Interesting data set. I am building a new kind of data analysis tool (https://www.Didgets.com) 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.
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 'apartment_id' column. The most common one is d41d8cd98f00b204e9800998ecf8427e which is referenced 1451 times. At first I thought that it might actually be some kind of 'plan_id' where the same plan was used to build multiple apartments (this id is associated with 13 different 'building_id' values) but drilling down to each one reveals some very different features.
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.
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't explain them very well.
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.
I think d41d8cd98f00b204e9800998ecf8427e is the md5 sum of "nothing", e.g. md5 reverse will get you a zero-length stream of characters.
(granted this is entirely without looking at the data) but my guess is that they MD5 hashed whatever was in that apartment_id column and if it was empty it spat out d41d8cd98f00b204e9800998ecf8427e
Suggestion: add “is this null”/“is this common?” to your analysis tool. It might take determining that the hashing method is for each dataset “column”, but this kind of trap is everywhere and your users would probably be delighted when they see that’s already identified.
co-author here, thanks a lot for your feedback! We will make sure to clarify on the apartment_id field in the next version. The apartment id is actually the unique id for an apartment in a single site (note: it happens an apartment spans multiple floors) - but it is not necessarily unique over the whole dataset.
We will also include a plan_id field that allows to identify which floors of a building are repeated (the apartment_ids differ already though).
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.
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'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)
On a slightly more disruptive angle, imagine an AI that could understand a municipality'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).
I'm pretty sure I've seen urban planning software that use building codes / parametric parameters to to do this. But architecture / construction has poor history of adopting leading edge tech (constructionphysics.substack.com has great dives into history).
One thing i'm excited for is AI generating ornamentation combined with additive/subtractive manufacturing and we might finally get relatively budget revival of a bunch of more craft based aethetics. Even though we can (relatively) cheaply create detailed geometry now, it's still cost prohibitive to design said details.
As someone who knows Matthias, I can vouch for the engineering effort behind Archilyse's work. I'll admit I was a tiny bit jealous when I first watched their pitch!
They have identified an area where they can clearly add significant value and the analysis their software runs on a dwelling is robustly built and incredibly thorough. I wish them well with their expansion beyond Switzerland!
Probably not the kind of comment that’s usually left on HN but whatever, please tell Matthias (and if he could tell his team) that they went CRAZY with this dataset. As a data nerd I d*mn near started BARKING. Can’t stress enough how HARD they went, I hope their bills are ALWAYS paid, I hope they catch EVERY green light they need to, I hope their pasta dishes are NEVER watery. You get the drift by now. Matthias and gang if you’re reading this thanks for all your hard work. BEYONCÉS of data FORREAL.
I'm an architect (of buildings) and mostly lurk here but this is very interesting and had to comment - many designers don't like the idea of quantifying design value through measurable data, but it's most definitely the future. The design time and cost savings that can be had from using datasets or software like Archilyse's will continue to grow, while also (hopefully) ensuring a higher baseline design value of buildings. I'm personally just beginning my journey of coding/programming because of exactly this sort of thing (among other reasons).
The way we shape our maze determines our behavior which one could use to design personality. For example, if the kitchen is on the road side you wont see your neighbors as often. Or 2 bathrooms give your grumpy morning mind some alone time.
In a way we are different people in different rooms. The transitions could be interesting to explore. For example toilet > kitchen is not done or even illegal. A toilet in the garden on the other hand seems fun.
Maybe hybrid rooms build an interesting character. Say a bath in the middle of the living room next to the fire place. A kitchen library also seems fascinating.
One question has been bothering me: why are rooms square?
Some say that it's more economical to build this way, and that it causes less problems with aligning furniture to the wall.
But that doesn't explain billionaire houses. They certainly love spending money on them, yet despite all kinds of extravaganza, the rooms are also mostly square.
So i think it's something deeper, I think it's just too suspicious that across most modern cultures rooms are square. My theory is that it's related to the fact that we have 4 sides, so it's kind of symmetrical that we prefer to live in 4-sided things too.
High-end houses aren’t square in the same way that a normal house is square: often they’ll be open plan, where rooms join together through shared spaces that are all sorts of shapes. A better description of the consistency amongst property is “straight” walls.
