Output: -Observed Mean Distance -Expected Mean Distance - Nearest Neighbor Index -Graphic report - Test variables:
|
|
- Mariah McBride
- 5 years ago
- Views:
Transcription
1 Clustering: global indexes (to measure the global degree of clustering for the whole set of events) -> methods based on quadrats (joint count) vs. on distances AVERAGE NEAREST NEIGHBOUR: the distance between events is less (clustering) or more (pattern inibitorio) of the expected distance in case of complete spatial randomness? (Clark-Evans, 50s) Nearest neighbour ratio = observed mean distance / expected mean distance (CSR) -> Input: Points: unweighted (= 1) / Projected coordinate system! (Polygons and lines: convert into points with x, y = centroids) Output: -Observed Mean Distance -Expected Mean Distance - Nearest Neighbor Index -Graphic report - Test variables: -> Toolbox / Spatial statistics / Analyzing patterns p-value: probabilty of the spatial distribution to be random z-score: standard deviation of the real values from expected values - measure the ANN for firms within the GRA (selection of rm_immig.shp) Bivariate point patterns : co-agglomeration, co-location, competition/cooperation, related variety: Bivariate/Cross K function, Pairwise interaction point process.. Crimestat, R.. Risk-Adjusted Nearest Neighbor Hierarchical Spatial Clustering (Rnnh) (Crimestat) Clustering index in which the probability of identifying clusters for certain categories of events is assessed in relation to the spatial distribution of all events, by using an interpolation between the (kernel) density surfaces of the primary file (e.g. crimes) and the secondary files (eg. population) Multi-variate point patterns ( ). -> Bivariate point patterns analysis for each couple of patterns
2 Clustering processes at different scales In the figure: 10 clusters of first order, 8 clusters of second order, 3 of third order, and so on.. NEAREST NEIGHBOR HIERARCHICAL CLUSTER: constantdistance clustering routine for non-weighted events, hierarchical: first order clusters are considered points which may cluster at the second order and so on, until criteria are satisfied (for each order). RIPLEY'S K-FUNCTION: To identify clustered/inhibitory/random point patterns t different scales/distances between points (Ripley 1976, 1981 Spatial statistics ) Two uses: to confirm/reject the null/random hypothesis at various scales/distances + to dientify the scale/distance where the clustering/inhibition is more intense/weak K = expected number of events / real number of events In case of complete spatial randomness: K(d) = πd2 : Output (dbf, shp): n. cluster, mean center, deviational ellipse and convex hull (spezzata) of points beloning to each cluster, area and cluster density. Results are heavily influenced by the identified first order clusters
3 Linearization of the K function: L function (Besag 1977) In case of complete spatial randomness: L(d) = d (ArcGIS): Or L(d) = 0 -> (Crimestat) K value (clustering) Confidence interval Expected value Confidence interval Ripley s K Lower and upper confidence envelops: beyond which results may be considered significant Confidence envelops are estimated thanks to the reiteration of a Montecarlo simulation (Crimestat: 100 simulations; ArcGIS: 0 / 9%, 99% o 99,9% of the confidence interval). Corollary: simulations work better if the number of points is not small (> 100) Spatial statistics / Analyzing patterns / Multi-Distance Spatial Cluster Analysis Maximum distance Crimestat: SQRT(A)/3 ArcGIS:? Distance ranges Crimestat: 100 ArcGIS: from 1 to 100 (or: beginning distance + distance increment )
4 (K function) other parameters: Weight field: default: 1, fixed: weight (number of events at each point). The weighted estimation gives different results (clustering is likely to be higher)!: points cannot have distance=0* Problems with the analysis of spatial data #1: -Study area extension (if too small, the analysis may not include elements which are important to provide an exhaustive explanation. If too big, the spatial distribution pattern may be due of a diversity of processes which have nothing to do with what we want to explain. Example: suburban, scattered and low density urban areas). Is an area sensitive tool: results are influenced by the area extension Study area methods: Default: minimum enclosing rectangle User provided: via polygonial layer -> «Study Area» -> reduce the size of the area Creat a mask of the area within the GRA (ring road) by selecting (manually) the zone urbanistiche within the GRA and exporting the selection as mask_area.shp Specific problems in the analysis of spatial data #2: Boundary problems: given the probability of non observed events beyond the study area s boundaries (with a similar or dissimilar spatial distribution), con distribuzione spaziale simile o dissimile), clustering near the boundaries is under-estimated. Boundary correction methods: NONE: because events are only to be found within the boundaries. Or because the point layer is wider than the study area: points beyond the boundaries of the study area are used for estimating the K function (!!!) SIMULATE_OUTER_BOUNDARY_VALUES: simulate a «mirrored» distribution of points beyond the bounadries REDUCE_ANALYSIS_AREA: reduces the study area. RIPLEY'S_EDGE_CORRECTION_FORMULA: those points whose distance from the boundary is smaller than to other points, are weighted more (good only for non irregular study areas) Output: table(+ Display result graphically): - ExpK (K expected value in case of CSR), - Envelopes (confidence intervals), - ObservedK (value of K) - DiffK (ObservedK-ExpK) Cautions: - Works better for clustered than for inhibitory processes - It s mainly a tool for identifying second-order clusters, i.e. localized clusters, intra-regional scales or medium distances. - Not reliable for small numbers of events (>30, >100) - Not reliable for strongly irregular areas (if it s not possible to solve adequately the boundary problem)
