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Guan, Qingfeng

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GeoComputational Intelligence and High-performance Geospatial ComputingQingfeng (Gene) Guan, Ph.DCenter for Advanced Land Management Information TechnologiesSchool of Natural ResourcesUniversity of Nebraska - LincolnGIS Day @ University of KansasNov. 16th, 2011Contents1. Computational Science and GeoComputation2. GeoComputational Intelligence- ANN-based Urban-CA model3. High-performance Geospatial Computing- Parallel Geostatistical Areal Interpolation- pRPL and pSLEUTH4. ConclusionIntroduction – Computational ScienceDefinition “the field of study concerned with constructing mathematical models and numerical solution techniques and using computers to analyze and solve scientific, social scientific and engineering problems.” (wikipedia)Domains include: Numerical simulations Model fitting and data analysisMassive computational intensity http://www.it.uu.se/edu/masters/CompSc/Introduction - GeoComputationDefinition Couclelis (1998) identified the “core GeoComputation” as the innovative (or derived from other disciplines) computer-based geospatial modeling and analysisContrasted against the traditional computer-supported spatial data analysis and geospatial modeling Openshaw (2000) also emphasized Computational Science as the origin of GeoComputation (the Computation part) and the essential concerns about geographical and earth systems (the Geo part) The capital G and CIntroduction – GeoComputation (cont.)Methodology A wide array of computer-based models and techniques, many of them derived from the field of Artificial Intelligence (AI) and the more recently defined area of Computational Intelligence (CI) (Couclelis, 1998)• Expert Systems, Cellular Automata, Neural Networks, Fuzzy Sets, Genetic Algorithms, Fractal Modelling, Visualization and Multimedia, Exploratory Data Analysis and Data Mining, etc.High-performance geospatial computingANN-Urban-CA: an urban growth modelOverview Combination of a Cellular Automata (CA) model, an Artificial Neural Network (ANN), and a macro-scale socio-economic model Integration of Geography, Natural Resource Science, Social Science, and Economics in a GeoComputation frameworkGeospatial Cellular Automata Bottom-Up structure Simple local rules to simulate complex global spatio-temporal dynamics Widely used in geospatial modeling Land-use/Land-cover Change Wildfire Propagation  Flood Spreading Freeway Traffic Flow More and More Coming up…Prediction of urban development to the year 2050 over southeastern Pennsylvania and part of Delaware using the SLEUTH modelhttp://www.essc.psu.edu/~dajr/chester/animation/movie_small.htmHard to set proper transition rules and parameters How to produce realistic simulations? Brute-force calibration• Generate results using all possible parameter values• Find the “best-match” combination• Highly computationally intensive Lack of global control Bottom-up structure Evolve without constraintsIssues of Geospatial CAANN-Urban-CA: Structure An Artificial-Neural-Network-Based, constrained, Cellular Automata model for urban growth simulationANN-Urban-CA: ANN Artificial Neural Network ANN is suited for dealing with complex nonlinear relationships, e.g., the impacts of driving factors to urban growth  ANN can learn from available data, and deal with redundancy, inaccuracy, and noise Knowledge and experience can be easily learned and stored for further simulationANN-Urban-CA: Macro ConstrainMacro-scale Socio-economic model The Tietenberg model is used to generate the proper demand for urban space in each period (e. g. year) in the future. A Resource Economic model, which usually is used to solve the problem of “how to consume resources in the future according to the principle of sustainable development” Lands are treated as non-regenerative resources, and the urbanization process is treated as land source consumption Population increase as the driving force of land consumption0)1(/1  ttatrcPbqa),,2,1( nt 01nttqQANN-Urban-CA: Training Purpose ANN adjusts the weight values Determine the best-fit transition rules and parameters of the CA Method Back-Propagation (BP) training algorithm Input: Driving factors Output: Urbanization probability  Target: Historical urban dataANN-Urban-CA: ResultsHistory