If you spend much time looking at high-end real estate, you’ll encounter much more than just the standard square rooms you would see in the average house, but ultimately, they’re still square(ish) because straight walls are convenient and practical.
pg wrote about this a couple years back. big-boxy architecture of american suburbia vs all the diverse architectural styles in europe/asia etc.
in many parts of costarica, panama & especially in upscale indian houses, the house looks more like an art project. i’ve been to houses with what would be called gaudy colors - blood red, bright yellow, parrot green walls. non-square rooms, oval spaces with arches and round pillars inside the house. in those places, people use their art skills not just on canvas but also in living spaces. it might not look conventionally pretty, but everybody gets a say - like in the kitchen, my mom had holes of different shapes in the wall, so bottles of different sizes fit into specific holes. no actual shelves! i wanted a table so i got a table built of concrete and cement! just put a tablecloth on top. no need to buy a table, table was an actual part of the house itself. i’ve been to bathrooms with a raised mound of cement to hold pots of water. you carry water from well and you can place the pot with water on the raised mound without bending down all the way to the floor. very thoughtful ideas.
Furniture is pretty difficult to make for for rooms with odd wall configurations. You have to custom design every piece and wind up losing a bunch of space in a dresser or closet in most cases.
This is a nitpick that's not related to the article itself, but I found that sorting by most viewed, the asc/desc dropdown has an effect from what I expected.
The intersection of architecture and 3d design with ML is an intersect I haven't seen much work in, but it would be fascinating to see what comes from this
[+] [-] didgetmaster|3 years ago|reply
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 'apartment_id' column. The most common one is d41d8cd98f00b204e9800998ecf8427e which is referenced 1451 times. At first I thought that it might actually be some kind of 'plan_id' where the same plan was used to build multiple apartments (this id is associated with 13 different 'building_id' values) but drilling down to each one reveals some very different features.
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.
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't explain them very well.
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.
[+] [-] screature2|3 years ago|reply
(granted this is entirely without looking at the data) but my guess is that they MD5 hashed whatever was in that apartment_id column and if it was empty it spat out d41d8cd98f00b204e9800998ecf8427e
[+] [-] joshspankit|3 years ago|reply
[+] [-] mfranzen|3 years ago|reply
We will also include a plan_id field that allows to identify which floors of a building are repeated (the apartment_ids differ already though).
[+] [-] schnevets|3 years ago|reply
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'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)
On a slightly more disruptive angle, imagine an AI that could understand a municipality'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).
[+] [-] hooverd|3 years ago|reply
[+] [-] dirtyid|3 years ago|reply
I'm pretty sure I've seen urban planning software that use building codes / parametric parameters to to do this. But architecture / construction has poor history of adopting leading edge tech (constructionphysics.substack.com has great dives into history).
One thing i'm excited for is AI generating ornamentation combined with additive/subtractive manufacturing and we might finally get relatively budget revival of a bunch of more craft based aethetics. Even though we can (relatively) cheaply create detailed geometry now, it's still cost prohibitive to design said details.
[+] [-] Oarch|3 years ago|reply
They have identified an area where they can clearly add significant value and the analysis their software runs on a dwelling is robustly built and incredibly thorough. I wish them well with their expansion beyond Switzerland!
[+] [-] seleenz|3 years ago|reply
[+] [-] cameron4|3 years ago|reply
[+] [-] rubyfan|3 years ago|reply
[+] [-] 6510|3 years ago|reply
In a way we are different people in different rooms. The transitions could be interesting to explore. For example toilet > kitchen is not done or even illegal. A toilet in the garden on the other hand seems fun.
Maybe hybrid rooms build an interesting character. Say a bath in the middle of the living room next to the fire place. A kitchen library also seems fascinating.
[+] [-] 323|3 years ago|reply
Some say that it's more economical to build this way, and that it causes less problems with aligning furniture to the wall.
But that doesn't explain billionaire houses. They certainly love spending money on them, yet despite all kinds of extravaganza, the rooms are also mostly square.
So i think it's something deeper, I think it's just too suspicious that across most modern cultures rooms are square. My theory is that it's related to the fact that we have 4 sides, so it's kind of symmetrical that we prefer to live in 4-sided things too.
[+] [-] pottertheotter|3 years ago|reply
[+] [-] phphphphp|3 years ago|reply
If you spend much time looking at high-end real estate, you’ll encounter much more than just the standard square rooms you would see in the average house, but ultimately, they’re still square(ish) because straight walls are convenient and practical.
[+] [-] dxbydt|3 years ago|reply
[+] [-] alex_young|3 years ago|reply
[+] [-] stephc_int13|3 years ago|reply
Round and triangular rooms are not practical for obvious reasons.
Square or close to square is thus optimal.
[+] [-] dwater|3 years ago|reply
[+] [-] legulere|3 years ago|reply
[+] [-] sieabahlpark|3 years ago|reply
[deleted]
[+] [-] tiagod|3 years ago|reply
[+] [-] rainbringer2000|3 years ago|reply
[+] [-] dirkhe|3 years ago|reply