5 Measure the Ripley K function for the distribution of firms owned by foreigners within the GRA (ring road) Space-time Ripley s K Input: vv/rm_immig_wdata.shp (Confidence envelop: 0 permutations)* Click Display results graphically Distance bands: 20 Weight field: CNT Beginning distance: 250 Distance increments: 250 Boundary correction method: NONE, because: Study area: User provided = dropbox/corsimemotef/lezgis16/4/mask.shp Verify the graphic and table (diff) output Taxonomy of spatial analysis tools (in ArcGIS and Crimestat) Of events (spatial distribution) Of intensities (spatial association) Global indexes of spatial autocorrelation Global indexes Average nearest neighbour (Multi scale) K Ripley Global indexes of autocorrelation: Moran s I Geary s C Kernel density maps Local indicators of spatial association (LISA): Local indexes Nearest neighbour hierarchical clustering Local Anselin of Moran s I (Cluster and outlier analys.) Risk Adjusted Nearest Neighbor Hierarchical Clustering Getis Ord Gi (Hot spot analysis)
6 3. Global indexes of spatial AUTOCORRELATION First law of geography (Tobler) = "Everything is related to everything else, but near things are more related than distant things." It s a form of spatial dependence (positive or negative): the degree to which nearby features are similar or dissimilar*, vs. an hypothesis of complete spatial randomness. - Similar to time series analysis, but both proximity AND direction/position (2D) Why to estimate the degree of spatial autocorrelation: - To understand the process (or the variety of processes..) which explain the geographical distribution of intensities - To estimate the degree to which nearby features potentially influence each other (=interaction, interdependence, attraction, contagion, clustering, segregation, etc ) - To verify the degree to which the observed variables are (not) statistically indipendent (eg. autocorrelation reduces the dataset s information content or obscures what is specific about each area, because intensities in one area are partially influenced by what is happening nearby) - (Eg. to test the spatial autocorrelation of models residuals) - (Eg. to assist in the identification of the spatial sample size) Exploratory Spatial Data Analysis (and mapping) vs. Modelling (formal verification and testing of hypothesis) Spatial auto-correlation: global indexes Moran s I Spatial autocorrelation (MORAN S I): Global co-variance index adapted from the analysis of the memory effect in time series (Moran 40s, Whittle 1954). Measures the gobal degree of similarity between the (upper and lower) intensities (-/+) of nearby features Xi X = intensity in point Xi average intensity (Xi-X)(Xj-X): Cross-product, high if values are similar Wij: spatial weights (/influences) matrix * Clustered/high autocorrelation if I is high (I>0), dispersed/low autocorrelation if I is low (I<0), vs. the CSR hypothesis Iexp=-[1/(n-1)]
7 Spatial statistics / Analyzing patterns / Spatial autocorrelation (Moran s I) Conceptualization of spatial relationships: Inverse distance (squared): spatial relationships between features are inversely proportional to their (squared) distance. Computational problems with small distances (crimestat: adjust for small distances ) and no threshold (n to n) Fixed distance band: within the threshold (band) any feature weights 1. Appropriate in the case of non-uniform polygons, and for large point datasets. Zone of indifference: neighbors (or features within the distance threshold) weight 1. Other features weight is inversely proportional to their distance. Appropriate as above, when the influence of distant features is relevant. Computational problems. Polygon contiguity (adjacency!): considersonlybordering features (1 if bordering, 0 all the others). Appropriate only for regular polygons (original Moran s I. Generalized by Cliff and Ord Widely used in spatial econometrics) Conceptualization of spatial relationships (2): Spatial statistics / Modeling spatial relationships / Generate spatial weight matrix Distance Band or Threshold Distance (mostly for large datasets): threshold beyond which influence is null (with inverse distance = i) 0: all features are considered; ii) Empty: applies a default threshold distance (min distance at which any feature has a neighbour); iii) defined by the user Weights Matrix from file: uses a spatial weight matrix file (.swm) created/adapted by the user Spatial weight matrix Table in.swm format in which any cell includes an expression of the distance, time, cost, influence, spatial relationship between any couple of features (presence/absence or intensity)
8 Conceptualization of spatial relationships (3): INVERSE_DISTANCE: ( ) + Exponent (!), eg. 2 FIXED_DISTANCE: ( ) K_NEAREST_NEIGHBORS: considers only a K number of the most proximate features CONTIGUITY_EDGES_ONLY: considers only features which share a boundary ( rooke ) CONTIGUITY_EDGES_CORNERS: considers only features which share a boundary and/or vertex ( queen ) ROW STANDARDIZATION: values in the spatial weight matrix are standardized in order for their sum to be = 1. To avoid the indexes to be influenced by the different number of nearby features: appropriate in the case of sample data and compulsory in the case of polygon contiguity, because (irregular) polygons have a different and arbitrary number of bordering features. Test variables: Z-score = standard deviation / p-value DELAUNAY_TRIANGULATION: create overlapping triangles connecting polygons centroids, and considers only features which share a triangle s vertex.. CONVERT_TABLE: to specify spatial relationships in a table [Convert spatial weight matrix to table (utilities)] Normality: the Z-score displays a normal distribution? Output: -Moran s index - Expected index - Variance - Z-score e p-value Cautions: -Significant only above a certain number of features (> 30) Vs. Geary s autocorrelation index (Moran is more robust) HIGH/LOW CLUSTERING (Getis & Ord). The probability for high or low values (+) to be clustered or dispersed (similar to Average Nearest Neighbour) LAB: a spatial analysis of public schools quality in Rome = to what extent school quality depends on the context? = what is the degree of spatial autocorrelation of school quality? Input: spatial17/addxy/schools_roma_xy_dprv.dbf = a table with XY coordinates of all primary and secondary schools in Rome, including a (normalized) «deprivation index» f(dropouts, repetitions, students to teachers ratio, students per classroom, foreign students ratio). 1. Georefer the Schools_Roma dbf using Add XY data + export the data output setting its coordinate system «as the data frame» 2. Estimate the global autocorrelation of the normalized deprivation index, using arctoolbox/spatial statistics/analyzing patterns/moran s I, and setting all the parameters..
