GIS beyond just a pretty map

Transcription

1 GIS beyond just a pretty map Presented by: Dr. Joshua Greenfeld Professor Emeritus New Jersey Institute of Technology New Jersey Society of Professional Land Surveyors February 6, 23

2 3 2 Std. Dev =.3 Mean =.393 N = GIS beyond just a pretty map Objective South.shp Frequency GI GI89 By Joshua Greenfeld fit x y The surveying profession is facing a historical challenge of sustaining itself as an acclaimed, distinguished and economically viable profession. Positioning and mapping technologies are widely available to the public and used by many to perform tasks traditionally reserved to a professional land surveyor. The umbrella under which this process takes place is GIS. Many surveyors believe that GIS is just another tool for creating pretty maps. This is a very limited and unproductive perspective of GIS. So, what is GIS from the perspective of the GIS community? 2 Objective The Association of American Geographers (AAG) published the GIS & Technology Body of Knowledge which describes what they perceive GIS is. It is important for us (surveyors) to become familiar with the AAG and the geo-spatial industry understanding of GIS. It is important for us to understand the GIS community s perception of the role of surveying in GIS so that surveyors are able not only to make their mark in GIS but also to find new opportunities to broaden surveyors involvement in GIS. Objective The objectives of the seminar are: to provide a detailed overview of the GIS & T BoK to discuss the opportunities for surveyors to become involved in this multi-billion Dollar industry. to outline the many different roles people may assume as GIS specialists. to highlight the indispensable role of surveyors in various aspects of GIS that could rejuvenate our professions substantially and financial viability. 3 4 Dr. Joshua Greenfeld (c)

3 The AAG Body of Knowledge The definition of GIS based on Chrisman (Chrisman 22) The organized activity by which people: measure aspects of geographic phenomena and processes; represent these measurements, usually in the form of a computer database, to emphasize spatial themes, entities, and relationships; operate upon these representations to produce more measurements and to discover new relationships by integrating disparate sources; and transform these representations to conform to other frameworks of entities and relationships. 5 6 The definition of GIS based on Chrisman (Chrisman 22) Context Organizes Views Data Quality: Verify Against World Social and Cultural Context Institutional Context Transformations Operations Representation Measurement Context Provides Goals Evaluate Inside Goals GIS Competencies for success in the geospatial technology profession TECHNICAL COMPETENCIES ANALYTICAL COMPETENCIES BUSINESS COMPETENCIES INTERPERSONAL COMPETENCIES Reference: Geospatial Workforce Development Center developed the Geospatial Technology Competency Model (GTCM) (Gaudet, et.al. 23) 7 8 Dr. Joshua Greenfeld (c) 2

4 GIS Competencies for success in the geospatial technology profession TECHNICAL COMPETENCIES Ability to Assess Relationships Among Geospatial Technologies Cartography Computer Programming Skills Environmental Applications GIS Theory and Applications Geological Applications Geospatial Data Processing Tools Photogrammetry Remote Sensing Theory and Applications Spatial Information Processing Technical Writing Technological Literacy GIS Competencies for success in the geospatial technology profession ANALYTICAL COMPETENCIES Creative Thinking Knowledge Management Model Building Skills Problem-Solving Skills Research Skill Systems Thinking Topology 9 GIS Competencies for success in the geospatial technology profession BUSINESS COMPETENCIES Ability to See the 'Big Picture" Business Understanding Buy-in/Advocacy Change Management Cost Benefit Analysis/ROI Ethics Modeling Industry Understanding Legal Understanding Organizational Understanding Performance Analysis and Evaluation Visioning GIS Competencies for success in the geospatial technology profession INTERPERSONAL COMPETENCIES Coaching Communication Conflict Management Feedback Skills Group Process Understanding Leadership Skills Questioning Relationship Building Skills Self-Knowledge/Self- Management 2 Dr. Joshua Greenfeld (c) 3

