From sunrise to sunset: Exploring landscape preference through global reactions to ephemeral events captured in georeferenced social media.

Events profoundly influence human-environment interactions. Through repetition, some events manifest and amplify collective behavioral traits, which significantly affects landscapes and their use, meaning, and value. However, the majority of research on reaction to events focuses on case studies, based on spatial subsets of data. This makes it difficult to put observations into context and to isolate sources of noise or bias found in data. As a result, inclusion of perceived aesthetic values, for example, in cultural ecosystem services, as a means to protect and develop landscapes, remains problematic. In this work, we focus on human behavior worldwide by exploring global reactions to sunset and sunrise using two datasets collected from Instagram and Flickr. By focusing on the consistency and reproducibility of results across these datasets, our goal is to contribute to the development of more robust methods for identifying landscape preference using geo-social media data, while also exploring motivations for photographing these particular events. Based on a four facet context model, reactions to sunset and sunrise are explored for Where, Who, What, and When. We further compare reactions across different groups, with the aim of quantifying differences in behavior and information spread. Our results suggest that a balanced assessment of landscape preference across different regions and datasets is possible, which strengthens representativity and exploring the How and Why in particular event contexts. The process of analysis is fully documented, allowing transparent replication and adoption to other events or datasets.

Digital anthropometric volumes: Toward the development and validation of a universal software.

PURPOSE: Anthropometry is a method for quantifying body size and shape often used to derive body composition and health risk prediction models. Recent technology advancements led to development of three-dimensional (3D) optical scanners that can overcome most of the limitations associated with manual anthropometric data collection. However, each of the currently available devices offers proprietary measurements that do not match conventional anthropometric definitions. The aim of the current study was to develop and then evaluate the precision and accuracy of new "universal" 3D optical analysis software that calculates digital anthropometric volumes using identical standard landmarks across scanners. METHODS: Dual-energy x-ray absorptiometry (DXA) and air displacement plethysmography (ADP) total body and regional volume and fat mass reference measurements and 3D optical scans from two proprietary devices were collected from 356 participants to evaluate the robustness of total body and regional volume and fat mass measurements calculated by the developed software. Linear regression modeling with threefold cross validation was used to evaluate total body and regional fat masses from 3D scans. RESULTS: Total body and regional volumes measured by DXA and ADP had strong associations with corresponding estimates from the commercial 3D optical scanners coupled with the universal software (e.g., R2  = 0.98 for Styku and R2  = 1.00 for SS20, for both DXA and ADP comparisons). Regional body volumes also had strong correlation between DXA and the 3DO scanners (e.g., for arm, leg and trunk, respective R2 s of 0.75, 0.86, and 0.97 for Styku and 0.79, 0.89, and 0.98 for SS20). Similarly, there were strong associations between DXA- measured total body and regional fat mass and 3D optical estimates calculated by the universal software (e.g., for total body, arm, leg and trunk, respective R2 s of 0.86, 0.72, 0.77, and 0.88 for Styku and 0.84, 0.76, 0.78, and 0.85 for SS20). Absolute differences in volumes and fat mass between the reference methods and the universal software values revealed underlying proprietary scanner differences that can be improved when designing future devices. CONCLUSIONS: The current study suggests that, when compared against values calculated using DXA and ADP, the universal software was able to measure total and regional body volumes reliably from scans obtained by two different scanners. The universal software, with future refinements, combined with potential optical scanner design improvements, creates new opportunities for developing large multicenter anthropometric databases with uniformly defined body dimensions that can be used for modeling health risks. CLINICAL TRIAL REGISTRATION ID: Shape Up! Adults Study, NCT0363785.

Digital anthropometry for body circumference measurements: Toward the development of universal three-dimensional optical system analysis software.

BACKGROUND/OBJECTIVE: Digital anthropometric (DA) assessments are increasingly being administered with three-dimensional (3D) optical devices in clinical settings that manage patients with obesity and related metabolic disorders. However, anatomic measurement sites are not standardized across manufacturers, precluding use of published reference values and pooling of data across research centers. SUBJECTS/METHODS: This study aimed to develop universal 3D analysis software by applying novel programming strategies capable of producing device-independent DA estimates that agree with conventional anthropometric (CA) measurements made at well-defined anatomic sites. A series of technical issues related to proprietary methods of 3D geometrical reconstruction and image analysis were addressed in developing major software components. To evaluate software accuracy, comparisons were made to CA circumference measurements made with a flexible tape at eleven standard anatomic sites in up to 35 adults scanned with three different commercial 3D optical devices. RESULTS: Overall, group mean CA and DA values across the three systems were in good agreement, with ∼2 cm systematic differences; CA and DA estimates were highly correlated (all p-values <0.01); root-mean square errors were low (0.51-3.27 cm); and CA-DA bias tended to be small, but significant depending on anatomic site and device. CONCLUSIONS: Availability of this software, with future refinements, has the potential to facilitate clinical applications and creation of large pooled uniform anthropometric databases.

Hole Filling in 3D Scans for Digital Anthropometric Applications.

Anthropometric measurements have been used to assess an individual's body composition, disease risk, and nutritional status. Three-dimensional (3D) optical devices can rapidly acquire body surface scans in the form of a triangular mesh which can then be used to obtain anthropometric measurements such as body volume, limb lengths, and circumferences; however, the meshes provided by some scanners may include missing data patches known as holes. These need to be repaired in order to obtain correct landmark detection and automatic calculation of anthropometric measurements-especially body volume. In this study, we present ScReAM (Scan Reconstruction for Anthropometric Measurements) which is a fully automated geometrical 3D reconstruction approach to find and fill these holes. We compare ScReAM with Alias and MeshFix which are well-known software used for triangular meshing. Evaluations are derived from a sample size of 47 subjects that were scanned by two different 3D optical scanners. Our results validate the accuracy of ScReAM for reconstructing a mesh for volume calculation.