9 LAB: what is the degree of spatial autocorrelation of school quality? 2. Estimate the global autocorrelation of the normalized deprivation index, using the Moran s I. Parameters: Input feature class: schools Input field = «DPRV_NORM» Conceptualization of spatial relationships:? Row standardization:? Threshold distance: meters Generate report -> Verify the graphic report and test variables: what is the result? Is this statistically significant? Do high or low quality schools cluster in certain zones, and where? -> Local indicators of spatial autocorrelation 4. LOCAL INDEX OF SPATIAL AUTOCORRELATION To measure the degree of autocorrelation for each geographical feature (where and which features?) Local indexes of spatial association/autocorrelation Anselin local of Moran s I (Anselin L. 1995, Local indicators of spatial association LISA. Geographical Analysis 27, ) To attribute to each feature a degree of high/low autocorrelation based on its (high/low) intensity being similar/dissimilar to nearby features Z: intensity, S: variance, W: spatial weight matrix
10 Input: polygons (crimestat) and points(arcgis) Output: Grado di segregazione tra aree a prevalenza di imprenditori cinesi e aree a prevalenza di imprenditori italiani Contributo Anselin locale alla local segregazione of Moran s tra aree a I prevalente of the presenza distance of unità condotte entrepreneurs da imprenditori from cinesi o their italiani country of origin Cluster type (COType) identifies (and renders): - Features which are part of high (HH) or low (LL) values clusters, because nearby features have similar values, and are statistical significant (positive and high z-score). - outlier features, with high or low values, surrounded by features with low (HL) or high (LH) values, and are statistical significant (low and negative z-score) Spatial statistics / Mapping clusters / Cluster and outlier analysis LAB: a spatial analysis of public schools quality in Rome = To what extent school quality depends on the context? Do high or low quality schools cluster in certain zones, and where? 1. Identify and render those schools which are part of clusters of nearby low or high quality schools using arctoolbox/spatial statistics/mapping clusters/cluster and outlier analysis Input: Schools with data shapefile, input field: DPRV_NORM Spatial relationships: Inverse distance -> Modify the symbology of the ouput layer in order to visualize only the schools in clusters of high or low and significant spatial autocorrelation values -> Open and verify the ouput layer attribute table -> In a copied layer, represent the value of the index (L_Milndex) disregarding of the degree of significance -> Check(and trytomakesense) ofoutliers
11 Local indexes of spatial autocorrelation (2): Getis-Ord Gi, high/low clustering (Hot Spot Analysis) Identifies features which are part of hot spots : areas with unusual clustering of high or low values (Cliff & Ord, Spatial autocorrelation, 1973), based on the value of the GiZScore (categorized according to the standard deviation: the higher the GiZscore, the more nearby features have high values, and viceversa. (You may do a density map of using the Z-Score as weight) Cautions: - reliable only with large dataset (>30 features) - test problems (the significativity test is based on global indexes of spatial autocorrelation)
12 LAB: 1. measure the (global) spatial autocorrelation of the distribution of all foreigners (and of Chinese) in Rome s zone urbanistiche and 2. identify (local) clusters of contigous zones with an high or low density of foreigners (and of Chinese) Spatial interpolation: to obtain surface data from point sample observations Input: spatial17/vv/zurb_wdata.shp Input field:? Arctoolbox tools? Conceptualization of spatial relationship:? Standardization:? Threshold dist.:? Results? Spatial interpolation: INVERSE DISTANCE WEIGHTED Spatial interpolation: KRIGING
13 Spatial interpolation: KRIGING (..more) problems with the analysis of spatial data Example of the modifiable area unit problem (MAUP): Gerrymandering (distortions due to the shape of electoral partitions) The modifiable area unit problem (MAUP): any geographical discontinuity is artificial, (more or the less) arbitrary, modifiable, and influences the results and explanation - Scale problem, f(spatial resolution). E.g. statistical relations are stronger the lower is the degree of spatial resolution, because variance is lower = the more we aggregate data, the stronger they correlate. The more we disaggregate date (and increase spatial resolution), the more the variance and the risk this is due to chance or mistakes - Zoning problem, f(geodata geometry), for any given number of zones, results are influenced by their shape The urban (and mostly liberal) concentration of Columbus, Ohio, located at the center of the map, is split into thirds, each segment then attached to - and outnumbered by - largely conservative suburbs. -Non uniformity: a uniform geographical partition, will be non uniform in terms of statistical attributes, and viceversa (e.g. population). Data in less dense areas are less reliable. -Irregularity, vs. compactness (e.g. administrative divisions)
