Spatial Analytics Workshop
|
|
- Harry Freeman
- 5 years ago
- Views:
Transcription
1 Spatial Analytics Workshop Pete Skomoroch, LinkedIn Kevin Weil, Twitter Sean Gorman, FortiusOne #spatialanalytics
2 Introduction The Rise of Spatial Analytics Spatial Analysis Techniques Hadoop, Pig, and Big Data Bringing the Two Together Conclusion Q&A
3 Introduction The Rise of Spatial Analytics Spatial Analysis Techniques Hadoop, Pig, and Big Data Bringing the Two Together Conclusion Q&A
4 Introduction The Rise of Spatial Analytics Spatial Analysis Techniques Hadoop, Pig, and Big Data Bringing the Two Together Conclusion Q&A
5 Spatial Analysis Analytical techniques to determine the spatial distribution of a variable, the relationship between the spatial distribution of variables, and the association of the variables in an area.
6 Pattern Analysis
7 Spatial Analysis Types 1. Spatial autocorrelation 2. Spatial interpolation 3. Spatial interaction 4. Simulation and modeling 5. Density mapping
8 Spatial Autocorrelation Spatial autocorrelation statistics measure and analyze the degree of dependency among observations in a geographic space. First law of geography: everything is related to everything else, but near things are more related than distant things. -- Waldo Tobler
9 Moran s I - Random Variable Moran s I - Per Capita Income in Monroe County Moran s I =.012 Moran s I =.66
10 Spatial Interpolation Spatial interpolation methods estimate the variables at unobserved locations in geographic space based on the values at observed locations.
11 $14.00 Chicago $14.00 NYC Natural Gas Demand in Response to February 21, 2003 Alberta Clipper cold front $7.55 Henry
12 $18.50 Chicago $30.00 NYC Natural Gas Demand in Response to February 24, 2003 Alberta Clipper cold front $16.00 Henry
13 $20.00 Chicago $37.00 NYC Natural Gas Demand in Response to February 25, 2003 Alberta Clipper cold front $22.00 Henry
14 Spatial Interaction Spatial interaction or gravity models estimate the flow of people, material, or information between locations in geographic space.
15 Introduction Motiviation Execution Prototype Service API Operations UX Global Oil Supply and Demand Gravity Model
16 Simulation and Modeling Simple interactions among proximal entities can lead to intricate, persistent, and functional spatial entities at aggregate levels (complex adaptive systems).
17 SpaNal Interdependency Analysis of the San Francisco Failure SimulaNon Infrastructure Refined Products (NaAonal) Refined Products (MSA) Total Number of Links No. Links Congested % Links Congested %Volume Delay 3, % 0.05% % 93% Power Grid (Regional) 1, % N/A Power Grid (MSA) % N/A
18 Density Mapping Calculating the proximity and frequency of a spatial phenomenon by creating a probabilistic surface.
19 New York City Fiber Density Map
20 Standard GIS Architectures
21 Distributed Analytics Queueing analysis tasks from disparate data sources for agents to run across distributed servers to collate back to the user as answers.
22 Agents Distributed Servers Disparate Data User Request Queue Analysis
23 ( 1. Rasterize 2. Kernel density calc User 3. Color map Request Queue Agent Amazon EC2 Amazon S3
24 Vector Density Mapping Demo
25
26
27
28 Introduction The Rise of Spatial Analytics Spatial Analysis Techniques Hadoop, Pig, and Big Data Bringing the Two Together Conclusion Q&A
29 Data is Getting Big NYSE: 1 TB/day Facebook: 20+ TB compressed/day CERN/LHC: 40 TB/day (15 PB/year!) And growth is accelerating Need multiple machines, horizontal scalability
30 Hadoop Distributed file system (hard to store a PB) Fault-tolerant, handles replication, node failure, etc MapReduce-based parallel computation (even harder to process a PB) Generic key-value based computation interface allows for wide applicability Open source, top-level Apache project Scalable: Y! has a 4000-node cluster Powerful: sorted a TB of random integers in 62 seconds
31 MapReduce? cat file grep geo sort uniq -c > output Challenge: how many tweets per county, given tweets table? Input: key=row, value=tweet info Map: output key=county, value=1 Shuffle: sort by county Reduce: for each county, sum Output: county, tweet count With 2x machines, runs close to 2x faster.
32 MapReduce? cat file grep geo sort uniq -c > output Challenge: how many tweets per county, given tweets table? Input: key=row, value=tweet info Map: output key=county, value=1 Shuffle: sort by county Reduce: for each county, sum Output: county, tweet count With 2x machines, runs close to 2x faster.
33 MapReduce? cat file grep geo sort uniq -c > output Challenge: how many tweets per county, given tweets table? Input: key=row, value=tweet info Map: output key=county, value=1 Shuffle: sort by county Reduce: for each county, sum Output: county, tweet count With 2x machines, runs close to 2x faster.
