ASU Regents' Professors Open Access Works
The title “Regents’ Professor” is the highest faculty honor awarded at Arizona State University. It is conferred on ASU faculty who have made pioneering contributions in their areas of expertise, who have achieved a sustained level of distinction, and who enjoy national and international recognition for these accomplishments. This collection contains primarily open access works by ASU Regents' Professors.
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- Creators: School of Geographical Sciences and Urban Planning
- Creators: Buman, Matthew
Description
Precipitation and temperature enact variable influences on vegetation, impacting the type and condition of land cover, as well as the assessment of change over broad landscapes. Separating the influence of vegetative variability independent and discrete land cover change remains a major challenge to landscape change assessments. The heterogeneous Lerma-Chapala-Santiago watershed of central Mexico exemplifies both natural and anthropogenic forces enacting variability and change on the landscape. This study employed a time series of Enhanced Vegetation Index (EVI) composites from the Moderate Resolution Imaging Spectoradiometer (MODIS) for 2001–2007 and per-pixel multiple linear regressions in order to model changes in EVI as a function of precipitation, temperature, and elevation. Over the seven-year period, 59.1% of the variability in EVI was explained by variability in the independent variables, with highest model performance among changing and heterogeneous land cover types, while intact forest cover demonstrated the greatest resistance to changes in temperature and precipitation. Model results were compared to an independent change uncertainty assessment, and selected regional samples of change confusion and natural variability give insight to common problems afflicting land change analyses.
ContributorsChristman, Zachary (Author) / Rogan, John (Author) / Eastman, J. Ronald (Author) / Turner II, B. L. (Author) / College of Liberal Arts and Sciences (Contributor) / School of Geographical Sciences and Urban Planning (Contributor)
Created2016-06-07
Description
Aim
The aim of this study was to investigate the potential associations of reallocating 30 minutes sedentary time in long bouts (>60 min) to sedentary time in non-bouts, light intensity physical activity (LPA) and moderate- to vigorous physical activity (MVPA) with cardiometabolic risk factors in a population diagnosed with prediabetes or type 2 diabetes.
Methods
Participants diagnosed with prediabetes and type 2 diabetes (n = 124, 50% men, mean [SD] age = 63.8 [7.5] years) were recruited to the physical activity intervention Sophia Step Study. For this study baseline data was used with a cross-sectional design. Time spent in sedentary behaviors in bouts (>60 min) and non-bouts (accrued in <60 min bouts) and physical activity was measured using the ActiGraph GT1M. Associations of reallocating bouted sedentary time to non-bouted sedentary time, LPA and MVPA with cardiometabolic risk factors were examined using an isotemporal substitution framework with linear regression models.
Results
Reallocating 30 minutes sedentary time in bouts to MVPA was associated with lower waist circumference (b = -4.30 95% CI:-7.23, -1.38 cm), lower BMI (b = -1.46 95% CI:-2.60, -0.33 kg/m2) and higher HDL cholesterol levels (b = 0.11 95% CI: 0.02, 0.21 kg/m[superscript 2]. Similar associations were seen for reallocation of sedentary time in non-bouts to MVPA. Reallocating sedentary time in bouts to LPA was associated only with lower waist circumference.
Conclusion
Reallocation of sedentary time in bouts as well as non-bouts to MVPA, but not to LPA, was beneficially associated with waist circumference, BMI and HDL cholesterol in individuals with prediabetes and type 2 diabetes. The results of this study confirm the importance of reallocation sedentary time to MVPA.
The aim of this study was to investigate the potential associations of reallocating 30 minutes sedentary time in long bouts (>60 min) to sedentary time in non-bouts, light intensity physical activity (LPA) and moderate- to vigorous physical activity (MVPA) with cardiometabolic risk factors in a population diagnosed with prediabetes or type 2 diabetes.
Methods
Participants diagnosed with prediabetes and type 2 diabetes (n = 124, 50% men, mean [SD] age = 63.8 [7.5] years) were recruited to the physical activity intervention Sophia Step Study. For this study baseline data was used with a cross-sectional design. Time spent in sedentary behaviors in bouts (>60 min) and non-bouts (accrued in <60 min bouts) and physical activity was measured using the ActiGraph GT1M. Associations of reallocating bouted sedentary time to non-bouted sedentary time, LPA and MVPA with cardiometabolic risk factors were examined using an isotemporal substitution framework with linear regression models.
Results
Reallocating 30 minutes sedentary time in bouts to MVPA was associated with lower waist circumference (b = -4.30 95% CI:-7.23, -1.38 cm), lower BMI (b = -1.46 95% CI:-2.60, -0.33 kg/m2) and higher HDL cholesterol levels (b = 0.11 95% CI: 0.02, 0.21 kg/m[superscript 2]. Similar associations were seen for reallocation of sedentary time in non-bouts to MVPA. Reallocating sedentary time in bouts to LPA was associated only with lower waist circumference.