5 The GIS&T Body of Knowledge Analytical Methods (-AM) Conceptual Foundations (2-CF) Cartography and Visualization (3-CV) Design Aspects (4-DA) Data Modeling (5-DM) Data Manipulation (6-DN) Geocomputation (7-GC) Geospatial Data (8-GD) GIS&T and Society (9-GS) Organizational and Institutional Aspects (-OI) The GIS&T Body of Knowledge Knowledge Area : Analytical Methods (AM) Operations to derive analytical results from geospatial data Data analysis seeks to understand both first-order (environmental) effects and second-order (interaction) effects. Two approaches to data analysis: Data driven techniques (exploration of geospatial data) derive summary descriptions of data, evoke insights about characteristics of data, contribute to the development of research hypotheses, and lead to the derivation of analytical results. The goal of model-driven analysis is to create and test geospatial process models. This knowledge area requires knowledge and skills in mathematics, statistics, and computer programming. The GIS&T Body of Knowledge Knowledge Area : Analytical Methods (AM) Query operations and query languages (Set theory, SQL ) Geometric measures (Dist, Shape, Proximity, Adjacency and connectivity) Basic analytical operations (Buffers, Overlay, Neighborhoods, Map algebra) Basic Analytical Methods (pattern, Spatial interaction, Multi-criteria evaluation) Analysis of surfaces Spatial statistics (Testing of hypotheses about the nature of spatial pattern, dependency, and heterogeneity) Geostatistics, (Spatial sampling, Semi-variogram modeling, kriging) Spatial regression and econometrics Data mining Network Analysis Optimization and location - allocation modeling Query operations and query languages Considered to be the most basic form of analysis. Set theory What is set theory The relation of set theory to spatial queries The set based model involves: The constituent objects to be modeled, called elements or members Collection of elements, called sets The relationship between the elements and the sets to which they belong, termed membership We write s 2 S to indicate that an element s is a member of the set S Dr. Joshua Greenfeld (c) 4

6 Distinguished sets Name Symbol Description Booleans B Two-valued set of true/false, /, or on/off Integers Z Positive and negative numbers, including zero Reals R Measurements on the number line Real Plane R 2 Ordered pairs of reals Closed interval Open interval Semi-open interval [a,b] ]a,b[ [a,b[ All reals between a and b 9 including a and b) All reals between a and b (excluding a and b) All reals between a and b (including a and excluding b) Worboys and Duckham (24) Worboys and Duckham (24) GIS: A Computing Perspective, Convexity in terms of Sets A set is convex if every point is visible from every other point within the set Let S be a set of points in the Euclidean plane Visible: Point x in S is visible from point y in S if either x=y or; it is possible to draw a straight-line segment between x and y that consists entirely of points of S S GIS: A Computing Perspective, Query operations and query languages Considered to be the most basic form of analysis. Structured Query Language (SQL) and attribute queries Spatial queries Attribute query and spatial query Spatial queries examples: Selecting features based on location or spatial relationships Searching for a specific spatial or temporal relationship Extracting all point objects that fall within a polygon Geometric measures Basic geometric operations that help in extracting meaning from sets of data or for deriving new data for further analysis Distances and lengths Different measures of distance between two points (e.g., Euclidean, Manhattan, network distance, spherical) Different measures of distance can be used to calculate the spatial weights matrix Differences in the calculated distance between the same two places when data used are in different projections Dr. Joshua Greenfeld (c) 5

7 Geometric measures Direction Define "direction" and its measurement in different angular measures Compare and contrast how direction is determined and stated in raster and vector data Differences in the measured direction between two places when the data are presented in a GIS in different projections Geometric measures Shape Method for describing the shape of a cluster of similarly valued points by using the concept of the convex hull Algorithm to determine the skeleton of polygons Find centroids of polygons under different definitions of a centroid and different polygon shapes Calculate several different shape indices for a polygon dataset. For example: Geometric measures Area Reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes Variations in the calculation of area may have real world implications, such as calculating density Area of a region calculated from a raster data set will vary by resolution and orientation Geometric measures Proximity and distance decay Applications where: distance decay is an appropriate representation of the strength of spatial relationships (e.g., shopping behavior, property values) distance decay would not be an appropriate representation of the strength of spatial relationships (e.g., distance education, commuting, telecommunications) Different forms of distance decay functions (which one to use when) Dr. Joshua Greenfeld (c) 6

