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.

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.

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.

A MODIS/ASTER airborne simulator (MASTER) imagery for urban heat island research

Thermal imagery is widely used to quantify land surface temperatures to monitor the spatial extent and thermal intensity of the urban heat island (UHI) effect. Previous research has applied Landsat images, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, Moderate Resolution Imaging Spectroradiometer (MODIS) images, and other coarse- to medium-resolution remotely sensed imagery to estimate surface temperature. These data are frequently correlated with vegetation, impervious surfaces, and temperature to quantify the drivers of the UHI effect. Because of the coarse- to medium-resolution of the thermal imagery, researchers are unable to correlate these temperature data with the more generally available high-resolution land cover classification, which are derived from high-resolution multispectral imagery. The development of advanced thermal sensors with very high-resolution thermal imagery such as the MODIS/ASTER airborne simulator (MASTER) has investigators quantifying the relationship between detailed land cover and land surface temperature. While this is an obvious next step, the published literature, i.e., the MASTER data, are often used to discriminate burned areas, assess fire severity, and classify urban land cover. Considerably less attention is given to use MASTER data in the UHI research. We demonstrate here that MASTER data in combination with high-resolution multispectral data has made it possible to monitor and model the relationship between temperature and detailed land cover such as building rooftops, residential street pavements, and parcel-based landscaping. Here, we report on data sources to conduct this type of UHI research and endeavor to intrigue researchers and scientists such that high-resolution airborne thermal imagery is used to further explore the UHI effect

Impact of tree locations and arrangements on outdoor microclimates and human thermal comfort in an urban residential environment

Trees serve as a valuable asset in the urban built environment. In an arid city like Phoenix, trees are one of the primary urban green infrastructures to ameliorate extreme heat stress. Because of the cost of water and space in the desert residential environment, designing the optimal tree arrangement to maximize overall thermal benefits for residential neighborhoods is important and necessary. In this research, we first simulated a real neighborhood with current tree arrangement in ENVI-met (a holistic three-dimensional model for the simulation of surface-plant-air interactions), and validated the reliability of ENVI-met models by comparing the simulated results with systematic temperature collection transects. Further, we evaluated and compared differences in outdoor microclimates and human thermal comfort by simulating different tree layouts (clustered, equal interval, or dispersed) in the same neighborhood. Tree benefits at individual building scale and neighborhood scale are also compared and discussed. Based on the simulation, an equal interval two trees arrangement provided the most microclimate and human thermal comfort benefits in the neighborhood due to the importance of shading in the hot arid desert environment, following by clustered tree arrangement without canopy overlap. These findings will help policy makers and urban planners offer better guidelines for planting and establishing residential trees to mitigate extreme heat in the hot arid residential environment

The Role of GIS to Enable Public-Sector Decision Making Under Conditions of Uncertainty

Uncertainty is inherent in environmental planning and decision making. For example, water managers in arid regions are attuned to the uncertainty of water supply due to prolonged periods of drought. To contend with multiple sources and forms of uncertainty, resource managers implement strategies and tools to aid in the exploration and interpretation of data and scenarios. Various GIS capabilities, such as statistical analysis, modeling and visualization are available to decision makers who face the challenge of making decisions under conditions of deep uncertainty. While significant research has lead to the inclusion and representation of uncertainty in GIS, existing GIS literature does not address how decision makers implement and utilize GIS as an assistive technology to contend with deep uncertainty. We address this gap through a case study of water managers in the Phoenix Metropolitan Area, examining how they engage with GIS in making decisions and coping with uncertainty. Findings of a qualitative analysis of water mangers reveal the need to distinguish between implicit and explicit uncertainty. Implicit uncertainty is linked to the decision-making process, and while understood, it is not displayed or revealed separately from the data. In contrast, explicit uncertainty is conceived as separate from the process and is something that can be described or displayed. Developed from twelve interviews with Phoenix-area water managers in 2005, these distinctions of uncertainty clarify the use of GIS in decision making. Findings show that managers use the products of GIS for exploring uncertainty (e.g., cartographic products). Uncertainty visualization emerged as a current practice, but definitions of what constitutes such visualizations were not consistent across decision makers. Additionally, uncertainty was a common and even sometimes helpful element of decision making; rather than being a hindrance, it is seen as an essential component of the process. These findings contradict prior research relating to uncertainty visualization where decision makers often express discomfort with the presence of uncertainty.