Conclusion
Reallocation of sedentary time in bouts as well as non-bouts to MVPA, but not to LPA, was beneficially associated with waist circumference, BMI and HDL cholesterol in individuals with prediabetes and type 2 diabetes. The results of this study confirm the importance of reallocation sedentary time to MVPA.
ContributorsRossen, Jenny (Author) / Buman, Matthew (Author) / Johansson, Unn-Britt (Author) / Yngve, Agneta (Author) / Ainsworth, Barbara (Author) / Brismar, Kerstin (Author) / Hagstromer, Maria (Author) / College of Health Solutions (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2017-07-28
Description
Fire is one of the earliest and most common tools used by humans to modify the earth surface. Landscapes in the Yucatán Peninsula are composed of a mosaic of old growth subtropical forest, secondary vegetation, grasslands, and agricultural land that represent a well-documented example of anthropogenic intervention, much of which involves the use of fire. This research characterizes land use systems and land cover changes in the Yucatán during the 2000–2010 time period. We used an active fire remotely sensed data time series from the Moderate Resolution Imaging Spectroradiometer (MODIS), in combination with forest loss, and anthrome map sources to (1) establish the association between fire and land use change in the region; and (2) explore links between the spatial and temporal patterns of fire and specific types of land use practices, including within- and between-anthromes variability. A spatial multinomial logit model was constructed using fire, landscape configuration, and a set of commonly used control variables to estimate forest persistence, non-forest persistence, and change. Cross-tabulations and descriptive statistics were used to explore the relationships between fire occurrence, location, and timing with respect to the geography of land use. We also compared fire frequencies within and between anthrome groups using a negative binomial model and Tukey pairwise comparisons. Results show that fire data broadly reproduce the geography and timing of anthropogenic land change. Findings indicate that fire and landscape configuration is useful in explaining forest change and non-forest persistence, especially in fragmented (mosaicked) landscapes. Absence of fire occurrence is related usefully to the persistence of spatially continuous core areas of older growth forest. Fire has a positive relationship with forest to non-forest change and a negative relationship with forest persistence. Fire is also a good indicator to distinguish between anthrome groups (e.g., croplands and villages). Our study suggests that active fire data series are a reasonable proxy for anthropogenic land persistence/change in the context of the Yucatán and are useful to differentiate quantitatively and qualitatively between and within anthromes.
ContributorsMillones, Marco (Author) / Rogan, John (Author) / Turner II, B. L. (Author) / Parmentier, Benoit (Author) / Clary Harris, Robert (Author) / Griffith, Daniel A. (Author) / College of Liberal Arts and Sciences (Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor) / School of Sustainability (Contributor)
Created2017-09-12
Description
A species’ response to climate change depends on the interaction of biotic and abiotic factors that define future habitat suitability and species’ ability to migrate or adapt. The interactive effects of processes such as fire, dispersal, and predation have not been thoroughly addressed in the climate change literature. Our objective was to examine how life history traits, short-term global change perturbations, and long-term climate change interact to affect the likely persistence of an oak species - Quercus engelmannii (Engelmann oak). Specifically, we combined dynamic species distribution models, which predict suitable habitat, with stochastic, stage-based metapopulation models, which project population trajectories, to evaluate the effects of three global change factors – climate change, land use change, and altered fire frequency – emphasizing the roles of dispersal and seed predation. Our model predicted dramatic reduction in Q. engelmannii abundance, especially under drier climates and increased fire frequency. When masting lowers seed predation rates, decreased masting frequency leads to large abundance decreases. Current rates of dispersal are not likely to prevent these effects, although increased dispersal could mitigate population declines. The results suggest that habitat suitability predictions by themselves may under-estimate the impact of climate change for other species and locations.
ContributorsConlisk, Erin (Author) / Lawson, Dawn (Author) / Syphard, Alexandra D. (Author) / Franklin, Janet (Author) / Flint, Lorraine (Author) / Flint, Alan (Author) / Regan, Helen M. (Author) / College of Liberal Arts and Sciences (Contributor) / School of Geographical Sciences and Urban Planning (Contributor)
Created2012-05-18
Description
Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI) for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations.