34 MapReduce? cat file grep geo sort uniq -c > output Challenge: how many tweets per county, given tweets table? Input: key=row, value=tweet info Map: output key=county, value=1 Shuffle: sort by county Reduce: for each county, sum Output: county, tweet count With 2x machines, runs close to 2x faster.
35 MapReduce? cat file grep geo sort uniq -c > output Challenge: how many tweets per county, given tweets table? Input: key=row, value=tweet info Map: output key=county, value=1 Shuffle: sort by county Reduce: for each county, sum Output: county, tweet count With 2x machines, runs close to 2x faster.
36 MapReduce? cat file grep geo sort uniq -c > output Challenge: how many tweets per county, given tweets table? Input: key=row, value=tweet info Map: output key=county, value=1 Shuffle: sort by county Reduce: for each county, sum Output: county, tweet count With 2x machines, runs close to 2x faster.
37 MapReduce? cat file grep geo sort uniq -c > output Challenge: how many tweets per county, given tweets table? Input: key=row, value=tweet info Map: output key=county, value=1 Shuffle: sort by county Reduce: for each county, sum Output: county, tweet count With 2x machines, runs close to 2x faster.
38 But... Analysis typically done in Java Single-input, two-stage data flow is rigid Projections, filters: custom code Joins: lengthy, error-prone n-stage jobs: Hard to manage Prototyping/exploration requires compilation analytics in Eclipse? ur doin it wrong...
39 Enter Pig High level language Transformations on sets of records Process data one step at a time Easier than SQL?
40 Why Pig? Because I bet you can read the following script.
41 A Real Pig Script Now, just for fun... the same calculation in vanilla Hadoop MapReduce.
42 No, seriously.
43 Pig Simplifies Analysis The Pig version is: 5% of the code, 5% of the time Within 50% of the execution time. Pig Geo: Programmable: fuzzy matching, custom filtering Easily link multiple datasets, regardless of size/structure Iterative, quick
44 A Real Example Fire up your EMR.... or follow along at Pete used Twitter s streaming API to store some tweets Simplest thing: group by location and count with Pig Here comes some code!
45
46 tweets = LOAD 's3://where20demo/sample-tweets' as ( user_screen_name:chararray, tweet_id:chararray,... user_friends_count:int, user_statuses_count:int, user_location:chararray, user_lang:chararray, user_time_zone:chararray, place_id:chararray,...);
47 tweets = LOAD 's3://where20demo/sample-tweets' as ( user_screen_name:chararray, tweet_id:chararray,... user_friends_count:int, user_statuses_count:int, user_location:chararray, user_lang:chararray, user_time_zone:chararray, place_id:chararray,...);
48 tweets_with_location = FILTER tweets BY user_location!= 'NULL';
49 normalized_locations = FOREACH tweets_with_location GENERATE LOWER(user_location) as user_location;
50 grouped_tweets = GROUP normalized_locations BY user_location PARALLEL 10;
51 location_counts = FOREACH grouped_tweets GENERATE $0 as location, SIZE($1) as user_count;
52 sorted_counts = ORDER location_counts BY user_count DESC;
53 STORE sorted_counts INTO 'global_location_tweets';
54 hadoop dfs -cat /global_location_counts/part* head -30 brasil indonesia brazil london usa são paulo new york tokyo singapore rio de janeiro los angeles 9934 california 9386 chicago 9155 uk 9095 jakarta 9086 germany 8741 canada jakarta, indonesia 6480 nyc 6456 new york, ny 6331
55 Neat, but... Wow, that data is messy! brasil, brazil at #1 and #3 new york, nyc, and new york ny all in the top 30 Pete to the rescue.
