Extracting moving regions from spatial data

McKenney, Mark; Webb, James

doi:10.1145/1869790.1869856

Cited by 24 publications

(10 citation statements)

References 7 publications

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“…For the remainder of the paper, we use the notation m f () to indicate the application of a motion function. Basic motion functions exist [8,12] that guarantee type closure across an interval.…”

Section: Discrete Data Modelmentioning

confidence: 99%

The CMR model of moving regions

McKenney

Viswanadham

Littman

2014

Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming

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Many natural phenomena can be nicely represented by concepts of moving regions. For example, hurricanes, rain clouds, pollution zones, etc., change shape and position over time. Current models of moving regions have proven to be difficult to translate effectively to implementation for two reasons: i) algorithms for operations, such as intersection, are difficult to implement, and ii) creating instances of moving regions from data sources is difficult. In this paper, we create a new model of moving regions at the abstract, discrete, and implementation levels that overcome the difficulties of previous models. The CMR model focuses moving region representation at temporal instants, allowing the model to focus on temporally static regions; thus, algorithms for data collection and operations can be implemented using largely 2-dimensional algorithms, which are well understood. The result is a model that aligns well with data collection techniques, can be implemented easily, and allows complex movement patterns to be easily depicted.

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Section: Discrete Data Modelmentioning

confidence: 99%

The CMR model of moving regions

McKenney

Viswanadham

Littman

2014

Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming

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“…Therefore, our first step to completing these queries is to convert the data to a form that aligns more closely with reality. Using the algorithm in [12], we convert a hurricane represented as a series of regions at discrete points in time into a moving region with infinite temporal granularity. Essentially, the region snapshots are plotted in 3D space where the third dimension is time, and the region's movements between adjacent snapshots are interpolated; this process forms an approximation of the hurricane across a time interval.…”

Section: Queriesmentioning

confidence: 99%

“…In fact, we must adjust a range query to include 8 to 11 overlapping hurricanes before any overlap occurs in the Gulf Mexico. Figure 9 depicts the k-range-intersection (8,12) query result, indicating a small area of coverage in the Gulf of Mexico and an area of coverage near North Carolina. These operators apply to both Query 1 and Query 2 above.…”

Section: Queriesmentioning

confidence: 99%

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Algorithms for fundamental spatial aggregate operations over regions

McKenney

Olsen

2013

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data

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Aggregate operators are a useful class of operators in relational databases. In this paper, we examine spatial aggregate operators over regions. Spatial aggregates are defined to operate over a set of regions, and return a single region as a result. We systematically identify individual spatial aggregate operations by extending existing spatial operations into aggregate form. Semantic meaning for each operator is defined over a specified data model. Once defined, algorithms for computing spatial aggregates over regions are provided. We show that all proposed aggregates can be computed using a single algorithm. Furthermore, we provide serial and parallel algorithm constructions that can take advantage of vector co-processors, such as graphical processing units (GPUs), and that can be integrated into map/reduce queries to take advantage of big data-style clusters. Example queries and their results are provided.

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“…We consider regions to be complex regions (Section 2) corresponding to the type definition in Schneider and Behr [2006], and with boundaries stored as polygonal curves [Lema et al 2003]. …”

Section: Introductionmentioning

confidence: 99%

Generating Moving Regions from Snapshots of Complex Regions

Mckennney

Frye

2015

ACM Trans. Spatial Algorithms Syst.

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Moving regions are a form of spatiotemporal data in which a region changes in shape and/or position over time. In many fields, moving regions representing real-world phenomena are collected using sensors that take temporally encoded snapshots of regions. We provide a novel algorithm that creates a moving region between any two complex regions. The proposed algorithm has worst-case time bounds of O(n 2 ), but can use approximation techniques to achieve O(n lg n) in practice, space bounds of O(n), and output size bounded by O(n) (where n is the number of line segments that define the boundaries of the regions).

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Extracting moving regions from spatial data

Abstract: We provide an algorithm that can construct a valid moving region from snapshots of a region in time. Our algorithm is robust in the sense that given valid input regions, the generated moving region will define a valid region at every instant over the given time period.

Cited by 24 publications

References 7 publications

The CMR model of moving regions

The CMR model of moving regions

Algorithms for fundamental spatial aggregate operations over regions

Generating Moving Regions from Snapshots of Complex Regions

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