Simulation Trained using samples of Beijing urban maps of 1980, 1995, and 2000 Simulate urban growth in Beijing 1995 - 2000Real Beijing urban, 2000Simulated Beijing urban, 20000.8318simrealsimrealAAAASalleeLee0.9018)()( 22nkjjnkiinkijzzzzcncorrelatioANN-Urban-CA: Results Future Forecast Increased populations of Beijing 2001- 2015 By using the TietenbergModel, 6 scenarios of urbanization were derived 02004006008001000120014001978198119841987199019931996199920022005200820112014Simulated UrbanPopulationReal UrbanPopulationYears Increased Pop（10,000）Total Scenario 1（hm2）Total Scenario 2（hm2）Total Scenario 3（hm2）r=0 r=0.01 r=0 r=0.01 r=0 r=0.012001～2005 123.956 7522.9 8201.1 15186.8 15781.6 17741.5 18308.52006～2010 141.766 8687.9 8758.7 17538.6 17600.8 20488.8 20548.12011～2015 162.134 10033.2 9284.2 20254.6 19597.6 23661.7 23035.4ANN-Urban-CA: ResultsUrban Growth in Beijing 2000 – 2015 (Scenarios 1)ANN-Urban-CA: ResultsUrban Growth in Beijing 2000 – 2015 (Scenario 4)Sub-Conclusion on ANN-Urban-CA ANN’s capability of dealing with nonlinear complex systems Calibrated without heavy computing overhead and subjective human interference Optimal quantity allocation + optimal spatial allocation Providing an ideal pattern of sustainable urban development, useful in urban planning Highly flexible structure and modeling approach Easily generalized to model other kinds of spatio-temporal dynamics for various purposes, e.g., spread of invasive species and vegetative epidemics, movement of toxic pollutants in water systems, and land-cover change caused by climate change Open to any possible/available datasets, e.g., numerous remotely sensed data and other natural resource and environmental dataHigh-performance Geospatial ComputingWhy high-performance computing? GeoComputation implies HPC Increasing demand for computational power in geospatial research and applications– Sophisticated and complicated analytical algorithms and simulation models– High-resolution and large-volume datasets– Rapid processing and real-time responseHigh-performance ComputingDefinition Usually refers to parallel computing• The use of multiple computing units (e.g., computers, processors/CPU cores, or processes) working together on a common task in a concurrent manner in order to achieve higher performance• In contrast to sequential computing that usually has only one computing unit Performance is usually measured with computing time Emerging Cyberinfrastructure Grid Computing Cloud ComputingA massive parallel computing system (http://ctbp.ucsd.edu/pc/html/intro4.html )Areal Interpolation – IntroductionDefinition Predicts the unknown (target) attribute values at the required partition (target zones or supports) from a set of known (source) attribute data available on a different partition (source zones or supports) Two main approaches Cartographical methods• Use cartographical properties of supports, e.g., area, as the basis• Simple and widely used Geostatistical methods• Use variants of Kriging• Accounts for spatial autocorrelation• Measure the reliability of prediction• Mass-preserving target prediction0 2 4 6x 1053.63.73.83.944.14.24.34.44.54.6x 106Population of counties0 2 4 6x 1053.63.73.83.944.14.24.34.44.54.6x  106Population of watersheds = ?An areal interpolation problemGeostatistical Areal Interpolation Steps Discretization of source and target supports with a regular raster (point values not known, just location) Computation of support-to-support covariances as integrals from a given point covariance model• Between all source supports • Between all source and target supports Use of Kriging system with computed covariancesto derive weights for interpolation  Interpolated values computed as linear combinations of the Kriging weights and the source dataFFT-based Areal Interpolation Traditional geostatistical interpolation Highly computationally intensive• Massive memory space• Long computing time FFT-based spectral method Use Fast Fourier Transform (FFT) to compute support-to-support covariances)(:)),1(()()}(),({),( ''' kkkkkkz gFFTCFFTgFFTsZsZCovss 0 50 100 150 200 250 3000501001502002503008590951001051101151200 50 100 150 200 250 300050100150200250300859095100105110115120Execution times (for a 360x360 discretization grid)Traditional method: ~4,000 sec FFT-based Spectral method: ~50 sec→FFT-based