43 lines
1.6 KiB
Markdown
43 lines
1.6 KiB
Markdown
---
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id: 2025-12-17T05:39:00-0500
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title: 2025-12-17 05:39:??
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tags:
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- topic/estimating
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daily: "[[2025-12-17]]"
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---
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# 2025-12-17 05:39:??
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%% [[statistical-modeling-for-construction-estimating]] %%
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One aspect of [[construction-estimating]] that I find most interesting,
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but that is criminally understudied,
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is the effect of building dimensions
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(footprint shape, floor area, stories, height)
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on total cost.
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Unfortunately, lack of interest in the subject extends beyond estimating.
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Discourse on spatial data seems to fall into one of two bins:
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* civil engineering
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* n-dimensional mathematics[^1]
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neither are readily applicable to building construction.
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[^1]: worse still, the "space" studied in such disciplines is
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[vector space](https://en.wikipedia.org/wiki/Vector_space)
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where "distance" is a measure of similarity
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and physical geometry is rarely considered.
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Of the two, pure math would be be preferred---
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being generally more rigorous---
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but the first bin far outweighs the second.
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See the difference in content from
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[geostatistics](https://en.wikipedia.org/wiki/Geostatistics)
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to the conceivably far more broad
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[spatial statistics](https://en.wikipedia.org/wiki/Spatial_statistics).
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> [!quote] [Geographic data and information](https://en.wikipedia.org/wiki/Geographic_data_and_information)
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> **Spatial data** or **spatial information** is broader class of data
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> whose geometry is relevant
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> but it is not necessarily [georeferenced](https://en.wikipedia.org/wiki/Georeferenced "Georeferenced"),
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> such as in computer-aided design (CAD),
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> see [geometric modeling](https://en.wikipedia.org/wiki/Geometric_modeling "Geometric modeling").
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