8 Geometric measures Adjacency and connectivity Different ways connectivity can be determined in a raster and in a polygon dataset The nine-intersection model for spatial relationships Adjacency and connectivity can be recorded in matrices W O C A N I M W U C N A R A Z V D T Y T O M Washington Oregon California Arizona Nevada Idaho Montana Wyoming Utah Colorado New Mexico Basic analytical operations Buffers Circumstances in which buffering around an object is useful in analysis (drug free, hazardous offsets) Compare and contrast raster and vector definitions of buffers Basic analytical operations Overlay "dissolve and merge" '"planar enforcement" (objects and their attributes are capable of describing the conditions existing on a map or in reality) Possible sources of error in overlay operations Applications in which overlay is useful, such as site suitability analysis Overlay implementation in raster and vector domains Map overlay using Boolean logic GIS analytical functions Basic GIS analytical functions: Map overlay A AND B A OR B A XOR B A NOT B Worboys and Duckham (24) GIS: A Computing Perspective, Second Edition, CRC Press Dr. Joshua Greenfeld (c) 7

9 Basic analytical operations Local operations Map algebra Map algebra to perform mathematical functions on raster grids Modeling situation in which map algebra would be used (e.g., site selection, climate classification, least-cost path) Categories of map algebra operations (i.e., local, focal, zonal, and global functions) Local operation: acts upon one or more spatial fields to produce a new field The value of the new field at any location is dependent on the values of the input field function at that location The symbol is any operation Example: f(x) is population g(x) is number of cars number of cars per unit population f(x) g(x) = g(x)/ f(x) x f(x) g(x) f(x) g(x) F f g f g Worboys and Duckham (24) GIS: A Computing Perspective, Focal operations Focal operation: the attribute value derived at a location x may depend on the attributes of the input spatial field functions at x and the attributes of these functions in the neighborhood n(x) of x Given framework F, neighborhood function n and a spatial field function f, for each location x:. Compute n(x) (set of neighborhood point of x 2. Compute values of f applied to n(x) 3. Derive a single value of the new field at x Example: compute slope vector at x from DTM x f(x) n(x) φ(x) F f n φ Zonal operations Zonal operation: aggregates values of a field over a set of zones (arising in general from another field function) in the spatial framework For each location x:.find the Zone Z i in which x is contained 2.Compute the values of the field function f applied to each point in Z i 3.Derive a single value ζ(x) of the new field from the values computed in step 2 Example: given a layer of temperature and a layer of administrative regions, create a layer of average temperature for each region x f(x) F f Z i, Z k n(x) ζ ζ(x) Worboys and Duckham (24) GIS: A Computing Perspective, Worboys and Duckham (24) GIS: A Computing Perspective, Dr. Joshua Greenfeld (c) 8

10 Basic analytical operations Neighborhoods Methods to establish overlapping and non-overlapping neighborhoods of similarity in raster datasets (Block Function) A Max Block Operation on a 3x3 nonoverlapping neighborhood results in: Basic analytical operations Neighborhoods Voronoi polygons and Delaunay triangulation Thiessen-Noronoi proximity polygons Basic Analytical Methods Point pattern analysis How Independent Random Process/Chi- Squared Result (IRP/CSR) may be used to make statistical statements about point patterns (hypothesis tests) Measures of pattern based on first and second order properties such as the mean center and standard distance, quadrat counts, nearest neighbor distance, and the more modern G, F, and K functions Median and Mean Centers for US Population Median Center: Intersection of a north/south and an east/west line drawn so half of population lives above and half below the e/w line, and half lives to the left and half to the right of the n/s line Mean Center: Balancing point of a weightless map, if equal weights placed on it at the residence of every person on census day. Source: US Statistical Abstract 23 Dr. Joshua Greenfeld (c) 9