56 Introduction The Rise of Spatial Analytics Spatial Analysis Techniques Hadoop, Pig, and Big Data Bringing the Two Together Conclusion Q&A
57
58
59
60
61
62
63
64
65
66
67 Users by County
68 Lady Gaga
69 Tea Party
70 Dallas
71 Colbert
72 Introduction The Rise of Spatial Analytics Spatial Analysis Techniques Hadoop, Pig, and Big Data Bringing the Two Together Conclusion Q&A
73 Introduction The Rise of Spatial Analytics Spatial Analysis Techniques Hadoop, Pig, and Big Data Bringing the Two Together Conclusion Q&A
74 Questions? Follow us at twitter.com/peteskomoroch twitter.com/kevinweil twitter.com/seangorman
CS425: Algorithms for Web Scale Data
CS425: Algorithms for Web Scale Data Most of the slides are from the Mining of Massive Datasets book. These slides have been modified for CS425. The original slides can be accessed at: www.mmds.org Challenges
More informationVisualizing Big Data on Maps: Emerging Tools and Techniques. Ilir Bejleri, Sanjay Ranka
Visualizing Big Data on Maps: Emerging Tools and Techniques Ilir Bejleri, Sanjay Ranka Topics Web GIS Visualization Big Data GIS Performance Maps in Data Visualization Platforms Next: Web GIS Visualization
More informationBig Data Infrastructure CS 489/698 Big Data Infrastructure (Winter 2016)
Big Data Infrastructure CS 489/698 Big Data Infrastructure (Winter 2016) Week 12: Real-Time Data Analytics (2/2) March 31, 2016 Jimmy Lin David R. Cheriton School of Computer Science University of Waterloo
More informationMapReduce in Spark. Krzysztof Dembczyński. Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland
MapReduce in Spark Krzysztof Dembczyński Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland Software Development Technologies Master studies, second semester
More informationMapReduce in Spark. Krzysztof Dembczyński. Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland
MapReduce in Spark Krzysztof Dembczyński Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland Software Development Technologies Master studies, first semester
More informationNature of Spatial Data. Outline. Spatial Is Special
Nature of Spatial Data Outline Spatial is special Bad news: the pitfalls of spatial data Good news: the potentials of spatial data Spatial Is Special Are spatial data special? Why spatial data require
More informationCS 347. Parallel and Distributed Data Processing. Spring Notes 11: MapReduce
CS 347 Parallel and Distributed Data Processing Spring 2016 Notes 11: MapReduce Motivation Distribution makes simple computations complex Communication Load balancing Fault tolerance Not all applications
More informationRepresentation of Geographic Data
GIS 5210 Week 2 The Nature of Spatial Variation Three principles of the nature of spatial variation: proximity effects are key to understanding spatial variation issues of geographic scale and level of
More informationJoint MISTRAL/CESI lunch workshop 15 th November 2017
MISTRAL@Newcastle Joint MISTRAL/CESI lunch workshop 15 th November 2017 ITRC at Newcastle ITRC at Newcastle MISTRAL at Newcastle New approach to infrastructure data management to open-up analytics, modelling
More informationQuery Analyzer for Apache Pig
Imperial College London Department of Computing Individual Project: Final Report Query Analyzer for Apache Pig Author: Robert Yau Zhou 00734205 (robert.zhou12@imperial.ac.uk) Supervisor: Dr Peter McBrien
More informationArcGIS Enterprise: What s New. Philip Heede Shannon Kalisky Melanie Summers Sam Williamson
ArcGIS Enterprise: What s New Philip Heede Shannon Kalisky Melanie Summers Sam Williamson ArcGIS Enterprise is the new name for ArcGIS for Server What is ArcGIS Enterprise ArcGIS Enterprise is powerful
More informationDistributed Architectures
Distributed Architectures Software Architecture VO/KU (707023/707024) Roman Kern KTI, TU Graz 2015-01-21 Roman Kern (KTI, TU Graz) Distributed Architectures 2015-01-21 1 / 64 Outline 1 Introduction 2 Independent
More informationRainfall data analysis and storm prediction system
Rainfall data analysis and storm prediction system SHABARIRAM, M. E. Available from Sheffield Hallam University Research Archive (SHURA) at: http://shura.shu.ac.uk/15778/ This document is the author deposited
More informationKnowledge Discovery and Data Mining 1 (VO) ( )
Knowledge Discovery and Data Mining 1 (VO) (707.003) Map-Reduce Denis Helic KTI, TU Graz Oct 24, 2013 Denis Helic (KTI, TU Graz) KDDM1 Oct 24, 2013 1 / 82 Big picture: KDDM Probability Theory Linear Algebra
More informationEverything is related to everything else, but near things are more related than distant things.
SPATIAL ANALYSIS DR. TRIS ERYANDO, MA Everything is related to everything else, but near things are more related than distant things. (attributed to Tobler) WHAT IS SPATIAL DATA? 4 main types event data,
More informationArcGIS Enterprise: What s New. Philip Heede Shannon Kalisky Melanie Summers Shreyas Shinde
ArcGIS Enterprise: What s New Philip Heede Shannon Kalisky Melanie Summers Shreyas Shinde ArcGIS Enterprise is the new name for ArcGIS for Server ArcGIS Enterprise Software Components ArcGIS Server Portal
More informationArcGIS GeoAnalytics Server: An Introduction. Sarah Ambrose and Ravi Narayanan
ArcGIS GeoAnalytics Server: An Introduction Sarah Ambrose and Ravi Narayanan Overview Introduction Demos Analysis Concepts using GeoAnalytics Server GeoAnalytics Data Sources GeoAnalytics Server Administration
More informationGIS for ChEs Introduction to Geographic Information Systems
GIS for ChEs Introduction to Geographic Information Systems AIChE Webinar John Cirucci 1 GIS for ChEs Introduction to Geographic Information Systems What is GIS? Tools and Methods Applications Examples
More informationArcGIS Platform For NSOs
ArcGIS Platform For NSOs Applying GIS and Spatial Thinking to Official Statistics Esri UC 2014 Demo Theater Applying GIS at the NSO Generic Statistical Business Process Model (GSBPM) 1 Specify Needs 2
More informationLuc Anselin Spatial Analysis Laboratory Dept. Agricultural and Consumer Economics University of Illinois, Urbana-Champaign
GIS and Spatial Analysis Luc Anselin Spatial Analysis Laboratory Dept. Agricultural and Consumer Economics University of Illinois, Urbana-Champaign http://sal.agecon.uiuc.edu Outline GIS and Spatial Analysis
More informationM. Saraiva* 1 and J. Barros 1. * Keywords: Agent-Based Models, Urban Flows, Accessibility, Centrality.
