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Abstract Indoor spaces are essential to most humans' lives. Furthermore, in many cases, buildings are shared indoor environments that contain diverse people and resources. Spatial patterns of use are important but under‐examined aspects of human‐building interactions. This study leverages perspectives from human‐environment geography and mechanical engineering to examine spatial patterns of use within a network of shared indoor spaces in an academic building at a research university in the United States. Here we ask: (1) What spaces and resources do building users value? and (2) How are values associated with observed measures of use? We hypothesise that spatial patterns of use follow an ideal free distribution (IFD), a common ecological model of resource use. To test this, we define measures of value and use derived from mixed qualitative (n = 50) and survey‐based social data (n = 196) and data from a building‐based system of accelerometers. Our analyses provide some support for the IFD hypothesis. We discuss the implications of this finding and potential new avenues for geographic research in shared indoor environments. 

Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as “flickering” in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy.