A Multidimensional Urban Land Cover Change Analysis in Tempe, AZ

Rapid population growth leading to significant conversion of rural to urban lands requires deep understanding on how the human population interacts with the built-environment. Our research goal is to explore methodologies on how to analyze multidimensional urban change with the consideration of time, space, and landscape patterns. Using NAIP high resolution satellite images and LIDAR data, we were able to derive land cover classification maps and normalized height difference at different time periods. Then we performed the 2D, 3D and landscape pattern change analysis for a case study area. The research results show that a combination of 2D, 3D and landscape pattern change analysis can provide a comprehensive understanding of urban change, and the results will help urban planners and decision makers to better understand the status of urban transformation and design city for the future

An Ensemble Approach to Space-Time Interpolation

There has been much excitement and activity in recent years related to the relatively sudden availability of earth-related data and the computational capabilities to visualize and analyze these data. Despite the increased ability to collect and store large volumes of data, few individual data sets exist that provide both the requisite spatial and temporal observational frequency for many urban and/or regional-scale applications. The motivating view of this paper, however, is that the relative temporal richness of one data set can be leveraged with the relative spatial richness of another to fill in the gaps. We also note that any single interpolation technique has advantages and disadvantages. Particularly when focusing on the spatial or on the temporal dimension, this means that different techniques are more appropriate than others for specific types of data. We therefore propose a space- time interpolation approach whereby two interpolation methods – one for the temporal and one for the spatial dimension – are used in tandem in order to maximize the quality of the result. We call our ensemble approach the Space-Time Interpolation Environment (STIE). The primary steps within this environment include a spatial interpolator, a time-step processor, and a calibration step that enforces phenomenon-related behavioral constraints. The specific interpolation techniques used within the STIE can be chosen on the basis of suitability for the data and application at hand. In the current paper, we describe STIE conceptually including the structure of the data inputs and output, details of the primary steps (the STIE processors), and the mechanism for coordinating the data and the 1 processors. We then describe a case study focusing on urban land cover in Phoenix Arizona. Our empirical results show that STIE was effective as a space-time interpolator for urban land cover with an accuracy of 85.2% and furthermore that it was more effective than a single technique.

An Ensemble Approach to Space-Time Interpolation

There has been much excitement and activity in recent years related to the relatively sudden availability of earth-related data and the computational capabilities to visualize and analyze these data. Despite the increased ability to collect and store large volumes of data, few individual data sets exist that provide both the requisite spatial and temporal observational frequency for many urban and/or regional-scale applications. The motivating view of this paper, however, is that the relative temporal richness of one data set can be leveraged with the relative spatial richness of another to fill in the gaps. We also note that any single interpolation technique has advantages and disadvantages. Particularly when focusing on the spatial or on the temporal dimension, this means that different techniques are more appropriate than others for specific types of data. We therefore propose a space- time interpolation approach whereby two interpolation methods – one for the temporal and one for the spatial dimension – are used in tandem in order to maximize the quality of the result. We call our ensemble approach the Space-Time Interpolation Environment (STIE). The primary steps within this environment include a spatial interpolator, a time-step processor, and a calibration step that enforces phenomenon-related behavioral constraints. The specific interpolation techniques used within the STIE can be chosen on the basis of suitability for the data and application at hand. In the current paper, we describe STIE conceptually including the structure of the data inputs and output, details of the primary steps (the STIE processors), and the mechanism for coordinating the data and the processors. We then describe a case study focusing on urban land cover in Phoenix, Arizona. Our empirical results show that STIE was effective as a space-time interpolator for urban land cover with an accuracy of 85.2% and furthermore that it was more effective than a single technique.

Space-Time Forecasting Using Soft Geostatistics: A Case Study in Forecasting Municipal Water Demand for Phoenix, AZ

Managing environmental and social systems in the face of uncertainty requires the best possible forecasts of future conditions. We use space-time variability in historical data and projections of future population density to improve forecasting of residential water demand in the City of Phoenix, Arizona. Our future water estimates are derived using the first and second order statistical moments between a dependent variable, water use, and an independent variable, population density. The independent variable is projected at future points, and remains uncertain. We use adjusted statistical moments that cover projection errors in the independent variable, and propose a methodology to generate information-rich future estimates. These updated estimates are processed in Bayesian Maximum Entropy (BME), which produces maps of estimated water use to the year 2030. Integrating the uncertain estimates into the space-time forecasting process improves forecasting accuracy up to 43.9% over other space-time mapping methods that do not assimilate the uncertain estimates. Further validation studies reveal that BME is more accurate than co-kriging that integrates the error-free independent variable, but shows similar accuracy to kriging with measurement error that processes the uncertain estimates. Our proposed forecasting method benefits from the uncertain estimates of the future, provides up-to-date forecasts of water use, and can be adapted to other socioeconomic and environmental applications.

