以确保数据准确性，同时将公寓面积偏差控制在5%以内（经验证，实际中位偏差仅为1.2%）。 **几何数据** 本数据集包含`geometries.csv`文件，存储了公寓所有区域、墙体、栏杆、柱体、窗户、门及设施（水槽、浴缸等）的几何信息。数据集总计包含约150万个分隔构件（墙体、栏杆）、67万个开口构件（窗户、门）、约40万个区域（房间、卫生间、厨房等）以及29万个设施（水槽、坐便器、浴缸等）的二维几何数据。 每行数据包含以下字段： - `apartment_id`：公寓ID（仅适用于设施、区域类要素），注：公寓ID仅在单个站点内唯一 - `site_id`：站点ID - `building_id`：建筑ID - `floor_id`：楼层ID - `plan_id`：该楼层所基于的平面图ID，同一建筑的多个楼层可共享同一平面图 - `unit_id`：要素所在的单元ID（仅适用于设施、区域类要素） - `area_id`：要素所在的区域ID（仅适用于设施类要素） - `unit_usage`：单元用途，可选值包括：RESIDENTIAL（居住）、COMMERCIAL（商业）、PUBLIC（公共）、JANITOR（保洁） - `entity_type`：实体类型，可选值包括：area（区域）、separator（分隔构件）、opening（开口构件）、feature（设施） - `entity_subtype`：实体子类型，例如`WALL`（墙体） - `geometry`：要素的几何信息，以米为单位的熟知文本（Well-Known Text，WKT）几何格式。几何数据采用站点本地坐标系，同一站点内各要素的相对位置准确，其中+y轴指向北方，+x轴指向东方 - `elevation`：物体距地表的高程，单位为米。本数据集假设每栋建筑采用统一的地表基准面，因此同一楼层的所有墙体具有相同的高程值，但部分窗户的起始高程与高度可能存在差异 - `height`：实体的高度，单位为米，注：多数情况下采用默认高度 示例：某柱体的字段值如下： `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` **模拟数据** 除几何模型外，本数据集还提供公寓的视觉、声学、日照、布局及连通性相关特性的模拟数据。`simulations.csv`文件包含按区域聚合的模拟数据，每行包含前文定义的`area_id`、`unit_id`、`apartment_id`、`floor_id`、`building_id`、`site_id`等标识字段，以及367个模拟字段。所有模拟字段的命名格式均遵循：`<模拟类别>_<模拟维度>_<聚合函数>`。例如，`view_buildings_median`字段表示从指定房间内任意点位可观测到的建筑表面积。不同模拟类别的聚合方式详见下文。 **布局特征** 布局特征基于房间的几何形态与空间组成，本数据集以未聚合的形式提供以下信息： #### 区域基础/几何维度 - `layout_area_type`：区域的类型 - `layout_net_area`：该区域占公寓净面积的比例（例如阳台的该值为0） - `layout_area`：区域的实际面积 - `layout_perimeter`：区域的周长 - `layout_compactness`：区域的紧致性（波尔斯比-波普尔得分） - `layout_room_count`：该区域占公寓总房间数的比例 - `layout_is_navigable`：若该区域可通过轮椅通行，则为True #### 区域设施维度 - `layout_has_sink`：该区域是否配有水槽 - `layout_has_shower`：该区域是否配有淋浴设施 - `layout_has_bathtub`：该区域是否配有浴缸 - `layout_has_toilet`：该区域是否配有坐便器 - `layout_has_stairs`：该区域是否包含楼梯 - `layout_has_entrance_door`：该区域是否直接连通公寓出口 #### 区域门窗维度 - `layout_number_of_doors`：直接连通该区域的门的数量 - `layout_number_of_windows`：该区域的窗户数量 - `layout_door_perimeter`：直接连通该区域的所有门的总长度 - `layout_window_perimeter`：该区域所有窗户的总长度 #### 区域墙体/栏杆维度 - `layout_open_perimeter`：该区域边界中非墙体、非栏杆的总长度 - `layout_railing_perimeter`：该区域边界中栏杆的总长度 - `layout_mean_walllengths`：该区域各边长度的平均值 - `layout_std_walllengths`：该区域各边长度的标准差 #### 区域邻接维度 - `layout_connects_to_bathroom`：该区域是否连通卫生间 - `layout_connects_to_private_outdoor`：该区域是否连通公寓专属的室外区域 **视野模拟** 对物体视野的分析可用于评估周边环境对该物体的影响。视野模拟会计算分析对象每个六边形网格内可见的建筑、绿化、水体等要素的占比，结果以球面度（sr）为单位，表示某类物体在球面视野中所占的比例。所有视野维度的模拟结果均采用按房间聚合的统计值，包括最小值（min）、最大值（max）、平均值（mean）、标准差（std）、中位数（median）、20分位数（p20）和80分位数（p80）。例如，`view_greenery_p20`字段表示该区域内至少20%的点位可观测到的绿化面积占比。