ContributorsLi, Wenwen (Author) / Li, Linna (Author) / Goodchild, Michael F. (Author) / Anselin, Luc (Author) / College of Liberal Arts and Sciences (Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / Computational Spatial Science (Contributor)
Created2013-05-21
Description
Evolving Earth observation and change detection techniques enable the automatic identification of Land Use and Land Cover Change (LULCC) over a large extent from massive amounts of remote sensing data. It at the same time poses a major challenge in effective organization, representation and modeling of such information. This study proposes and implements an integrated computational framework to support the modeling, semantic and spatial reasoning of change information with regard to space, time and topology. We first proposed a conceptual model to formally represent the spatiotemporal variation of change data, which is essential knowledge to support various environmental and social studies, such as deforestation and urbanization studies. Then, a spatial ontology was created to encode these semantic spatiotemporal data in a machine-understandable format. Based on the knowledge defined in the ontology and related reasoning rules, a semantic platform was developed to support the semantic query and change trajectory reasoning of areas with LULCC. This semantic platform is innovative, as it integrates semantic and spatial reasoning into a coherent computational and operational software framework to support automated semantic analysis of time series data that can go beyond LULC datasets. In addition, this system scales well as the amount of data increases, validated by a number of experimental results. This work contributes significantly to both the geospatial Semantic Web and GIScience communities in terms of the establishment of the (web-based) semantic platform for collaborative question answering and decision-making.
ContributorsVadjunec, Jacqueline M. (Author) / Radel, Claudia (Author) / Turner II, B. L. (Author) / College of Liberal Arts and Sciences (Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor) / School of Sustainability (Contributor)
Created2016-10-25
Description
Aim
To establish a chronology for late Quaternary avian extinction, extirpation and persistence in the Bahamas, thereby testing the relative roles of climate change and human impact as causes of extinction.
Location
Great Abaco Island (Abaco), Bahamas, West Indies.
Methods
We analysed the resident bird community as sampled by Pleistocene (> 11.7 ka) and Holocene (< 11.7 ka) fossils. Each species was classified as extinct (lost globally), extirpated (gone from Abaco but persists elsewhere), or extant (still resident on Abaco). We compared patterns of extinction, extirpation and persistence to independent estimates of climate and sea level for glacial (late Pleistocene) and interglacial (Holocene) times.
Results
Of 45 bird species identified in Pleistocene fossils, 25 (56%) no longer occur on Abaco (21 extirpated, 4 extinct). Of 37 species recorded in Holocene deposits, 15 (14 extirpated, 1 extinct; total 41%) no longer exist on Abaco. Of the 30 extant species, 12 were recovered as both Pleistocene and Holocene fossils, as were 9 of the 30 extirpated or extinct species. Most of the extinct or extirpated species that were only recorded from Pleistocene contexts are characteristic of open habitats (pine woodlands or grasslands); several of the extirpated species are currently found only where winters are cooler than in the modern or Pleistocene Bahamas. In contrast, most of the extinct or extirpated species recorded from Holocene contexts are habitat generalists.
Main conclusions
The fossil evidence suggests two main times of late Quaternary avian extirpation and extinction in the Bahamas. The first was during the Pleistocene–Holocene transition (PHT; 15–9 ka) and was fuelled by climate change and associated changes in sea level and island area. The second took place during the late Holocene (< 4 ka, perhaps primarily < 1 ka) and can be attributed to human impact. Although some species lost during the PHT are currently found where climates are cooler and drier than in the Bahamas today, a taxonomically and ecologically diverse set of species persisted through that major climate change but did not survive the past millennium of human presence.
ContributorsSteadman, David W. (Author) / Franklin, Janet (Author) / College of Liberal Arts and Sciences (Contributor) / School of Geographical Sciences and Urban Planning (Contributor)
Created2015-03-01
Description
Estimating and projecting population trends using population viability analysis (PVA) are central to identifying species at risk of extinction and for informing conservation management strategies. Models for PVA generally fall within two categories, scalar (count-based) or matrix (demographic). Model structure, process error, measurement error, and time series length all have known impacts in population risk assessments, but their combined impact has not been thoroughly investigated. We tested the ability of scalar and matrix PVA models to predict percent decline over a ten-year interval, selected to coincide with the IUCN Red List criterion A. 3, using data simulated for a hypothetical, short-lived organism with a simple life-history and for a threatened snail, Tasmaphena lamproides. PVA performance was assessed across different time series lengths, population growth rates, and levels of process and measurement error. We found that the magnitude of effects of measurement error, process error, and time series length, and interactions between these, depended on context. We found that high process and measurement error reduced the reliability of both models in predicted percent decline. Both sources of error contributed strongly to biased predictions, with process error tending to contribute to the spread of predictions more than measurement error. Increasing time series length improved precision and reduced bias of predicted population trends, but gains substantially diminished for time series lengths greater than 10-15 years. The simple parameterization scheme we employed contributed strongly to bias in matrix model predictions when both process and measurement error were high, causing scalar models to exhibit similar or greater precision and lower bias than matrix models. Our study provides evidence that, for short-lived species with structured but simple life histories, short time series and simple models can be sufficient for reasonably reliable conservation decision-making, and may be preferable for population projections when unbiased estimates of vital rates cannot be obtained.
