<story><title>Largest open dataset of apartment models ever got published</title><url>https://zenodo.org/record/7070952</url></story><parent_chain></parent_chain><comment><author>didgetmaster</author><text>Interesting data set. I am building a new kind of data analysis tool (&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.Didgets.com&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.Didgets.com&lt;/a&gt;) so I am always looking for good open data sets to download, import into my tool, and see what the data shows and to test out my tool.&lt;p&gt;I downloaded both CSV files (geometry and simulations) and built a couple relational tables with them in a few minutes. I am confused by a few things. There are 42,207 unique values in the &amp;#x27;apartment_id&amp;#x27; column. The most common one is d41d8cd98f00b204e9800998ecf8427e which is referenced 1451 times. At first I thought that it might actually be some kind of &amp;#x27;plan_id&amp;#x27; where the same plan was used to build multiple apartments (this id is associated with 13 different &amp;#x27;building_id&amp;#x27; values) but drilling down to each one reveals some very different features.&lt;p&gt;It is certainly possible that the same plan could be used with slight variations (e.g. one has a tub in the bathroom while another had a shower installed), but some of the features were very unique. For example there are 26 different KITCHEN areas associated with the id, but only 21 LIVING_DINING areas.&lt;p&gt;My tool is great for finding and fixing anomalies in data sets if they exist. This one is a bit confusing about what some elements mean and the site doesn&amp;#x27;t explain them very well.&lt;p&gt;If the same plan is being used across multiple buildings, it might be interesting to see how the amount of light entering the building differs based on if the same plan was used to build an apartment on the north side of a building vs the south side.</text></comment>
<story><title>Largest open dataset of apartment models ever got published</title><url>https://zenodo.org/record/7070952</url></story><parent_chain></parent_chain><comment><author>schnevets</author><text>I think the improvements and increased acceptance of prefabricated construction and machine learning can make for an intriguing combination. I am by no means a construction specialist, but if you distill ML to new innovation from historical data sets, architecture certainly has untapped potential.&lt;p&gt;Just imagine being able to input a geolocation and automatically receiving insight about construction that optimizes for usable space, energy efficiency, or even the prospective homeowner&amp;#x27;s lifestyle (an AI that recommends different layout options for a family of 5, lifelong bachelor, and non-family roommates on identical quarter-acre plots)&lt;p&gt;On a slightly more disruptive angle, imagine an AI that could understand a municipality&amp;#x27;s building code and optimize the space while complying with the literal requirements. Your town has banned finished attics without two methods of egress? Here is an ideal renovation that will provide that necessary balcony while maintaining budget (and here are 4 other buildings in the town that were approved with the same design).</text></comment>