14 And.. - Ecological fallacy: the results of aggregate analysis cannot be attributed to each individuals, or to higher scales (the rate of suicides is higher where more catholics live = catholics more keen to suicide?) - Outliers: very frequent in spatial data. The higher the spatial resolution of data, the more the probability of outliears. - Geodata quality (accuracy, completeness, consistence, resolution..) Specific problems: measurement mistakes are not indipendent (e.g. population subtracted from an area is attributed to the neighbour). The more dense the areas, the lower the data quality (but the lower the distortion due to measurement mistakes) - Categorial data: spatial analysis tools for categorial data are still largely missing - Coincident locations (distance = 0) -> collect events (to turn coincident points of unique events into weighted points) ArcGIS desktop/online Help.. ArcGIS desktop/online Help (2) Help!!! arcgis.com esri.com/en/ esri.com esri/arcgis/
15
Introduction to spatial data analysis
Introduction to spatial data analysis 3 Scuola di Dottorato in Economia Sapienza 2018 Instructors: Filippo Celata (and Luca Salvati) http://www.memotef.uniroma1.it/node/6524 Spatial statistics: - f(location,
More informationIntroduction to spatial data analysis
Introduction to spatial data analysis 2 Doctoral School in Economics (+EDSD), La Sapienza, 2017 Instructors: Filippo Celata (and Luca Salvati) http://www.memotef.uniroma1.it/node/6524 Associate external
More informationSpatial Analysis I. Spatial data analysis Spatial analysis and inference
Spatial Analysis I Spatial data analysis Spatial analysis and inference Roadmap Outline: What is spatial analysis? Spatial Joins Step 1: Analysis of attributes Step 2: Preparing for analyses: working with
More informationExploratory Spatial Data Analysis (ESDA)
Exploratory Spatial Data Analysis (ESDA) VANGHR s method of ESDA follows a typical geospatial framework of selecting variables, exploring spatial patterns, and regression analysis. The primary software
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 informationSpatial Regression. 1. Introduction and Review. Luc Anselin. Copyright 2017 by Luc Anselin, All Rights Reserved
Spatial Regression 1. Introduction and Review Luc Anselin http://spatial.uchicago.edu matrix algebra basics spatial econometrics - definitions pitfalls of spatial analysis spatial autocorrelation spatial
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 informationGIS CONFERENCE MAKING PLACE MATTER Decoding Health Data with Spatial Statistics
esri HEALTH AND HUMAN SERVICES GIS CONFERENCE MAKING PLACE MATTER Decoding Health Data with Spatial Statistics Flora Vale Jenora D Acosta Wait a minute Wait a minute Where is Lauren?? Wait a minute Where
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 informationLab #3 Background Material Quantifying Point and Gradient Patterns
Lab #3 Background Material Quantifying Point and Gradient Patterns Dispersion metrics Dispersion indices that measure the degree of non-randomness Plot-based metrics Distance-based metrics First-order
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 information2/7/2018. Module 4. Spatial Statistics. Point Patterns: Nearest Neighbor. Spatial Statistics. Point Patterns: Nearest Neighbor
Spatial Statistics Module 4 Geographers are very interested in studying, understanding, and quantifying the patterns we can see on maps Q: What kinds of map patterns can you think of? There are so many
More informationSpatial Autocorrelation
Spatial Autocorrelation Luc Anselin http://spatial.uchicago.edu spatial randomness positive and negative spatial autocorrelation spatial autocorrelation statistics spatial weights Spatial Randomness The
More informationKAAF- GE_Notes GIS APPLICATIONS LECTURE 3
GIS APPLICATIONS LECTURE 3 SPATIAL AUTOCORRELATION. First law of geography: everything is related to everything else, but near things are more related than distant things Waldo Tobler Check who is sitting
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 informationThe Use of Spatial Weights Matrices and the Effect of Geometry and Geographical Scale
The Use of Spatial Weights Matrices and the Effect of Geometry and Geographical Scale António Manuel RODRIGUES 1, José António TENEDÓRIO 2 1 Research fellow, e-geo Centre for Geography and Regional Planning,
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 informationGeoprocessing Tools at ArcGIS 9.2 Desktop
Geoprocessing Tools at ArcGIS 9.2 Desktop Analysis Tools Analysis Tools \ Extract Clip Analysis Tools \ Extract Select Analysis Tools \ Extract Split Analysis Tools \ Extract Table Select Analysis Tools
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 informationWhat s special about spatial data?