Continental-scale homogenization of residential lawn plant communities

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Landscape and Urban Planning 165 (2017): 54-63, doi:10.1016/j.landurbplan.2017.05.004.Residential lawns are highly managed ecosystems that occur in urbanized landscapes across the United States. Because they are ubiquitous, lawns are good systems in which to study the potential homogenizing effects of urban land use and management together with the continental-scale effects of climate on ecosystem structure and functioning. We hypothesized that similar homeowner preferences and management in residential areas across the United States would lead to low plant species diversity in lawns and relatively homogeneous vegetation across broad geographical regions. We also hypothesized that lawn plant species richness would increase with regional temperature and precipitation due to the presence of spontaneous, weedy vegetation, but would decrease with household income and fertilizer use. To test these predictions, we compared plant species composition and richness in residential lawns in seven U.S. metropolitan regions. We also compared species composition in lawns with understory vegetation in minimally-managed reference areas in each city. As expected, the composition of cultivated turfgrasses was more similar among lawns than among reference areas, but this pattern also held among spontaneous species. Plant species richness and diversity varied more among lawns than among reference areas, and more diverse lawns occurred in metropolitan areas with higher precipitation. Native forb diversity increased with precipitation and decreased with income, driving overall lawn diversity trends with these predictors as well. Our results showed that both management and regional climate shaped lawn species composition, but the overall homogeneity of species regardless of regional context strongly suggested that management was a more important driver.This research was supported by the Macrosystems Biology Program in the Emerging Frontiers Division of the Biological Sciences Directorate at the National Science Foundation (NSF) under grants EF-1065548, 1065737, 1065740, 1065741, 1065772, 1065785, 1065831, and 121238320

American Environmentalism and the City: An Ecosystem Services Perspective

This study traces the evolution of eight influential American environmental organizations and their relationship with, and conceptualizations of, ‘the city’ and ‘nature.’ Guided by the ecosystem services framework, the organizations’ urban-based initiatives were analyzed to determine what types of ecosystem services they emphasized and what the implications are for cities and environmental health. Results from historical, content, and interview analyses reveal the potential of the ecosystem services framework to unite the interests and efforts of multiple stakeholders, including urbanists, ecologists, economists, and environmentalists in a way that enhances both urban quality of life and conservation efforts overall

Adaptation, exposure, and politics: Local extreme heat and global climate change risk perceptions in the phoenix metropolitan region, USA

Cities around the planet are facing climate change risks including (but not limited to) extreme heat, drought, wildfire, and flooding. Urbanites perceptions of the risks posed by climate change influence communities' mitigation and adaption responses, but there is limited literature on the perceptions of climate risks in cities. Urban climate change impacts are multi-scalar, but existing work isolates local versus global considerations. Adaptive capacity affects climate change impacts, yet scholarship on urban climate typically is not framed through an adaptive capacity lens. In this study, we explore how exposure to heat, place-based vs. social connections, and socio-demographics affect residents' perception that extreme heat (local extreme heat) or climate change (global climate change) seriously affects their household and way of life. Using a survey from metropolitan Phoenix, Arizona (USA), an area facing increased extreme heat and rapid climate change, this study shows that urbanites' perceptions of risks posed by extreme weather conditions and global climate change are mediated in part by the existing urban infrastructure and planning (e.g., access to urban green infrastructure) and magnified by exposure to heat, but also shaped by political ideology. We also find that place attachment and Latino or Hispanic ethnic background positively affect perceptions of local extreme heat, while high income negatively influences perceptions of global climate change impacts. Heat exposure positively, whereas green infrastructure negatively affects risk perceptions of both local extreme heat and global climate change. Risk perceptions are influenced by exposure and adaptive capacity. Identifying the drivers of risk perceptions across different local contexts is an essential step for generating in-situ climate adaptation strategies for cities.publishedVersio

Paleodistribution modeling in archaeology and paleoanthropology

abstract: Species distribution modeling (SDM) is a methodology that has been widely used in the past two decades for developing quantitative, empirical, predictive models of species–environment relationships. SDM methods could be more broadly applied than they currently are to address research questions in archaeology and paleoanthropology. Specifically, SDM can be used to hindcast paleodistributions of species and ecological communities (paleo-SDM) for time periods and locations of prehistoric human occupation. Paleo-SDM may be a powerful tool for understanding human prehistory if used to hindcast the distributions of plants, animals and ecological communities that were key resources for prehistoric humans and to use this information to reconstruct the resource landscapes (paleoscapes) of prehistoric people. Components of the resource paleoscape include species (game animals, food plants), habitats, and geologic features and landforms associated with stone materials for tools, pigments, and so forth. We first review recent advances in SDM as it has been used to hindcast paleodistributions of plants and animals in the field of paleobiology. We then compare the paleo-SDM approach to paleoenvironmental reconstructions modeled from zooarchaeological and archaeobotanical records, widely used in archaeology and paleoanthropology. Next, we describe the less well developed but promising approach of using paleo-SDM methods to reconstruct resource paleoscapes. We argue that paleo-SDM offers an explicitly deductive strategy that generates spatial predictions grounded in strong theoretical understandings of the relation between species, habitat distributions and environment. Because of their limited sampling of space and time, archaeobiological records may be better suited for paleo-SDM validation than directly for paleoenvironmental reconstruction. We conclude by discussing the data requirements, limitations and potential for using predictive modeling to reconstruct resource paleoscapes. There is a need for improved paleoclimate models, improved paleoclimate proxy and species paleodistribution data for model validation, attention to scale issues, and rigorous modeling methods including mechanistic models.This is the accepted author manuscript, accepted for publication 12/17/1