11 Standard Distance Deviation Example S dd 5 Circle with radii = S dd = 2.9 2,3 4,7 6,2 7,7 7,3 5 i X Y (X - X c ) 2 (Y - Y c ) Centroid n 2 n 2 ( Xi Xc) + ( Yi Yc) 42. i= i= n 8.4 N 2.9 random Number of Points Number of Number of Quadrat Per Points Per Quadrat Points Per 2 2 x x ( ) x x ( ) 2 # Quadrat x x ( ) i i Quadrat # Quadrat # Quadrat i Variance 2. Mean 2. Var/Mean. = i Note :N = number of Quadrats = x Variance/Mean Ratio (VMR) n ( Xi X ) = var = N uniform x Variance. Mean 2. Var/Mean. 2 Clustered x Variance 6. Mean 2. Var/Mean 8. Significance Test for VMR A significance test can be conducted based upon the χ 2 frequency The test statistic is given by: (sum of squared differences)/mean 2 2 n ( xi x) χ = i= x The test will ascertain if a pattern is significantly more clustered than would be expected by chance (but does not test for a uniformity) Significance Test for VMR The values of the test statistics in our cases would be: random 2/2= uniform /2 = 2 2 n ( xi x) χ = i= x clustered 6/2=8 For degrees of freedom: N - = - = 9 The value of χ 2 at the % level is Meaning, there is only a % chance of obtaining a value of or greater if the points had been distributed randomly. Since our test statistic for the clustered pattern is 8, we conclude that there is (considerably) less than a % chance that the clustered pattern could have resulted from a random process Dr. Joshua Greenfeld (c)

12 Basic Analytical Methods Kernels and density estimation Differentiate between kernel density estimation (sand pile) and spatial interpolation Effects on analysis results of variations in the kernel function used and the bandwidth adopted Basic Analytical Methods Spatial cluster analysis Cluster detection techniques K-means example Y k 2 k k k 2 k 3 k 3 X 42 Basic Analytical Methods Spatial interaction Estimate the flow of people, material or information between locations in geographic space includes classic gravity model, The unconstrained spatial interaction model, The production constrained spatial interaction model The attraction constrained spatial interaction model The doubly constrained spatial interaction model Dynamic, chaotic, complex, or unpredictable aspects in some phenomena make spatial interaction models more appropriate than gravity models Basic Analytical Methods Analyzing multidimensional attributes Data matrix of attributes Simple hierarchical cluster analysis to classify area objects into statistically similar regions Spatial process models Processes that influence properties (e.g., temperature) at fixed locations, or tracks the changing location of particles through space and, therefore, is a description of movement. Relationship between spatial processes and spatial patterns Dr. Joshua Greenfeld (c)

13 Analysis of surfaces Calculating surface derivatives Methods for calculating slope from a DEM The interpretation of higher order height derivatives Slope and aspect represented as vector fields Zero slopes are indicative of surface specific points such as peaks, pits, and passes Analysis of surfaces Interpolation of surfaces Interpolation algorithms (inverse distance weighting, bi-cubic spline fitting, and kriging...) Trend surface analysis Algorithm that interpolates irregular point elevation data onto a regular grid Algorithms to produce repeatable contourtype lines from point datasets using proximity polygons, spatial averages, or inverse distance weighting Analysis of surfaces Natural Neighbor 5 and m Grids IDW 5 and m Grids Analysis of surfaces Intervisibility Sources and impact of errors that affect intervisibility analyses Algorithm to determine the viewshed (visible area) from specific locations on surfaces specified by DEMs Friction surfaces The cost of traveling through the surface or resistance to travel across the surface Friction surfaces are enhanced by the use of impedance and barriers Principles of friction surfaces in the calculation of leastcost paths Dr. Joshua Greenfeld (c) 2

14 Analysis of surfaces Spatial statistics Spatial statistical analysis forms the backbone for the testing of hypotheses about the nature of spatial pattern, dependency, and heterogeneity. An extension of traditional statistics. Graphical methods Statistical characteristics of a set of spatial data using a variety of graphs and plots (including scatterplots, histograms, boxplots, q q plots. Selecting the appropriate statistical methods for the analysis of given spatial datasets by first exploring them using graphic methods Scatterplot and Histograms Boxplots Histogram Bin Width max max 75% 75% median median 25% 25% min min Min=max(min, Q2.5(Q3 Q)) Max=min(max, Q2.5(Q Q3)) max Q Q Q min 2.8 Dr. Joshua Greenfeld (c) 3