The AXS Model: an agent-based simulation model for urban flows M. Saraiva* 1 and J. Barros 1 1 Department of Geography, Birkbeck, University of London, 32 Tavistock Square, London, WC1H 9EZ *Email: m.saraiva@mail.bbk.ac.uk
More informationIntroduction-Overview. Why use a GIS? What can a GIS do? Spatial (coordinate) data model Relational (tabular) data model
Introduction-Overview Why use a GIS? What can a GIS do? How does a GIS work? GIS definitions Spatial (coordinate) data model Relational (tabular) data model intro_gis.ppt 1 Why use a GIS? An extension
More informationBehavioral Simulations in MapReduce
Behavioral Simulations in MapReduce Guozhang Wang, Marcos Vaz Salles, Benjamin Sowell, Xun Wang, Tuan Cao, Alan Demers, Johannes Gehrke, Walker White Cornell University 1 What are Behavioral Simulations?
More informationSpatial analysis. Spatial descriptive analysis. Spatial inferential analysis:
Spatial analysis Spatial descriptive analysis Point pattern analysis (minimum bounding box, mean center, weighted mean center, standard distance, nearest neighbor analysis) Spatial clustering analysis
More informationImplications for the Sharing Economy
Locational Big Data and Analytics: Implications for the Sharing Economy AMCIS 2017 SIGGIS Workshop Brian N. Hilton, Ph.D. Associate Professor Director, Advanced GIS Lab Center for Information Systems and
More informationFinding Hot Spots in ArcGIS Online: Minimizing the Subjectivity of Visual Analysis. Nicholas M. Giner Esri Parrish S.
Finding Hot Spots in ArcGIS Online: Minimizing the Subjectivity of Visual Analysis Nicholas M. Giner Esri Parrish S. Henderson FBI Agenda The subjectivity of maps What is Hot Spot Analysis? Why do Hot
More informationChallenges in Geocoding Socially-Generated Data
Challenges in Geocoding Socially-Generated Data Jonny Huck (2 nd year part-time PhD student) Duncan Whyatt Paul Coulton Lancaster Environment Centre School of Computing and Communications Royal Wedding
More informationEnabling Web GIS. Dal Hunter Jeff Shaner
Enabling Web GIS Dal Hunter Jeff Shaner Enabling Web GIS In Your Infrastructure Agenda Quick Overview Web GIS Deployment Server GIS Deployment Security and Identity Management Web GIS Operations Web GIS
More informationArcGIS is Advancing. Both Contributing and Integrating many new Innovations. IoT. Smart Mapping. Smart Devices Advanced Analytics
ArcGIS is Advancing IoT Smart Devices Advanced Analytics Smart Mapping Real-Time Faster Computing Web Services Crowdsourcing Sensor Networks Both Contributing and Integrating many new Innovations ArcGIS
More informationGeoSpark: A Cluster Computing Framework for Processing Spatial Data
GeoSpark: A Cluster Computing Framework for Processing Spatial Data Jia Yu School of Computing, Informatics, and Decision Systems Engineering, Arizona State University 699 S. Mill Avenue, Tempe, AZ jiayu2@asu.edu
More informationBentley Map V8i (SELECTseries 3)
Bentley Map V8i (SELECTseries 3) A quick overview Why Bentley Map Viewing and editing of geospatial data from file based GIS formats, spatial databases and raster Assembling geospatial/non-geospatial data
More informationThe Challenge of Geospatial Big Data Analysis
288 POSTERS The Challenge of Geospatial Big Data Analysis Authors - Teerayut Horanont, University of Tokyo, Japan - Apichon Witayangkurn, University of Tokyo, Japan - Shibasaki Ryosuke, University of Tokyo,
More informationOBEUS. (Object-Based Environment for Urban Simulation) Shareware Version. Itzhak Benenson 1,2, Slava Birfur 1, Vlad Kharbash 1
OBEUS (Object-Based Environment for Urban Simulation) Shareware Version Yaffo model is based on partition of the area into Voronoi polygons, which correspond to real-world houses; neighborhood relationship
More informationTOWARDS THE DEVELOPMENT OF A MONITORING SYSTEM FOR PLANNING POLICY Residential Land Uses Case study of Brisbane, Melbourne, Chicago and London
TOWARDS THE DEVELOPMENT OF A MONITORING SYSTEM FOR PLANNING POLICY Residential Land Uses Case study of Brisbane, Melbourne, Chicago and London Presented to CUPUM 12 July 2017 by Claire Daniel Urban Planning/Data
More informationSpatial Data, Spatial Analysis and Spatial Data Science
Spatial Data, Spatial Analysis and Spatial Data Science Luc Anselin http://spatial.uchicago.edu 1 spatial thinking in the social sciences spatial analysis spatial data science spatial data types and research
More informationSpatial Data Mining. Regression and Classification Techniques
Spatial Data Mining Regression and Classification Techniques 1 Spatial Regression and Classisfication Discrete class labels (left) vs. continues quantities (right) measured at locations (2D for geographic
More informationLecture 8. Spatial Estimation
Lecture 8 Spatial Estimation Lecture Outline Spatial Estimation Spatial Interpolation Spatial Prediction Sampling Spatial Interpolation Methods Spatial Prediction Methods Interpolating Raster Surfaces
More informationObjectives Define spatial statistics Introduce you to some of the core spatial statistics tools available in ArcGIS 9.3 Present a variety of example a
Introduction to Spatial Statistics Opportunities for Education Lauren M. Scott, PhD Mark V. Janikas, PhD Lauren Rosenshein Jorge Ruiz-Valdepeña 1 Objectives Define spatial statistics Introduce you to some
More informationInterpolating Raster Surfaces
Interpolating Raster Surfaces You can use interpolation to model the surface of a feature or a phenomenon all you need are sample points, an interpolation method, and an understanding of the feature or
More informationThese modules are covered with a brief information and practical in ArcGIS Software and open source software also like QGIS, ILWIS.