What s special about spatial data? Road map Geographic Information analysis The need to develop spatial thinking Some fundamental geographic concepts (PBCS) What are the effects of space? Spatial autocorrelation
More informationTemporal vs. Spatial Data
Temporal vs. Spatial Data Temporal 1 dimensional Units: day, week, month Lag: t, t-1, t-2 Durbin-Watson Spatial 2-3 dimensional Units: county, mile, region Lag: near neighbor, networks (?) Moran s I Differencing
More informationLecture 5 Geostatistics
Lecture 5 Geostatistics Lecture Outline Spatial Estimation Spatial Interpolation Spatial Prediction Sampling Spatial Interpolation Methods Spatial Prediction Methods Interpolating Raster Surfaces with
More informationIntroduction to Spatial Statistics and Modeling for Regional Analysis
Introduction to Spatial Statistics and Modeling for Regional Analysis Dr. Xinyue Ye, Assistant Professor Center for Regional Development (Department of Commerce EDA University Center) & School of Earth,
More informationSPACE Workshop NSF NCGIA CSISS UCGIS SDSU. Aldstadt, Getis, Jankowski, Rey, Weeks SDSU F. Goodchild, M. Goodchild, Janelle, Rebich UCSB
SPACE Workshop NSF NCGIA CSISS UCGIS SDSU Aldstadt, Getis, Jankowski, Rey, Weeks SDSU F. Goodchild, M. Goodchild, Janelle, Rebich UCSB August 2-8, 2004 San Diego State University Some Examples of Spatial
More informationLocal Spatial Autocorrelation Clusters
Local Spatial Autocorrelation Clusters Luc Anselin http://spatial.uchicago.edu LISA principle local Moran local G statistics issues and interpretation LISA Principle Clustering vs Clusters global spatial
More informationAn Introduction to Pattern Statistics
An Introduction to Pattern Statistics Nearest Neighbors The CSR hypothesis Clark/Evans and modification Cuzick and Edwards and controls All events k function Weighted k function Comparative k functions
More informationSpatial Analysis 2. Spatial Autocorrelation
Spatial Analysis 2 Spatial Autocorrelation Spatial Autocorrelation a relationship between nearby spatial units of the same variable If, for every pair of subareas i and j in the study region, the drawings
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 informationExploratory Spatial Data Analysis (And Navigating GeoDa)
Exploratory Spatial Data Analysis (And Navigating GeoDa) June 9, 2006 Stephen A. Matthews Associate Professor of Sociology & Anthropology, Geography and Demography Director of the Geographic Information
More informationGIST 4302/5302: Spatial Analysis and Modeling
GIST 4302/5302: Spatial Analysis and Modeling Review Guofeng Cao www.gis.ttu.edu/starlab Department of Geosciences Texas Tech University guofeng.cao@ttu.edu Spring 2016 Course Outlines Spatial Point Pattern
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 informationSpatial Analysis 1. Introduction
Spatial Analysis 1 Introduction Geo-referenced Data (not any data) x, y coordinates (e.g., lat., long.) ------------------------------------------------------ - Table of Data: Obs. # x y Variables -------------------------------------
More informationMeasures of Spatial Dependence
Measures of Spatial Dependence Carlos Hurtado Department of Economics University of Illinois at Urbana-Champaign hrtdmrt2@illinois.edu Junel 30th, 2016 C. Hurtado (UIUC - Economics) Spatial Econometrics
More informationThis lab exercise will try to answer these questions using spatial statistics in a geographic information system (GIS) context.
by Introduction Problem Do the patterns of forest fires change over time? Do forest fires occur in clusters, and do the clusters change over time? Is this information useful in fighting forest fires? This
More informationUniversitat Autònoma de Barcelona Facultat de Filosofia i Lletres Departament de Prehistòria Doctorat en arqueologia prehistòrica
Universitat Autònoma de Barcelona Facultat de Filosofia i Lletres Departament de Prehistòria Doctorat en arqueologia prehistòrica FROM MICRO TO MACRO SPATIAL DYNAMICS IN THE VILLAGGIO DELLE MACINE BETWEEN
More informationENGRG Introduction to GIS
ENGRG 59910 Introduction to GIS Michael Piasecki October 13, 2017 Lecture 06: Spatial Analysis Outline Today Concepts What is spatial interpolation Why is necessary Sample of interpolation (size and pattern)
More informationLecture 1: Introduction to Spatial Econometric
Lecture 1: Introduction to Spatial Econometric Professor: Mauricio Sarrias Universidad Católica del Norte September 7, 2017 1 Introduction to Spatial Econometric Mandatory Reading Why do We Need Spatial
More informationEXPLORATORY SPATIAL DATA ANALYSIS OF BUILDING ENERGY IN URBAN ENVIRONMENTS. Food Machinery and Equipment, Tianjin , China
EXPLORATORY SPATIAL DATA ANALYSIS OF BUILDING ENERGY IN URBAN ENVIRONMENTS Wei Tian 1,2, Lai Wei 1,2, Pieter de Wilde 3, Song Yang 1,2, QingXin Meng 1 1 College of Mechanical Engineering, Tianjin University
More informationBasics of Geographic Analysis in R
Basics of Geographic Analysis in R Spatial Autocorrelation and Spatial Weights Yuri M. Zhukov GOV 2525: Political Geography February 25, 2013 Outline 1. Introduction 2. Spatial Data and Basic Visualization