ContributorsRueda-Cediel, Pamela (Author) / Anderson, Kurt E. (Author) / Regan, Tracey J. (Author) / Franklin, Janet (Author) / Regan, Helen M. (Author) / College of Liberal Arts and Sciences (Contributor) / School of Geographical Sciences and Urban Planning (Contributor)
Created2015-07-15
Description
This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical user interface, command line or embedded in a computational notebook), open spatial analytics middleware, and web, cloud and distributed open geospatial analytics for decision support. A common thread throughout the discussion is the emphasis on openness, interoperability, and provenance management in a scientific workflow. The code base of the PySAL library provides the common computing framework underlying all delivery mechanisms.
ContributorsRey, Sergio (Author) / Anselin, Luc (Author) / Li, Xun (Author) / Pahle, Robert (Author) / Laura, Jason (Author) / Li, Wenwen (Author) / Koschinsky, Julia (Author) / College of Liberal Arts and Sciences (Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / Computational Spatial Science (Contributor)
Created2015-06-01
Description
Background
The purpose of this study is to determine the feasibility of three widely used wearable sensors in research settings for 24 h monitoring of sleep, sedentary, and active behaviors in middle-aged women.
Methods
Participants were 21 inactive, overweight (M Body Mass Index (BMI) = 29.27 ± 7.43) women, 30 to 64 years (M = 45.31 ± 9.67). Women were instructed to wear each sensor on the non-dominant hip (ActiGraph GT3X+), wrist (GENEActiv), or upper arm (BodyMedia SenseWear Mini) for 24 h/day and record daily wake and bed times for one week over the course of three consecutive weeks. Women received feedback about their daily physical activity and sleep behaviors. Feasibility (i.e., acceptability and demand) was measured using surveys, interviews, and wear time.
Results
Women felt the GENEActiv (94.7 %) and SenseWear Mini (90.0 %) were easier to wear and preferred the placement (68.4, 80 % respectively) as compared to the ActiGraph (42.9, 47.6 % respectively). Mean wear time on valid days was similar across sensors (ActiGraph: M = 918.8 ± 115.0 min; GENEActiv: M = 949.3 ± 86.6; SenseWear: M = 928.0 ± 101.8) and well above other studies using wake time only protocols. Informational feedback was the biggest motivator, while appearance, comfort, and inconvenience were the biggest barriers to wearing sensors. Wear time was valid on 93.9 % (ActiGraph), 100 % (GENEActiv), and 95.2 % (SenseWear) of eligible days. 61.9, 95.2, and 71.4 % of participants had seven valid days of data for the ActiGraph, GENEActiv, and SenseWear, respectively.
Conclusion
Twenty-four hour monitoring over seven consecutive days is a feasible approach in middle-aged women. Researchers should consider participant acceptability and demand, in addition to validity and reliability, when choosing a wearable sensor. More research is needed across populations and study designs.
The purpose of this study is to determine the feasibility of three widely used wearable sensors in research settings for 24 h monitoring of sleep, sedentary, and active behaviors in middle-aged women.
Methods
Participants were 21 inactive, overweight (M Body Mass Index (BMI) = 29.27 ± 7.43) women, 30 to 64 years (M = 45.31 ± 9.67). Women were instructed to wear each sensor on the non-dominant hip (ActiGraph GT3X+), wrist (GENEActiv), or upper arm (BodyMedia SenseWear Mini) for 24 h/day and record daily wake and bed times for one week over the course of three consecutive weeks. Women received feedback about their daily physical activity and sleep behaviors. Feasibility (i.e., acceptability and demand) was measured using surveys, interviews, and wear time.
Results
Women felt the GENEActiv (94.7 %) and SenseWear Mini (90.0 %) were easier to wear and preferred the placement (68.4, 80 % respectively) as compared to the ActiGraph (42.9, 47.6 % respectively). Mean wear time on valid days was similar across sensors (ActiGraph: M = 918.8 ± 115.0 min; GENEActiv: M = 949.3 ± 86.6; SenseWear: M = 928.0 ± 101.8) and well above other studies using wake time only protocols. Informational feedback was the biggest motivator, while appearance, comfort, and inconvenience were the biggest barriers to wearing sensors. Wear time was valid on 93.9 % (ActiGraph), 100 % (GENEActiv), and 95.2 % (SenseWear) of eligible days. 61.9, 95.2, and 71.4 % of participants had seven valid days of data for the ActiGraph, GENEActiv, and SenseWear, respectively.
Conclusion
Twenty-four hour monitoring over seven consecutive days is a feasible approach in middle-aged women. Researchers should consider participant acceptability and demand, in addition to validity and reliability, when choosing a wearable sensor. More research is needed across populations and study designs.
ContributorsHuberty, Jennifer (Author) / Ehlers, Diane (Author) / Kurka, Jonathan (Author) / Ainsworth, Barbara (Author) / Buman, Matthew (Author) / College of Health Solutions (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2015-07-30