Online GIS Training and training modules covered are: 1. ArcGIS, Analysis, Fundamentals and Implementation 2. ArcGIS Web Data Sharing 3. ArcGIS for Desktop 4. ArcGIS for Server These modules are covered
More informationA REVIEW ON SPATIAL DATA AND SPATIAL HADOOP
International Journal of Latest Trends in Engineering and Technology Vol.(8)Issue(1), pp.545-550 DOI: http://dx.doi.org/10.21172/1.81.071 e-issn:2278-621x A REVIEW ON SPATIAL DATA AND SPATIAL HADOOP Kirandeep
More informationIntroduction to IsoMAP Isoscapes Modeling, Analysis, and Prediction
Introduction to IsoMAP Isoscapes Modeling, Analysis, and Prediction What is IsoMAP To the user, and online workspace for: Accessing, manipulating, and analyzing, and modeling environmental isotope data
More informationComputational Frameworks. MapReduce
Computational Frameworks MapReduce 1 Computational challenges in data mining Computation-intensive algorithms: e.g., optimization algorithms, graph algorithms Large inputs: e.g., web, social networks data.
More informationTHE DESIGN AND IMPLEMENTATION OF A WEB SERVICES-BASED APPLICATION FRAMEWORK FOR SEA SURFACE TEMPERATURE INFORMATION
THE DESIGN AND IMPLEMENTATION OF A WEB SERVICES-BASED APPLICATION FRAMEWORK FOR SEA SURFACE TEMPERATURE INFORMATION HE Ya-wen a,b,c, SU Fen-zhen a, DU Yun-yan a, Xiao Ru-lin a,c, Sun Xiaodan d a. Institute
More informationTypes of spatial data. The Nature of Geographic Data. Types of spatial data. Spatial Autocorrelation. Continuous spatial data: geostatistics
The Nature of Geographic Data Types of spatial data Continuous spatial data: geostatistics Samples may be taken at intervals, but the spatial process is continuous e.g. soil quality Discrete data Irregular:
More informationComputational Frameworks. MapReduce
Computational Frameworks MapReduce 1 Computational complexity: Big data challenges Any processing requiring a superlinear number of operations may easily turn out unfeasible. If input size is really huge,
More informationProject 2: Hadoop PageRank Cloud Computing Spring 2017
Project 2: Hadoop PageRank Cloud Computing Spring 2017 Professor Judy Qiu Goal This assignment provides an illustration of PageRank algorithms and Hadoop. You will then blend these applications by implementing
More informationUsing R for Iterative and Incremental Processing
Using R for Iterative and Incremental Processing Shivaram Venkataraman, Indrajit Roy, Alvin AuYoung, Robert Schreiber UC Berkeley and HP Labs UC BERKELEY Big Data, Complex Algorithms PageRank (Dominant
More informationImproving Geographical Data Finder Using Tokenize Approach from GIS Map API
Improving Geographical Data Finder Using Tokenize Approach from GIS Map API Antveer Kaur Department of computer science Banasthali University, Jaipur, Rajasthan, India bntsnghbrr940@gmail.com Shweta Kumari
More informationLecture 3: Exploratory Spatial Data Analysis (ESDA) Prof. Eduardo A. Haddad
Lecture 3: Exploratory Spatial Data Analysis (ESDA) Prof. Eduardo A. Haddad Key message Spatial dependence First Law of Geography (Waldo Tobler): Everything is related to everything else, but near things
More informationGraduate Seminar & Student Technical Conference
Graduate Seminar & Student Technical Conference Tuesday November 26 th, 2013 & Monday, December 2 nd, 2013 Department of Geodesy and Geomatics Engineering University of New Brunswick The Department would
More informationLecture 3: Exploratory Spatial Data Analysis (ESDA) Prof. Eduardo A. Haddad
Lecture 3: Exploratory Spatial Data Analysis (ESDA) Prof. Eduardo A. Haddad Key message Spatial dependence First Law of Geography (Waldo Tobler): Everything is related to everything else, but near things