各视野维度字段说明如下： - `view_buildings`：可见建筑的占比 - `view_greenery`：可见绿化的占比 - `view_ground`：可见地面的占比 - `view_isovist`：可见等视线域的占比 - `view_mountains_class_2`：可见联合国山脉等级2的山体占比 - `view_mountains_class_3`：可见联合国山脉等级3的山体占比 - `view_mountains_class_4`：可见联合国山脉等级4的山体占比 - `view_mountains_class_5`：可见联合国山脉等级5的山体占比 - `view_mountains_class_6`：可见联合国山脉等级6的山体占比 - `view_railway_tracks`：可见铁路线路的占比 - `view_site`：可见站点区域的占比 - `view_sky`：可见天空的占比 - `view_tertiary_streets`：可见三级道路的占比 - `view_secondary_streets`：可见二级道路的占比 - `view_primary_streets`：可见一级道路的占比 - `view_pedestrians`：可见行人区域的占比 - `view_highways`：可见高速公路的占比 - `view_water`：可见水体的占比 **日照模拟** 日照模拟可用于评估太阳辐射对物体的影响，帮助识别具备高太阳能利用潜力的表面。该模拟基于分析对象每个六边形网格接收到的太阳辐射量计算，不仅包含直接日照，还考虑了散射光的影响，结果以千勒克斯（klx）为单位。模拟分别在夏至、冬至与春分三个日期进行。所有日照维度的模拟结果均采用按房间聚合的统计值（min、max、mean、std、median、p20、p80）。例如，`sun_201806211200_median`字段表示该区域内各点位接收到的正午直射日照量的中位数。 #### 春分日模拟字段 - `sun_201803210800`：3月21日08:00的日照量 - `sun_201803211000`：3月21日10:00的日照量 - `sun_201803211200`：3月21日12:00的日照量 - `sun_201803211400`：3月21日14:00的日照量 - `sun_201803211600`：3月21日16:00的日照量 - `sun_201803211800`：3月21日18:00的日照量 #### 夏至日模拟字段 - `sun_201806210600`：6月21日06:00的日照量 - `sun_201806210800`：6月21日08:00的日照量 - `sun_201806211000`：6月21日10:00的日照量 - `sun_201806211200`：6月21日12:00的日照量 - `sun_201806211400`：6月21日14:00的日照量 - `sun_201806211600`：6月21日16:00的日照量 - `sun_201806211800`：6月21日18:00的日照量 - `sun_201806212000`：6月21日20:00的日照量 #### 冬至日模拟字段 - `sun_201812211000`：12月21日10:00的日照量 - `sun_201812211200`：12月21日12:00的日照量 - `sun_201812211400`：12月21日14:00的日照量 - `sun_201812211600`：12月21日16:00的日照量 **噪声/窗面噪声** 对区域内噪声水平与要素分布的分析，可用于评估物体所承受的区域声学环境影响。声学模拟会结合交通与列车噪声数据集，计算分析对象每个六边形网格内的噪声强度，相邻建筑将被视为噪声遮挡物，结果以分贝（dBA）为单位。 #### 窗面噪声 指定区域各窗户的噪声值将通过最小值（min）与最大值（max）进行聚合。例如，`window_noise_train_day_max`字段表示该区域任意窗口接收到的日间列车噪声最大值。各窗面噪声字段说明如下： - `window_noise_traffic_day`：该区域窗户接收到的日间机动车交通噪声 - `window_noise_traffic_night`：该区域窗户接收到的夜间机动车交通噪声 - `window_noise_train_day`：该区域窗户接收到的日间列车噪声 - `window_noise_train_night`：该区域窗户接收到的夜间列车噪声 #### 区域整体噪声 区域整体噪声描述了整个区域从所有噪声源接收的噪声总量，以未聚合的形式提供。例如，`noise_traffic_night`字段表示该区域从所有窗口接收到的夜间机动车交通噪声的总分贝值。各区域噪声字段说明如下： - `noise_traffic_day`：该区域接收到的日间机动车交通噪声 - `noise_traffic_night`：该区域接收到的夜间机动车交通噪声 - `noise_train_day`：该区域接收到的日间列车噪声 - `noise_train_night`：该区域接收到的夜间列车噪声 **连通性模拟** 中心性模拟可用于分析平面图，例如在购物中心中识别核心区域以选择最优点位，或在室内设计中分析通行路径以确定开放式平面区域。中心性模拟通过拓扑度量方法对网格单元在网格网络中的重要性进行评分，所有距离与中心性指标均采用按房间聚合的统计值（min、max、mean、std、median、p20、p80）。例如，`connectivity_balcony_distance_min`字段表示该区域内距离阳台最近的点位到下一个阳台的最短距离。 #### 距离维度字段 - `connectivity_room_distance`：到下一个ROOM（房间）类型区域的距离 - `connectivity_living_dining_distance`：到下一个LIVING_DINING（客餐厅）类型区域的距离 - `connectivity_bathroom_distance`：到下一个BATHROOM（卫生间）类型区域的距离 - `connectivity_kitchen_distance`：到下一个KITCHEN（厨房）类型区域的距离 - `connectivity_balcony_distance`：到下一个BALCONY（阳台）类型区域的距离 - `connectivity_loggia_distance`：到下一个LOGGIA（凉廊）类型区域的距离 - `connectivity_entrance_door_distance`：到下一个公寓出口的距离 #### 中心性维度字段 - `connectivity_eigen_centrality`：特征中心性（Eigen-Centrality）值 - `connectivity_betweenness_centrality`：介数中心性（Betweenness-Centrality）值 - `connectivity_closeness_centrality`：接近度中心性（Closeness-Centrality）值
