---
id: 2025-12-17T05:39:00-0500
title: 2025-12-17 05:39:??
tags:
  - topic/estimating
daily: "[[2025-12-17]]"
---
# 2025-12-17 05:39:??

%% [[statistical-modeling-for-construction-estimating]] %%

One aspect of estimating that I find most interesting,
but that is criminally understudied,
is the effect of building dimensions
(footprint shape, floor area, stories, height)
on total cost.

Unfortunately, lack of interest in the subject extends beyond estimating.
Discourse on spatial data seems to fall into one of two bins:
* civil engineering
* n-dimensional mathematics[^1]
neither are readily applicable to building construction.

[^1]: worse still, the "space" studied in such disciplines is
    [vector space](https://en.wikipedia.org/wiki/Vector_space)
    where "distance" is a measure of similarity
    and physical geometry is rarely considered.

Of the two, pure math would be be preferred---
being generally more rigorous---
but the first bin far outweighs the second.
See the difference in content from
[geostatistics](https://en.wikipedia.org/wiki/Geostatistics)
to the conceivably far more broad
[spatial statistics](https://en.wikipedia.org/wiki/Spatial_statistics).

> [!quote] [Geographic data and information](https://en.wikipedia.org/wiki/Geographic_data_and_information)
> **Spatial data** or **spatial information** is broader class of data
> whose geometry is relevant
> but it is not necessarily [georeferenced](https://en.wikipedia.org/wiki/Georeferenced "Georeferenced"),
> such as in computer-aided design (CAD),
> see [geometric modeling](https://en.wikipedia.org/wiki/Geometric_modeling "Geometric modeling").