15 Q-Q Plot Spatial statistics Stochastic processes Assumption of the purely random process Deterministic and spatial stochastic processes Isotropic (properties identical in all directions) and anisotropic processes Cumulative distribution values calculated as: (i.5)/n for the ith ordered value out of n total values (the % of the data below a value) OH WV MD DE NJ NY PA OH WV MD DE NJ NY PA Spatial statistics The spatial weights matrix The spatial weights matrix is an nxn table, one row for every feature and one column for every feature. The cell value for any given row/column combination is the weight that quantifies the spatial relationship between those row and column features. The W and the standardized W matrices OH WV MD DE NJ NY PA Spatial statistics Global measures of spatial association K function to provides a scale-dependent measure of dispersion Example: Ripley's k-function L( d) = A n n i = j =, j i π n( n ) w( i, j) Where: d the distance n total number of features A the area of the features w(i,j) = if dij < d w(i,j) = otherwise Dr. Joshua Greenfeld (c) 4

16 Spatial statistics Global measures of spatial association Moran's I and Geary's c for patterns of attribute data measured on interval/ratio scales Compute measures of overall dispersion and clustering of point datasets using,nearest neighbor distance statistics d NNI = d E( d ) ( ).5 A n n n d i= i = E d = d i nearest distance to point i d ij the distance from point i to nearest point j A analyzed Area n # of points d = d 3 d 2 = d 23 d 3 = d 32 d 4 = d 43 For random pattern, NNI = For clustered pattern, NNI = For dispersed pattern, NNI = Spatial statistics Local measures of spatial association A weights matrix can be used to convert any classical statistic into a local measure of spatial association The Gi and Gi* statistics (calculates a Z score that tells you where features with either high or low values cluster spatially) also called the hot spot analysis. G( d ) = i n n = j = n w ( d ) x x n i = j = ij i j x x i j for i j w ij (d) is a distance function Spatial statistics Outliers The affect of outliers on the results of analyses Techniques that can be used to examine outliers: tabulation, histograms, box plots, correlation analysis, scatter plots, local statistics Bayesian methods "prior and posterior distributions" and "Markov-Chain Monte Carlo" A unified framework to view uncertainty Geostatistics A variety of techniques used to analyze continuous data (e.g., rainfall, elevation, air pollution). Spatial sampling for statistical analysis Spatial sampling schemes Model-based sampling schemes (requires that the relationship of sample properties to population properties be specified by a model, and the model itself contains the prescription for the inference to the population) Design-based sampling schemes (requires a probability sample, and inference is founded on the resulting sampling distribution) Dr. Joshua Greenfeld (c) 5

17 Sampling: The Quest to Represent the Real World Field - selecting discrete objects from a continuous surface Object - selecting some discrete objects, discarding others a spatially random sample a spatially systematic (stratified) sample a stratified random sample Spatially systematic sampling presumes that each observation is of equal importance in building a representation. a sampling scheme with periodic random changes in the grid width of a spatially systematic sample Geostatistics Principles of semi-variogram construction A measure of autocorrelation for all pairs of locations separated by distance h having values of a and a 2. Calculate ( a -a 2 /2) 2 To plot all pairs becomes unmanageable. Instead of plotting each pair, the pairs are grouped into lag bins. For example, compute the average semivariance for all pairs of points that are greater than 4 meters but less than 5 meters apart. The empirical semivariogram is a graph of the averaged semivariogram values on the y-axis and distance (or lag) on the x-axis (see next slide). Semivariogram Semivariogram pairing of each point (e.g. the red point) with all other measured locations. Fitting Covariance Model The empirical semivariogram is a graph of the averaged semivariogram values on the y-axis and the distance Dr. Joshua Greenfeld (c) 6