More informationEnabling ENVI. ArcGIS for Server
Enabling ENVI throughh ArcGIS for Server 1 Imagery: A Unique and Valuable Source of Data Imagery is not just a base map, but a layer of rich information that can address problems faced by GIS users. >
More informationIntroducing GIS analysis
1 Introducing GIS analysis GIS analysis lets you see patterns and relationships in your geographic data. The results of your analysis will give you insight into a place, help you focus your actions, or
More informationAdministering your Enterprise Geodatabase using Python. Jill Penney
Administering your Enterprise Geodatabase using Python Jill Penney Assumptions Basic knowledge of python Basic knowledge enterprise geodatabases and workflows You want code Please turn off or silence cell
More informationConcepts and Applications of Kriging
Esri International User Conference San Diego, California Technical Workshops July 24, 2012 Concepts and Applications of Kriging Konstantin Krivoruchko Eric Krause Outline Intro to interpolation Exploratory
More informationHow does ArcGIS Server integrate into an Enterprise Environment? Willy Lynch Mining Industry Specialist ESRI, Denver, Colorado USA
How does ArcGIS Server integrate into an Enterprise Environment? Willy Lynch Mining Industry Specialist ESRI, Denver, Colorado USA wlynch@esri.com ArcGIS Server Technology Transfer 1 Agenda Who is ESRI?
More informationGeodatabase Programming with Python John Yaist
Geodatabase Programming with Python John Yaist DevSummit DC February 26, 2016 Washington, DC Target Audience: Assumptions Basic knowledge of Python Basic knowledge of Enterprise Geodatabase and workflows
More informationPython Raster Analysis. Kevin M. Johnston Nawajish Noman
Python Raster Analysis Kevin M. Johnston Nawajish Noman Outline Managing rasters and performing analysis with Map Algebra How to access the analysis capability - Demonstration Complex expressions and optimization
More informationESRI Delivering geographic information systems to millions of users
Using Web GIS to Track Government Spending and Performance Eric Floss - ESRI April 12, 2010 ESRI Delivering geographic information systems to millions of users GIS Is Changing Everything How We Reason
More informationTRAITS to put you on the map
TRAITS to put you on the map Know what s where See the big picture Connect the dots Get it right Use where to say WOW Look around Spread the word Make it yours Finding your way Location is associated with
More informationPoint data in mashups: moving away from pushpins in maps
Point data in mashups: moving away from pushpins in maps Aidan Slingsby, Jason Dykes, Jo Wood gicentre, City University London Department of Information Science, City University London, Northampton Square,
More informationA Reconfigurable Quantum Computer
A Reconfigurable Quantum Computer David Moehring CEO, IonQ, Inc. College Park, MD Quantum Computing for Business 4-6 December 2017, Mountain View, CA IonQ Highlights Full Stack Quantum Computing Company
More informationSTATISTICAL PERFORMANCE
STATISTICAL PERFORMANCE PROVISIONING AND ENERGY EFFICIENCY IN DISTRIBUTED COMPUTING SYSTEMS Nikzad Babaii Rizvandi 1 Supervisors: Prof.Albert Y.Zomaya Prof. Aruna Seneviratne OUTLINE Introduction Background
More informationAnalysis of Bank Branches in the Greater Los Angeles Region
Analysis of Bank Branches in the Greater Los Angeles Region Brian Moore Introduction The Community Reinvestment Act, passed by Congress in 1977, was written to address redlining by financial institutions.
