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\title{Results from Prior NSF Support}
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{Dr Shekhar is a co-PI of a NSF grant titled \emph{CISE Expeditions in Computing: Understanding Climate Change: A Data Driven Approach} (Grant No.~1029711, \$10M approx, 08/2010-08/2015). This grant supported two graduate students (50\%) and one summer month faculty salary each year.}\\
%include \cite{EROUTE, TAG, SEI, CLEANER, Mobile, Diverse, Land, LandGraph, Networked, IGERT, Climate}. He also received grants from U.S. Department of Defense~\cite{DOD1, DOD2, DOD3}.
%Their most closely related grant is titled ``CRI:IAD Infrastructure for Research in Spatio-Temporal and Context-Aware Systems and Applications'' \cite{IAD} (Award\# CNS-0708604), for the amount of \$140,403 over the time period July 2007--June 2011. The equipment supported by the grant included a 28 node cluster, storage and, mobile devices. We now briefly describe the most significant accomplishments related to intellectual merit and broader impact.

\noindent \textbf{\underline{Intellectual Merits:}} Results from recent projects were published in {8} peer-reviewed articles~\cite{jiang2012learning, mohan2012quantifying, jiang2013Focal, jiang2014Focal, zhou2014spatiotemporal, zhouTKDE, zhou2011discovering, zhou2013discovering}. Illustrative examples of results include: \textbf{(1)} Spatial decision tree for geographical classification (Preliminary results published at IEEE ICDM~\cite{jiang2013Focal}, extended version accepted with major revision by the IEEE TKDE ~\cite{jiang2014Focal}). \textbf{(2)} An inter-disciplinary survey on change footprint patterns and discovery techniques.  (Published at the Wiley Interdisciplinary Review: Data Mining and Knowledge Discovery ~\cite{zhou2014spatiotemporal}). \textbf{(3)} Interesting sub-path discovery. (Initial results published at ACM SIGSPATIAL GIS ~\cite{zhou2011discovering}, full results accepted with major revision by IEEE TKDE ~\cite{zhouTKDE}). Our research results could also contribute to advancing computer science research in a broader range. Examples include the G-DAG representation and the RTCP algorithm~\cite{zhouTKDE}, which also might be used for pattern discovery in image and video processing, and the interdisciplinary survey~\cite{zhou2014spatiotemporal}, which enabled cross-fertilization of techniques from various disciplines. 

%\noindent \textbf{\underline{Relationship with proposed work:}}  The current research in the above project assumes geometry-specified shapes (e.g., circles, rectangles) of change footprints, which may not reflect the constraints in the natural environment (e.g., terrain, landscape). The new project will pursue physics-aware change footprint discovery techniques which automatically and efficiently extract/learn geography-specified constraints to guide the change footprint pattern discovery process. 

\noindent \textbf{\underline{Broader Impact:}} This project has supported two Ph.D. students on their thesis research. Doctoral student X. Zhou supported by this project  defended his Ph.D. thesis in June 2014. His thesis topic is "Spatiotemporal Big Data Analytics: Change Footprint Pattern Discovery". Another doctoral student Z. Jiang will take his thesis proposal exam in 2014, too. Jiang's thesis explores computational approaches to learning spatial decision trees for geographic classification.
Using the results obtained in our project, we contributed towards improving STEM education by enhancing the curriculum of a course titled ``Spatial databases (CSci 8715).'' Dr. Shekhar also presented several invited lectures and keynotes at major conferences.
Finally, the projects have engaged underrepresented students in research and education. In summer 2012, Ms. Rahni Sumler, an undergraduate student from North Carolina A\&T University (a HBCU) had a six-week internship on spatiotemporal data analytics for understanding climate change. Ms. Reem Y. Ali joined group in Fall 2012 and is working on cloud computing approach to persistent change window discovery on Earth science data. 
%\noindent \textbf{\underline{Other projects:}} Dr Shekhar also received other grants from NSF and USDOD. Due to the lack of relevance and space, results of these grants are not listed here but can be found at a full summary \cite{shekharGrants}. 
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%\textbf{Appendix: Other Intellectual Merits}
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%\item A K-Main Routes Activities Summarizations on Spatial Networks. Data summarization is an important concept in data mining for finding a compact representation of a dataset. In spatial network activity summarization (SNAS), we are given a spatial network and a collection of activities (e.g., pedestrian fatality reports, crime reports) and the goal is to find k shortest paths that summarize the activities. SNAS is important for applications where observations occur along linear paths such as roadways, train tracks, etc. SNAS is computationally challenging because of the large number of k subsets of shortest paths in a spatial network. Previous work has focused on either geometry or subgraph-based approaches (e.g., only one path), and cannot summarize activities using multiple paths. We proposed a K-Main Routes (KMR) approach that discovers k shortest paths to summarize activities. KMR generalizes K-means for network space but uses shortest paths instead of ellipses to summarize activities. To improve performance, KMR uses network Voronoi, divide and conquer, and pruning strategies. We present a case study comparing KMR's network-based output (i.e., shortest paths) to geometry-based outputs (e.g., ellipses) on pedestrian fatality data. Experimental results on synthetic and real data showed that KMR with our performance-tuning decisions yields substantial computational savings without reducing summary path coverage. Preliminary results have been published at ICDM workshop~\cite{oliver2010k} and full results have been published at IEEE Transections on Knowledge and Data Engineering (TKDE)~\cite{oliver2013k}.\\
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%\item Capacity Constraint Network Voronoi Diagram. Given a graph and a set of service centers, a Capacity Constrained Network-Voronoi Diagram (CCNVD) partitions the graph into a set of contiguous service areas that meet service center capacities and minimize the sum of the distances (min-sum) from graph-nodes to allotted service centers. The CCNVD problem is important for critical societal applications such as assigning evacuees to shelters and assigning patients to hospitals. This problem is NP-hard; it is computationally challenging because of the large size of the transportation network and the constraint that Service Areas (SAs) must be contiguous in the graph to simplify communication of allotments. Previous work has focused on honoring either service center capacity constraints (e.g., min-cost flow) or service area contiguity (e.g., Network Voronoi Diagrams), but not both. We proposed a novel Pressure Equalizer (PE) approach for CCNVD to meet the capacity constraints of service centers while maintaining the contiguity of service areas. Experiments and a case study using post-hurricane Sandy scenarios demonstrate that the proposed algorithm has comparable solution quality to min-cost flow in terms of min-sum; furthermore it creates contiguous service areas, and significantly reduces computational cost. Preliminary results have been published at International Symposium on Spatial and Temporal Databases~\cite{yang2013capacity}.
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