More informationExploratory Spatial Data Analysis Using GeoDA: : An Introduction
Exploratory Spatial Data Analysis Using GeoDA: : An Introduction Prepared by Professor Ravi K. Sharma, University of Pittsburgh Modified for NBDPN 2007 Conference Presentation by Professor Russell S. Kirby,
More informationPoints. Luc Anselin. Copyright 2017 by Luc Anselin, All Rights Reserved
Points Luc Anselin http://spatial.uchicago.edu 1 classic point pattern analysis spatial randomness intensity distance-based statistics points on networks 2 Classic Point Pattern Analysis 3 Classic Examples
More informationA GEOSTATISTICAL APPROACH TO PREDICTING A PHYSICAL VARIABLE THROUGH A CONTINUOUS SURFACE
Katherine E. Williams University of Denver GEOG3010 Geogrpahic Information Analysis April 28, 2011 A GEOSTATISTICAL APPROACH TO PREDICTING A PHYSICAL VARIABLE THROUGH A CONTINUOUS SURFACE Overview Data
More informationApplication of the Getis-Ord Gi* statistic (Hot Spot Analysis) to seafloor organisms
Application of the Getis-Ord Gi* statistic (Hot Spot Analysis) to seafloor organisms Diana Watters Research Fisheries Biologist Habitat Ecology Team Santa Cruz, CA Southwest Fisheries Science Center Fisheries
More informationSpatial Pattern Analysis: Mapping Trends and Clusters. Lauren M. Scott, PhD Lauren Rosenshein Bennett, MS
Spatial Pattern Analysis: Mapping Trends and Clusters Lauren M. Scott, PhD Lauren Rosenshein Bennett, MS Presentation Outline Spatial statistics overview Describing spatial patterns Quantifying spatial
More informationCSISS Tools and Spatial Analysis Software
CSISS Tools and Spatial Analysis Software June 5, 2006 Stephen A. Matthews Associate Professor of Sociology & Anthropology, Geography and Demography Director of the Geographic Information Analysis Core
More informationOutline ESDA. Exploratory Spatial Data Analysis ESDA. Luc Anselin
Exploratory Spatial Data Analysis ESDA Luc Anselin University of Illinois, Urbana-Champaign http://www.spacestat.com Outline ESDA Exploring Spatial Patterns Global Spatial Autocorrelation Local Spatial
More informationTexas A&M University
Texas A&M University CVEN 658 Civil Engineering Applications of GIS Hotspot Analysis of Highway Accident Spatial Pattern Based on Network Spatial Weights Instructor: Dr. Francisco Olivera Author: Zachry
More informationCreating and Managing a W Matrix
Creating and Managing a W Matrix Carlos Hurtado Department of Economics University of Illinois at Urbana-Champaign hrtdmrt2@illinois.edu Junel 22th, 2016 C. Hurtado (UIUC - Economics) Spatial Econometrics
More informationVisualize and interactively design weight matrices
Visualize and interactively design weight matrices Angelos Mimis *1 1 Department of Economic and Regional Development, Panteion University of Athens, Greece Tel.: +30 6936670414 October 29, 2014 Summary
More informationThe CrimeStat Program: Characteristics, Use, and Audience
The CrimeStat Program: Characteristics, Use, and Audience Ned Levine, PhD Ned Levine & Associates and Houston-Galveston Area Council Houston, TX In the paper and presentation, I will discuss the CrimeStat
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? What is Outlier
More informationSASI Spatial Analysis SSC Meeting Aug 2010 Habitat Document 5
OBJECTIVES The objectives of the SASI Spatial Analysis were to (1) explore the spatial structure of the asymptotic area swept (z ), (2) define clusters of high and low z for each gear type, (3) determine
More informationIntroduction GeoXp : an R package for interactive exploratory spatial data analysis. Illustration with a data set of schools in Midi-Pyrénées.
Presentation of Presentation of Use of Introduction : an R package for interactive exploratory spatial data analysis. Illustration with a data set of schools in Midi-Pyrénées. Authors of : Christine Thomas-Agnan,
More informationUsing GIS to Identify Pedestrian- Vehicle Crash Hot Spots and Unsafe Bus Stops
Using GIS to Identify Pedestrian-Vehicle Crash Hot Spots and Unsafe Bus Stops Using GIS to Identify Pedestrian- Vehicle Crash Hot Spots and Unsafe Bus Stops Long Tien Truong and Sekhar V. C. Somenahalli
More informationLecture 4. Spatial Statistics
Lecture 4 Spatial Statistics Lecture 4 Outline Statistics in GIS Spatial Metrics Cell Statistics Neighborhood Functions Neighborhood and Zonal Statistics Mapping Density (Density surfaces) Hot Spot Analysis
More informationThe Study on Trinary Join-Counts for Spatial Autocorrelation
Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences Shanghai, P. R. China, June 5-7, 008, pp. -8 The Study on Trinary Join-Counts
More informationOverview of Spatial analysis in ecology
Spatial Point Patterns & Complete Spatial Randomness - II Geog 0C Introduction to Spatial Data Analysis Chris Funk Lecture 8 Overview of Spatial analysis in ecology st step in understanding ecological
More informationTutorial 8 Raster Data Analysis
Objectives Tutorial 8 Raster Data Analysis This tutorial is designed to introduce you to a basic set of raster-based analyses including: 1. Displaying Digital Elevation Model (DEM) 2. Slope calculations