More informationGeography 38/42:376 GIS II. Topic 1: Spatial Data Representation and an Introduction to Geodatabases. The Nature of Geographic Data
Geography 38/42:376 GIS II Topic 1: Spatial Data Representation and an Introduction to Geodatabases Chapters 3 & 4: Chang (Chapter 4: DeMers) The Nature of Geographic Data Features or phenomena occur as
More informationIntroduction to ArcGIS GeoAnalytics Server. Sarah Ambrose & Noah Slocum
Introduction to ArcGIS GeoAnalytics Server Sarah Ambrose & Noah Slocum Agenda Overview Analysis Capabilities + Demo Deployment and Configuration Questions ArcGIS GeoAnalytics Server uses the power of distributed
More informationUSING GIS IN WATER SUPPLY AND SEWER MODELLING AND MANAGEMENT
USING GIS IN WATER SUPPLY AND SEWER MODELLING AND MANAGEMENT HENRIETTE TAMAŠAUSKAS*, L.C. LARSEN, O. MARK DHI Water and Environment, Agern Allé 5 2970 Hørsholm, Denmark *Corresponding author, e-mail: htt@dhigroup.com
More informationUsing netcdf and HDF in ArcGIS. Nawajish Noman Dan Zimble Kevin Sigwart
Using netcdf and HDF in ArcGIS Nawajish Noman Dan Zimble Kevin Sigwart Outline NetCDF and HDF in ArcGIS Visualization and Analysis Sharing Customization using Python Demo Future Directions Scientific Data
More informationGENERALIZATION IN THE NEW GENERATION OF GIS. Dan Lee ESRI, Inc. 380 New York Street Redlands, CA USA Fax:
GENERALIZATION IN THE NEW GENERATION OF GIS Dan Lee ESRI, Inc. 380 New York Street Redlands, CA 92373 USA dlee@esri.com Fax: 909-793-5953 Abstract In the research and development of automated map generalization,
More informationFUNDAMENTALS OF GEOINFORMATICS PART-II (CLASS: FYBSc SEM- II)
FUNDAMENTALS OF GEOINFORMATICS PART-II (CLASS: FYBSc SEM- II) UNIT:-I: INTRODUCTION TO GIS 1.1.Definition, Potential of GIS, Concept of Space and Time 1.2.Components of GIS, Evolution/Origin and Objectives
More informationGraph Analysis Using Map/Reduce
Seminar: Massive-Scale Graph Analysis Summer Semester 2015 Graph Analysis Using Map/Reduce Laurent Linden s9lalind@stud.uni-saarland.de May 14, 2015 Introduction Peta-Scale Graph + Graph mining + MapReduce
More informationBentley Map Advancing GIS for the World s Infrastructure
Bentley Map Advancing GIS for the World s Infrastructure Presentation Overview Why would you need Bentley Map? What is Bentley Map? Where is Bentley Map Used? Why would you need Bentley Map? Because your
More informationESRI Survey Summit August Clint Brown Director of ESRI Software Products
ESRI Survey Summit August 2006 Clint Brown Director of ESRI Software Products Cadastral Fabric How does Cadastral fit with Survey? Surveyors process raw field observations Survey measurements define high-order
More informationAnimating Maps: Visual Analytics meets Geoweb 2.0
Animating Maps: Visual Analytics meets Geoweb 2.0 Piyush Yadav 1, Shailesh Deshpande 1, Raja Sengupta 2 1 Tata Research Development and Design Centre, Pune (India) Email: {piyush.yadav1, shailesh.deshpande}@tcs.com
More informationIE Advanced Simulation Experiment Design and Analysis. Hong Wan Purdue University
IE Advanced Simulation Experiment Design and Analysis Hong Wan Purdue University Spring, 2007, based on Prof. Barry L. Nelson s notes for IEMS465, Northwestern University 1 SIMULATION OVERVIEW One view
More informationLUIZ FERNANDO F. G. DE ASSIS, TÉSSIO NOVACK, KARINE R. FERREIRA, LUBIA VINHAS AND ALEXANDER ZIPF
A discussion of crowdsourced geographic information initiatives and big Earth observation data architectures for land-use and land-cover change monitoring LUIZ FERNANDO F. G. DE ASSIS, TÉSSIO NOVACK, KARINE
More informationIntroduction to Portal for ArcGIS. Hao LEE November 12, 2015
Introduction to Portal for ArcGIS Hao LEE November 12, 2015 Agenda Web GIS pattern Product overview Installation and deployment Security and groups Configuration options Portal for ArcGIS + ArcGIS for
More informationIntroduction to ArcGIS Server Development
Introduction to ArcGIS Server Development Kevin Deege,, Rob Burke, Kelly Hutchins, and Sathya Prasad ESRI Developer Summit 2008 1 Schedule Introduction to ArcGIS Server Rob and Kevin Questions Break 2:15
More informationDEKDIV: A Linked-Data-Driven Web Portal for Learning Analytics Data Enrichment, Interactive Visualization, and Knowledge Discovery
DEKDIV: A Linked-Data-Driven Web Portal for Learning Analytics Data Enrichment, Interactive Visualization, and Knowledge Discovery Yingjie Hu, Grant McKenzie, Jiue-An Yang, Song Gao, Amin Abdalla, and
More informationUrban GIS for Health Metrics
Urban GIS for Health Metrics Dajun Dai Department of Geosciences, Georgia State University Atlanta, Georgia, United States Presented at International Conference on Urban Health, March 5 th, 2014 People,
More informationSpatial Data Science. Soumya K Ghosh
Workshop on Data Science and Machine Learning (DSML 17) ISI Kolkata, March 28-31, 2017 Spatial Data Science Soumya K Ghosh Professor Department of Computer Science and Engineering Indian Institute of Technology,
More informationDANIEL WILSON AND BEN CONKLIN. Integrating AI with Foundation Intelligence for Actionable Intelligence
DANIEL WILSON AND BEN CONKLIN Integrating AI with Foundation Intelligence for Actionable Intelligence INTEGRATING AI WITH FOUNDATION INTELLIGENCE FOR ACTIONABLE INTELLIGENCE in an arms race for artificial
More informationGIS Spatial Statistics for Public Opinion Survey Response Rates
GIS Spatial Statistics for Public Opinion Survey Response Rates July 22, 2015 Timothy Michalowski Senior Statistical GIS Analyst Abt SRBI - New York, NY t.michalowski@srbi.com www.srbi.com Introduction
More informationSPATIAL DATA MINING. Ms. S. Malathi, Lecturer in Computer Applications, KGiSL - IIM
SPATIAL DATA MINING Ms. S. Malathi, Lecturer in Computer Applications, KGiSL - IIM INTRODUCTION The main difference between data mining in relational DBS and in spatial DBS is that attributes of the neighbors
More informationGeographers Perspectives on the World
What is Geography? Geography is not just about city and country names Geography is not just about population and growth Geography is not just about rivers and mountains Geography is a broad field that
More informationSpatial Analysis with Web GIS. Rachel Weeden
Spatial Analysis with Web GIS Rachel Weeden Agenda Subhead goes here Introducing ArcGIS Online Spatial Analysis Workflows Scenarios Other Options Resources ArcGIS is a Platform Making mapping and analytics
More informationClustering algorithms distributed over a Cloud Computing Platform.