Areal Interpolation FFT-based method is STILL computationally intensive When it comes to real-world applications• Population density from counties to 3-digit zipcode regions in Northern California• 500X500 discretization grid• Matlab program• Penium4 3.2GHz PC with 2GB RAM• 900 seconds Solution Parallel computing• The computation of covariance between a pair of supports is independent from that of other pairs• The computation of prediction for a target support is independent from that for other target supportspAI: Algorithm Overview Three parallel processes Source-to-source, and source-to-target covariance matrices by means of FFT QR factorization of the source-to-source covariance matrix Source-to-target weights via Kriging, and predicted attribute values for target supportspAI: 1st Parallel Process Support-to-support covariance Parallel over source supports• Each processor handles a subset of source supportspAI: 2nd Parallel ProcessQR factorization Needed to solve a Ax=b problem• In a Kriging system– A: source-to-source covariance matrix– x: source-to-target weight matrix– b: source-to-target covariance matrix Each processor handles a subset of columns in the Q matrix• Data exchange among processors at each iteration• Non-blocking communication technique– Overlap computation and data exchangepAI: 3rd Parallel Process Source-to-target weights and target predictions Parallel over target supports• Each processor handles a subset of target supportspAI: Implementation Stand-along program Written in C++ Based on Message Passing Interface (MPI) Utilizes public-domain libraries• FFTW (www.fftw.org) • GsTL(http://pangea.stanford.edu/~nremy/GTL/) • Shapefile C Library (http://shapelib.maptools.org/) for data I/O• Template Numerical Toolkit (TNT, http://math.nist.gov/tnt/index.html) User-specified options Shapefiles of source and target supports Discretization density (point spacing distance) Covariogram model Task mapping scheme Simple Kriging or Ordinary KrigingpAI: Experiment Settings Two datasets Eastern Time Zone dataset• Source: population densities of counties in 2000 (2248 polygons)• Target: population densities of watersheds (1633 polygons) Continental U.S. dataset• Source: population densities of counties in 2000 (4703 polygons)• Target: population densities of watersheds (2848 polygons)Discretization scheme 2000-meter point spacing Eastern Time Zone – 1333X917 grid (1.2 million points) Continental U.S. - 1452X2348 grid (3.4 million points) Computer cluster 280 AMD quad core nodes (2.2 GHz, 8 GB RAM per node) 871 Opteron two-dual-core nodes (2.8 GHz, 8GB RAM per node) 800 MB/second InfiniBandpAI: ResultspAI: ResultspAI: Computing time050010001500200025003000350040001 2 4 8 16 32 64 128 256 512Computing Time (seconds)Number of CPU Cores(a) Eastern Time Zone Dataset050001000015000200002500030000350001 2 4 8 16 32 64 128 256 512Computing Time (seconds)Number of CPU Cores(b) Continental U.S. DatasetSub-Conclusion on pAI This parallel algorithm  Drastically reduced the computing time Achieved fairly high speed-ups and efficiencies Scaled reasonably well as the number of processors increased and as the problem size increased Based on global Kriging All source supports are used for the prediction for each target Can be used for local Kriging• Neighbor searchRegular discretization grids FFT-based technology requires regular grids If irregular discretization is to be used, computational complexity for support-to-support covariance is not uniform anymore• Adaptive task mapping methods will helppRPL: parallel Raster Processing LibraryRaster is born to be parallelized A raster dataset essentially is a matrix of values, each of which represents the attribute of the corresponding cell of the field A matrix can be easily partitioned into sub-matrices and assigned onto multiple processors so that the sub-matrices can be processed simultaneously  An open-source  general-purpose parallel Raster Processing programming Library  Encapsulates complex parallel computing utilities and routines specifically for raster processing Enables the implementation of parallel raster-processing algorithms with minimal knowledge of parallel computing and programming Greatly reduces the development complexity Possible usage Massive-volume geographic raster processing Image (including remote