建筑學與數據科學 (Architectonics and Data Science)

Understanding the site and topography are crucial first step of any architectural project. Site modelling can become very daunting, expensive, or just cumbersome, often having to use various software to just grasp a general awareness of the site. I found the most efficient method using QGIS and Rhino Grasshopper to cover most of my analysis, such as contour mapping, wind flow, solar radiation, sun path and shadow, natural topography, and so on. I also want to control all of these tests parametrically in one software. This is now possible thanks to the wonderful community of Grasshopper who create various plugins.

了解場地和地形是任何建筑項目中至關重要的第一步。 站點建模可能會變得非常艱巨，昂貴或非常麻煩，通常必須使用各種軟件來掌握站點的一般知識。 我發現了使用QGIS和Rhino Grasshopper覆蓋我的大部分分析的最有效方法，例如等高線圖，風流，太陽輻射，太陽路徑和陰影，自然地形等。 我還想在一個軟件中參數控制所有這些測試。 現在這可以歸功于Grasshopper的精彩社區，他們創建了各種插件。

The second step is gathering topographical data. Satellites make data gathering very simple as the data is made freely available. An architectural project is not just defined within its site boundary. The context is very important. Maybe it’s situated in a valley or on a mountain, next to a river that is prone to flooding, or in a city where the adjacent buildings block sunlight. In terms of topography, you would generally require a larger area to perform a proper analysis and understand the surrounding context.

第二步是收集地形數據。 衛星使數據收集變得非常簡單，因為可以免費獲得數據。 建筑項目不僅在其場地邊界內定義。 上下文非常重要。 也許它位于山谷或山脈中，靠近容易泛濫的河流，或者位于鄰近建筑物阻擋陽光的城市中。 在地形方面，通常需要較大的區域來執行適當的分析并了解周圍的環境。

For this larger area, I would use the satellite gathered data. Remote sensing is increasingly becoming more accurate heading towards the true conditions. Therefore, data from satellite scanning could become a very reliable data source for almost anywhere on the earth.

對于更大的區域，我將使用衛星收集的數據。 朝向真實條件的遙感越來越準確。 因此，來自衛星掃描的數據幾乎可以成為地球上任何地方的非常可靠的數據源。

任務 (Task)

Model the site using openly available datasets

使用公開可用的數據集對網站進行建模

目的 (Objective)

My objective is to attain elevation as point values on a grid of 20 and 25 m. I want to use this to strategically position buildings in the masterplan, such that each department/building is at least 20 or 25m apart from each other. I also want to control the heights of buildings, while also maintain the view for each. Therefore, at a later stage, I will use an evolutionary solver setting multi-objective optimisation with the height of the building, elevation, and view as constrains.

我的目標是在20和25 m的網格上獲得高程作為點值。 我想用它來在總體規劃中對建筑物進行戰略性定位，以使每個部門/建筑物之間至少相距20或25m。 我還想控制建筑物的高度，同時還要保持每個建筑物的視圖。 因此，在以后的階段中，我將使用演化求解器設置多目標優化，并以建筑物的高度，立面和視圖為約束。

計劃 (Plan)

1.以DEM(數字高程模型)收集高程數據 (1. Collecting data on elevation as DEM (Digital Elevation Model))

Using Elk, OpenStreetMap, and QGIS

使用Elk，OpenStreetMap和QGIS

Step 1: Download information acquired from remote sensing (satellite scan) that are freely available from USGS as LANDSAT Image for the region. Satellite scanning is increasingly becoming more accurate depending on the site location and could hold valuable information. They can give a good indication of the surrounding topography and could be at a later stage correlated with more accurate data made from surveying.

步驟1：下載從遙感(衛星掃描)獲得的信息，該信息可以從USGS免費獲得為該地區的LANDSAT圖像。 衛星掃描越來越精確，具體取決于站點的位置，并且可以保存有價值的信息。 它們可以很好地指示周圍的地形，并且可以在以后與測量得出的更準確的數據相關聯。

USGS Earth Explorer > Select region > Download DEM

USGS Earth Explorer >選擇區域>下載DEM

Alternatively, you might come in possession of a DEM (.tiff) Survey map from a surveyor.

另外，您可能擁有測量師的DEM(.tiff)測量圖。

Step 2: Gather GIS layers such as road network, building layer, waterbody, etc, in Grasshopper using Elk plugin, & OpenStreetMap data.