Creating Online Communities of Practice: Enhancing Preservice Teacher Growth-A Case Study

The purpose of this qualitative study was to examine pre-service teachers and their use of online community. This study explored four focal participants enrolled at Benedictine College and their perceptions about online community. The participants were completing their student teaching block during the spring 2016 semester. The data sources included transcripts from an online community situated within Blackboard, two semi-structured interviews that took place before and after the online community, and a reflective essay written by the participants. The study critically analyzed the participant’s perception of their participation within online community and whether or not their participation extended their learning and understanding of educational topics. The study was framed using Wenger’s Community of Practice. Seven themes emerged as participants reported that their participation within the online community (a) extended learning and understanding beyond the classroom, (b) became a place for professional support and community, (c) stimulated reflective thinking, (d) served as a place to share experiences, (e) was a flexible venue for community, (f) had benefits that outweighed the negatives, (g) and was built on relationships. Implications include suggestions for higher education and P-12 education and their use of online professional development

Sustainable Urban Planning and Climate Change Scenarios: An Investigation of Staten Island\u27s Urban Planning

Recent events like Hurricane Sandy, which struck Staten Island, NY on October 29, 2012, serve as a costly reminder of how unsustainable designs and mounting social pressures can contribute to extensive structural damage and subsequent financial cost, from storm surge inundation and coastal flooding. It is unlikely that Hurricane Sandy was a one-time event but rather a warning of what can occur over the next century without proper mitigation strategies. Based on climate change projections, such extreme events are expected to become more frequent and intense due to warmer sea surface temperatures and rising sea levels (Emmanual, 2005; Kirtman et. al., 2013). To mitigate impacts and improve resiliency, we need to devise and implement robust planning strategies that reduce society\u27s exposure and vulnerability to extreme environmental hazards. Hurricane Sandy presents an opportunity to rethink existing designs and place environmental constraints to development at the forefront. In the 1960s, Ian McHarg conducted a land use suitability on Staten Island and deemed most of the extensively damaged areas unsuitable for urbanization (McHarg 1969, Wagner et al,. forthcoming). Best practices suggest, urban areas should be buffered from coastlines and riverbanks to reduce their exposure to flooding. The next best approach is to devise and implement stormwater management and other mitigating measures. As part of the recovery process, a number of resilient strategies on Staten Island such as elevating urban structures, expanding greenbelts, and incorporating natural buffers have been proposed to ameliorate existing threats and those attributable to climate change (e.g., sea level rise and increased coastal flooding). While these designs address vulnerability to inundation, questions arise as to how resilient these strategies will be to future coastal flooding and extreme events

Employing Spatial Metrics in Urban Land-Use / Land-Cover Mapping: Comparing the Getis and Geary Indices

We examine the potential of supplementing per-pixel classifiers with the Getis index (Gi) in comparison to the Geary’s C on a subset of Ikonos imagery for urban land-use and land-cover classification. The test is pertinent considering that the Gi is generally considered more capable of identifying clusters of points with similar attributes. We quantify the impact of varying distance thresholds on the classification product and demonstrate how well the Gi identified cold and hot spots in comparison to Geary’s C. The exercise also provides a rule of thumb for effectively measuring spatial association in connection to adjacency. We are able to support existing literature that measuring local variability improves classification over spectral information alone. The results, however, neither confirm nor deny the challenge on whether measuring cold and hot spots rather than just spatial association improves classification accuracy

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