Clustering algorithms distributed over a Cloud Computing Platform. SEPTEMBER 28 TH 2012 Ph. D. thesis supervised by Pr. Fabrice Rossi. Matthieu Durut (Telecom/Lokad) 1 / 55 Outline. 1 Introduction to Cloud
More informationIntroduction to Portal for ArcGIS
Introduction to Portal for ArcGIS Derek Law Product Management March 10 th, 2015 Esri Developer Summit 2015 Agenda Web GIS pattern Product overview Installation and deployment Security and groups Configuration
More informationHarvard Center for Geographic Analysis Geospatial on the MOC
2017 Massachusetts Open Cloud Workshop Boston University Harvard Center for Geographic Analysis Geospatial on the MOC Ben Lewis Harvard Center for Geographic Analysis Aaron Williams MapD Small Team Supporting
More informationEsri UC2013. Technical Workshop.
Esri International User Conference San Diego, California Technical Workshops July 9, 2013 CAD: Introduction to using CAD Data in ArcGIS Jeff Reinhart & Phil Sanchez Agenda Overview of ArcGIS CAD Support
More informationWelcome to NR502 GIS Applications in Natural Resources. You can take this course for 1 or 2 credits. There is also an option for 3 credits.
Welcome to NR502 GIS Applications in Natural Resources. You can take this course for 1 or 2 credits. There is also an option for 3 credits. The 1st credit consists of a series of readings, demonstration,
More informationIntroduction to the 176A labs and ArcGIS
Introduction to the 176A labs and ArcGIS Acknowledgement: Slides by David Maidment, U Texas-Austin and Francisco Olivera (TAMU) Purpose of the labs Hands-on experience with one software pakage Introduction
More informationGIS CONCEPTS ARCGIS METHODS AND. 2 nd Edition, July David M. Theobald, Ph.D. Natural Resource Ecology Laboratory Colorado State University
GIS CONCEPTS AND ARCGIS METHODS 2 nd Edition, July 2005 David M. Theobald, Ph.D. Natural Resource Ecology Laboratory Colorado State University Copyright Copyright 2005 by David M. Theobald. All rights
More informationWelcome! Power BI User Group (PUG) Copenhagen
Welcome! Power BI User Group (PUG) Copenhagen Making Maps in Power BI Andrea Martorana Tusa BI Specialist Welcome to Making maps in Power BI Who am I? First name: Andrea. Last name: Martorana Tusa. Italian,
More informationDr Arulsivanathan Naidoo Statistics South Africa 18 October 2017
ESRI User Conference 2017 Space Time Pattern Mining Analysis of Matric Pass Rates in Cape Town Schools Dr Arulsivanathan Naidoo Statistics South Africa 18 October 2017 Choose one of the following Leadership
More informationYYT-C3002 Application Programming in Engineering GIS I. Anas Altartouri Otaniemi
YYT-C3002 Application Programming in Engineering GIS I Otaniemi Overview: GIS lectures & exercise We will deal with GIS application development in two lectures. Because of the versatility of GIS data models
More informationBentley Geospatial update
Bentley Geospatial update Tallinna, 01.11.2007 Timo Tuukkanen, Bentley Systems Issues today Bentley Map available now detailed introduction to Bentley Map Bentley Geo Web Publisher Bentley Web GIS update,
More informationCreation of an Internet Based Indiana Water Quality Atlas (IWQA)
Department of Environmental Management Creation of an Internet Based Water Quality Atlas (IWQA) May 4, 2005 IUPUI 1200 Waterway Blvd., Suite 100 polis, 46202-5140 Water Quality Atlas John Buechler, Neil
More informationPortal for ArcGIS: An Introduction
Portal for ArcGIS: An Introduction Derek Law Esri Product Management Esri UC 2014 Technical Workshop Agenda Web GIS pattern Product overview Installation and deployment Security and groups Configuration
More information