sensing imagery) processing Large-scale Cellular Automata (CA) and Agent-based Modeling Free downloadable and open source http://sourceforge.net/projects/prpl/pRPL: IntroductionObject-Oriented programming style Written in C++ Built upon the Message Passing Interface (MPI) Class templates support arbitrary data types e.g. integer, char, double precision floating point number, even user-defined types Transparent ParallelismpRPL: Features Spatially Flexible Supports any arbitrary neighborhood configuration Supports centralized and non-centralized algorithmspRPL: Features (cont.)Regular and irregular decomposition pRPL: Features (cont.)Multi-layer processing pRPL: Features (cont.)int main(int argc, char *argv[]) {PRProcess myPrc(MPI_COMM_WORLD); /* Raster processor */myPrc.init(argc, argv); Layer<int> lyr2update(myPrc, “Int_Layer”); … /* load the data on the master processor */lyr2update.smplDcmpDstrbt(SMPL_ROW, 2*myPrc.nPrcs()); /* Decompose distribute */ MyTransition myTrans; /* Customized Transition object */lyr2update.update(myTrans) ; /* Apply the Transition on the Layer */lyr2update.gatherCellSpace(); /* Gather the whole cellspace and store it on the master processor */myPrc.finalize();return 0;}pRPL: ProgrammingpSLEUTH: Introduction SLEUTH model Uses a modified CA to model the spread of urbanization across a landscape (Clarke et al., 1996, 1997) Its name comes from the GIS data layers that are incorporated into the model: Slope, Landuse, Exclusion layer, Urban, Transportation, and HillshadeUrban growth in Chester County, Pennsylvania, 1981 – 2025http://mcmcweb.er.usgs.gov/de_river_basin/phil/modeling.html Coefficients Dispersion Breed Spread Slope Road GravityRules Spontaneous Growth Rule (centralized) New Spreading Centers Rule (non-centralized) Edge Growth Rule (non-centralized) Road-Influenced Growth Rule (non-centralized) For more info. about SLEUTH: http://www.ncgia.ucsb.edu/projects/gig/pSLEUTH: Introduction (cont.) Calibration Determine best parameter values to produce realistic results Brute-force calibration• Produce results using all possible combinations of parameter values• Compare with historical data to determine the best-match combination• A large number of combinations of parameters (1015 coefficient sets)Extremely intensive computing overheadCalibration of SLUETHhttp://www.ncgia.ucsb.edu/projects/gig/pSLEUTH: Introduction (cont.) The four growth rules in the SLEUTH model, were implemented using pRPLpSLEUTH: Parallelization Processer Grouping With pRPL, pSLEUTH is able to organize the processors in groups. Data parallelism within a group, task parallelism among groups.  Static tasking and dynamic tasking pSLEUTH: Parallelization (cont.)Data Urban areas of the continental US (1980 and 1990, 4948×3108) Calibration Settings Only three values (0, 50, 100) will be evaluated for each coefficient The total number of simulations is 243 (= 35)  Each simulation includes 11 ( = 1990-1980+1) years pSLEUTH: Experiments SettingspSLEUTH – PerformanceComputing times of pSLEUTH0100020003000400050006000700080001 2 4 8 16 32staticdynamic - 2 gouprsdynamic - 4 groupsdynamic - 8 groups pRPL greatly reduces the development complexity of implementing a parallel raster processing algorithm pRPL can be used for many types of raster-based processing, including Cellular Automata (CA) and Agent-based Modeling pRPL largely reduces the computing time, and enables extremely complex geospatial analysis and modelingSub-Conclusions on pRPL and pSLEUTHConclusions GeoComputation is the application of Computational Science in geospatial studies A large variety of computer-based statistical and mathematical methods for the analysis and modeling of complex geospatial phenomena A natural next step of GIS High-performance computing enables extremely complex geospatial analysis and modeling using massive-volume data GeoComputation can be used as an exploratory-analysis tool, a simulation method, a problem-solving environment, a decision-making-support and planning tool, a theory test-bed, and a theory-discovery approachThank You!

GeoComputational Intelligence and High-Performance Geospatial Computing

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GeoComputational Intelligence and High-Performance Geospatial Computing

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