More informationOutline. Introduction to SpaceStat and ESTDA. ESTDA & SpaceStat. Learning Objectives. Space-Time Intelligence System. Space-Time Intelligence System
Outline I Data Preparation Introduction to SpaceStat and ESTDA II Introduction to ESTDA and SpaceStat III Introduction to time-dynamic regression ESTDA ESTDA & SpaceStat Learning Objectives Activities
More informationGlobal Spatial Autocorrelation Clustering
Global Spatial Autocorrelation Clustering Luc Anselin http://spatial.uchicago.edu join count statistics Moran s I Moran scatter plot non-parametric spatial autocorrelation Join Count Statistics Recap -
More informationMapping and Analysis for Spatial Social Science
Mapping and Analysis for Spatial Social Science Luc Anselin Spatial Analysis Laboratory Dept. Agricultural and Consumer Economics University of Illinois, Urbana-Champaign http://sal.agecon.uiuc.edu Outline
More informationMichael Harrigan Office hours: Fridays 2:00-4:00pm Holden Hall
Announcement New Teaching Assistant Michael Harrigan Office hours: Fridays 2:00-4:00pm Holden Hall 209 Email: michael.harrigan@ttu.edu Guofeng Cao, Texas Tech GIST4302/5302, Lecture 2: Review of Map Projection
More informationWhy Is It There? Attribute Data Describe with statistics Analyze with hypothesis testing Spatial Data Describe with maps Analyze with spatial analysis
6 Why Is It There? Why Is It There? Getting Started with Geographic Information Systems Chapter 6 6.1 Describing Attributes 6.2 Statistical Analysis 6.3 Spatial Description 6.4 Spatial Analysis 6.5 Searching
More informationSpatial Pattern Analysis: Mapping Trends and Clusters
Esri International User Conference San Diego, California Technical Workshops July 24, 2012 Spatial Pattern Analysis: Mapping Trends and Clusters Lauren M. Scott, PhD Lauren Rosenshein Bennett, MS Presentation
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 informationSpatial Point Pattern Analysis
Spatial Point Pattern Analysis Jiquan Chen Prof of Ecology, University of Toledo EEES698/MATH5798, UT Point variables in nature A point process is a discrete stochastic process of which the underlying
More informationSpatial Analysis with ArcGIS Pro STUDENT EDITION
Spatial Analysis with ArcGIS Pro STUDENT EDITION Copyright 2018 Esri All rights reserved. Course version 2.0. Version release date November 2018. Printed in the United States of America. The information
More informationGIS and Spatial Statistics: One World View or Two? Michael F. Goodchild University of California Santa Barbara
GIS and Spatial Statistics: One World View or Two? Michael F. Goodchild University of California Santa Barbara Location as attribute The data table Census summary table What value is location as an explanatory
More informationModeling the Ecology of Urban Inequality in Space and Time
Modeling the Ecology of Urban Inequality in Space and Time Corina Graif PhD Candidate, Department Of Sociology Harvard University Presentation for the Workshop on Spatial and Temporal Modeling, Center
More informationTracey Farrigan Research Geographer USDA-Economic Research Service
Rural Poverty Symposium Federal Reserve Bank of Atlanta December 2-3, 2013 Tracey Farrigan Research Geographer USDA-Economic Research Service Justification Increasing demand for sub-county analysis Policy
More informationWhere to Invest Affordable Housing Dollars in Polk County?: A Spatial Analysis of Opportunity Areas
Resilient Neighborhoods Technical Reports and White Papers Resilient Neighborhoods Initiative 6-2014 Where to Invest Affordable Housing Dollars in Polk County?: A Spatial Analysis of Opportunity Areas
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 informationChapter 6 Spatial Analysis
6.1 Introduction Chapter 6 Spatial Analysis Spatial analysis, in a narrow sense, is a set of mathematical (and usually statistical) tools used to find order and patterns in spatial phenomena. Spatial patterns
More informationOPEN GEODA WORKSHOP / CRASH COURSE FACILITATED BY M. KOLAK
OPEN GEODA WORKSHOP / CRASH COURSE FACILITATED BY M. KOLAK WHAT IS GEODA? Software program that serves as an introduction to spatial data analysis Free Open Source Source code is available under GNU license
More informationSpatial Data Analysis in Archaeology Anthropology 589b. Kriging Artifact Density Surfaces in ArcGIS
Spatial Data Analysis in Archaeology Anthropology 589b Fraser D. Neiman University of Virginia 2.19.07 Spring 2007 Kriging Artifact Density Surfaces in ArcGIS 1. The ingredients. -A data file -- in.dbf
More informationSpatial Analysis of Population Distribution by Employment Sectors in Tokyo Metropolitan Area
1 Spatial Analysis of Population Distribution by Employment Sectors in Tokyo Metropolitan Area MONZUR, Tawhid Abstract This research is focused on analyzing the urban spatial structure of the Tokyo metropolitan
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 informationIdentification of Economic Clusters Using ArcGIS Spatial Statistics. Joseph Frizado Bruce Smith Michael Carroll
Identification of Economic Clusters Using ArcGIS Spatial Statistics Joseph Frizado Bruce Smith Michael Carroll ABSTRACT Geographic proximity (co-location) is necessary for potential clustering activity.