步驟2：使用Elk插件和OpenStreetMap數據在Grasshopper中收集GIS圖層，例如道路網，建筑物層，水體等。

I also found an alternate method using Heron plugin in grasshopper. Heron is a wonderful plugin that imports GIS data directly onto Grasshopper without having to go through Method 1. There are plenty of tutorials online on this. The definitions are also available on Heron@food4rhino in the examples.

我還找到了在草hopper中使用Heron插件的替代方法。 Heron是一個很棒的插件，可以將GIS數據直接導入Grasshopper，而無需經過方法1。在線提供了很多教程。 示例中的Heron @ food4rhino也提供了這些定義。

I followed the Elk option.

我遵循了麋鹿的選擇。

2.處理和提取DEM的高程值 (2. Processing and Extracting Elevation values from DEM)

Digital elevations are available in various GIS compatible formats, such as .tiff, ASCII, .shp. For extracting the elevation, I downloaded the DEM raster in .tiff format. Using QGIS GRASS 3.2 (particularly, as this latest release with GRASS can only do the resample step), an open-source geographic information software, the image was processed, resampled and converted to points holding the Z value.

數字高程具有各種GIS兼容格式，例如.tiff，ASCII，.shp。 為了提取高程，我下載了.tiff格式的DEM柵格。 使用開源地理信息軟件QGIS GRASS 3.2(特別是因為該最新版本的GRASS只能執行重采樣步驟)，這是一種開源地理信息軟件，圖像經過處理，重采樣并轉換為具有Z值的點。

QGIS with GRASS > Processing Toolbox > Resample to 25 m grid > Calculate Z value > Save layer as points

帶有GRASS的QGIS>處理工具箱>重采樣到25 m網格>計算Z值>將圖層另存為點

Next step is to import this into Grasshopper.

下一步是將其導入Grasshopper。

3.蚱hopper的分析 (3. Analysis in Grasshopper)

Input for this stage is the point cloud with x,y and z values as obtained from QGIS. The output will be parametrically controlled discrete groups based on their elevation.

該階段的輸入是從QGIS獲得的具有x，y和z值的點云。 輸出將是基于其高程的參數控制的離散組。

As a rule, always parameterise items which you are not necessarily certain of, or want to maintain some flexibility.

通常，請始終對不一定要確定的項目或要保持一定靈活性的項目進行參數化。

For example, size of a room for which area is 10 sq.m but length and breadth are not definite. You would then make length and breadth a function of the area controlled by sliders.

例如，面積為10平方米但長度和寬度不確定的房間大小。 然后，您將使長度和寬度成為由滑塊控制的區域的函數。

I want to be able to control the grouping of points based on their elevation in order to parametrically discretise the levels. Parameterising the levels enables control over the criteria for selection of points.

我希望能夠基于其高程來控制點的分組，以便參數化水平。 通過對級別進行參數化，可以控制選擇點的標準。

4.輸出 (4. Output)

With this I have;

我有了這個

1. Organised the levels as controllable parametric features.
   將級別組織為可控制的參數特征。
2. Parameterised grouping levels into bands at increments of 5 m
   參數化的分組級別按5 m的增量分成頻段
3. Identified low and high-level regions across the site to plan for grazing and farmland as required from my project brief
   根據我的項目簡介中的要求，確定了站點的低層和高層區域，以計劃放牧和耕地
4. Set the stage for running an evolutionary solver to generate options at a later stage
   設置運行演化求解器以在以后生成選項的階段

下一步 (Next Step)

Package 2: Massing and zoning

套餐2：批量和分區

Hello and thanks for checking out my post! Feel free to shoot any questions you may have as comments. Also, get in touch with me on LinkedIn if you would like any help.

您好，感謝您檢查我的帖子！ 隨意拍攝您可能有任何疑問的問題。 另外， 如果您需要任何幫助，請 通過 LinkedIn 與我聯系 。

I am an architect (COA) and tech enthusiast from London. I am interested in the built environment and leveraging data sciences for architecture broadly around design, performance, and insights. I work on various topics from time to time such as generative design, spatial analytics, and energy and environmental studies. I am a Project Manager (AEC) at a biotech innovation company, developing a large-scale sustainable project in North Africa.

我是倫敦的一名建筑師(COA)和技術愛好者。 我對構建環境以及將數據科學廣泛應用于設計，性能和洞察力的架構感興趣。 我不時從事各種主題的工作，例如生成設計，空間分析以及能源和環境研究。 我是一家生物技術創新公司的項目經理(AEC)，正在開發北非的大型可持續項目。