More informationSpatial-Temporal Analytics with Students Data to recommend optimum regions to stay
Spatial-Temporal Analytics with Students Data to recommend optimum regions to stay By ARUN KUMAR BALASUBRAMANIAN (A0163264H) DEVI VIJAYAKUMAR (A0163403R) RAGHU ADITYA (A0163260N) SHARVINA PAWASKAR (A0163302W)
More informationGEO 463-Geographic Information Systems Applications. Lecture 1
GEO 463-Geographic Information Systems Applications Lecture 1 Rules of engagement No Mobile Submit course work- scratch my back.i..? Software- Quantum GIS vrs ArcGIS Open source vrs Commercial Free vrs
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 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 informationConcepts and Applications of Kriging. Eric Krause Konstantin Krivoruchko
Concepts and Applications of Kriging Eric Krause Konstantin Krivoruchko Outline Introduction to interpolation Exploratory spatial data analysis (ESDA) Using the Geostatistical Wizard Validating interpolation
More informationGIST 4302/5302: Spatial Analysis and Modeling Lecture 2: Review of Map Projections and Intro to Spatial Analysis
GIST 4302/5302: Spatial Analysis and Modeling Lecture 2: Review of Map Projections and Intro to Spatial Analysis Guofeng Cao http://www.spatial.ttu.edu Department of Geosciences Texas Tech University guofeng.cao@ttu.edu
More informationI don t have much to say here: data are often sampled this way but we more typically model them in continuous space, or on a graph
Spatial analysis Huge topic! Key references Diggle (point patterns); Cressie (everything); Diggle and Ribeiro (geostatistics); Dormann et al (GLMMs for species presence/abundance); Haining; (Pinheiro and
More informationGIST 4302/5302: Spatial Analysis and Modeling
GIST 4302/5302: Spatial Analysis and Modeling Lecture 2: Review of Map Projections and Intro to Spatial Analysis Guofeng Cao http://thestarlab.github.io Department of Geosciences Texas Tech University
More informationUsing Spatial Statistics and Geostatistical Analyst as Educational Tools
Using Spatial Statistics and Geostatistical Analyst as Educational Tools By Konrad Dramowicz Centre of Geographic Sciences Lawrencetown, Nova Scotia, Canada ESRI User Conference, San Diego, California
More informationTesting for global spatial autocorrelation in Stata
Testing for global spatial autocorrelation in Stata Keisuke Kondo March 31, 2018 (moransi: version 1.00) Abstract This paper introduces the new Stata command moransi, which computes Moran s I statistic
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 informationEVALUATING CHANGING RESIDENTIAL SEGREGATION IN AUCKLAND, NEW ZEALAND, USING SPATIAL STATISTICS
EVALUATING CHANGING RESIDENTIAL SEGREGATION IN AUCKLAND, NEW ZEALAND, USING SPATIAL STATISTICS RON JOHNSTON, MICHAEL POULSEN, AND JAMES FORREST THIS PAPER HAS BEEN SUBMITTED FOR PUBLICATION Not to be cited
More informationClass 9. Query, Measurement & Transformation; Spatial Buffers; Descriptive Summary, Design & Inference
Class 9 Query, Measurement & Transformation; Spatial Buffers; Descriptive Summary, Design & Inference Spatial Analysis Turns raw data into useful information by adding greater informative content and value
More informationThe Implementation of Autocorrelation-Based Regioclassification in ArcMap Using ArcObjects
140 The Implementation of Autocorrelation-Based Regioclassification in ArcMap Using ArcObjects Christoph MAYRHOFER Abstract Conventional methods for cartographic classification are often solely based on
More informationConcepts and Applications of Kriging
2013 Esri International User Conference July 8 12, 2013 San Diego, California Technical Workshop Concepts and Applications of Kriging Eric Krause Konstantin Krivoruchko Outline Intro to interpolation Exploratory
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 informationSpatial Pattern Analysis: Mapping Trends and Clusters
2013 Esri International User Conference July 8 12, 2013 San Diego, California Technical Workshop Spatial Pattern Analysis: Mapping Trends and Clusters Lauren Rosenshein Bennett Brett Rose Presentation
More informationConstruction Engineering. Research Laboratory. Approaches Towards the Identification of Patterns in Violent Events, Baghdad, Iraq ERDC/CERL CR-09-1
ERDC/CERL CR-09-1 Approaches Towards the Identification of Patterns in Violent Events, Baghdad, Iraq Luc Anselin and Gianfranco Piras May 2009 Construction Engineering Research Laboratory Approved for
More informationComparison of spatial methods for measuring road accident hotspots : a case study of London
Journal of Maps ISSN: (Print) 1744-5647 (Online) Journal homepage: http://www.tandfonline.com/loi/tjom20 Comparison of spatial methods for measuring road accident hotspots : a case study of London Tessa
More informationHUMAN CAPITAL CATEGORY INTERACTION PATTERN TO ECONOMIC GROWTH OF ASEAN MEMBER COUNTRIES IN 2015 BY USING GEODA GEO-INFORMATION TECHNOLOGY DATA
International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 11, November 2017, pp. 889 900, Article ID: IJCIET_08_11_089 Available online at http://http://www.iaeme.com/ijciet/issues.asp?jtype=ijciet&vtype=8&itype=11
More informationSection C: Management of the Built Environment GIS As A Tool: Technical Aspects of Basic GIS
Section C: Management of the Built Environment GIS As A Tool: Technical Aspects of Basic GIS This lecture covers five topics: 1.Scale, 2.Framework data, 3.Generalisation, 4.Aggregation, 5.Modifiable unit
More informationConcepts and Applications of Kriging. Eric Krause
Concepts and Applications of Kriging Eric Krause Sessions of note Tuesday ArcGIS Geostatistical Analyst - An Introduction 8:30-9:45 Room 14 A Concepts and Applications of Kriging 10:15-11:30 Room 15 A
More information