18 Geostatistics Principles of kriging Steps involved in Kriging: Calculate empirical semivariogram (determine autocorrelation) Fit a model through semivariogram Create matrices to determine kriging weights Make a prediction to calculate unknown values The concept of the kriging variance (or Kriging error) Ordinary Kriging Geostatistics In our case Z, at point p, Z e (p) to be calculated using a weighted average of the known values or control points: z ( e i i p) = w z( p ) This estimated value will most likely differ from the actual value at point p, Za(p), and this difference is called the estimation error: ε = z ( p) z ( p) p e a Data mining The process of extracting patterns from data. Spatial data mining follows along the same functions in data mining, with the end objective to find patterns in geography. Problems of large spatial databases Difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity How to recognize contaminated data in large datasets Data mining Data mining approaches The differences between the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction Knowledge discovery Visual data exploration combined with data mining techniques as a means of discovering research hypotheses in large spatial datasets Dr. Joshua Greenfeld (c) 7

19 Data mining Pattern recognition and matching The principles of pattern recognition Artificial intelligence analysis (e.g., edge recognition), human vision vs computer identification Simple spatial mean filter as a means of recognizing patterns Network Analysis Procedures, techniques, and methods and models of connected sets of edges and vertices. Applications include transportation networks, river networks, and utility networks (electric, cable, sewer and water, etc. Such sets are termed a network, or a graph, and the mathematical basis for network analysis is known as graph theory. Graph theory contains descriptive measures and indices of networks (such as connectivity, adjacency, capacity, and flow) as well as methods for proving the properties of networks. Bridges in Konigsberg park (Euler) Network Analysis Networks defined Terms pertaining to a network include: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle Graph theoretic (descriptive) measures of networks Networks can be measured using the number of elements in a network, the distances along network edges, and the level of connectivity of the network Diameter of a Graph The number of links (edges) between the furthest nodes (2 and 7) of this graph is 4 Dr. Joshua Greenfeld (c) 8

20 Number of Cycles This number (u) is estimated through the number of nodes (v), links (e) and of subgraphs (p) Alpha Index A measure of connectivity which evaluates the number of cycles in a graph in comparison with the maximum number of cycles 7 6 Beta Index Measures the level of connectivity in a graph. In a fixed nodes network, the higher the number of links, the higher the number of possible paths in the network Network Analysis Least-cost (shortest) path Dijkstra's algorithm and other variants The K-shortest path algorithms to find many efficient alternate paths across the network Flow modeling The concept of capacity to represent an upper limit on the amount of flow through the network Assignment of capacity to edges in a network using the appropriate data structure Maximum flow algorithm The classic transportation problem The classic transportation problem (O-D) and S/D Dr. Joshua Greenfeld (c) 9

21 Network Analysis Other classic network problems The traveling salesman problem, the Chinese postman problem (shortest closed trail (circuit) that visits every edge of a (connected) undirected graph) Accessibility modeling Definitions of accessibility on a network Methods for measuring different kinds of accessibility on a network Accessibility modeling at the individual level versus at an aggregated level The GIS&T Body of Knowledge Knowledge Area 2: Conceptual Foundations (CF) Objective: To recognize, identify, and appreciate the explicit spatial, spatio-temporal and semantic components of the geographic environment. Philosophical foundations Domains of geographic information (Space, Time, Properties) Elements of geographic, information (Discrete entities, Events, Fields ) Relationships (Topological Metrical Spatial distribution) Imperfections in Geographic information (Vagueness Fuzzy sets Error-based uncertainty) The GIS&T Body of Knowledge Knowledge Area 3: Cartography and Visualization (CV) Visual display of geographic information. History and trend Data considerations (Source materials, Data abstraction, Projections as a map design issue) Principles of map design (Map design, symbolization, Color, Typography) Graphic representation techniques (thematic mapping methods, Dynamic and interactive displays ) Map production Map use and evaluation (The power of maps, Map reading, interpretation, Map analysis, Impact of uncertainty) The GIS&T Body of Knowledge Knowledge Area 4: Design Aspects (DA) Objective: Design of applications and databases for a particular need. The scope of GIS&T system design Project definition Resource planning Database design (Logical and physical models) Analysis design (derived by analysis vs. stored explicitly) Application design (Workflow, User interfaces, CASE tools System implementation (planning, tasks, testing, deployment) Dr. Joshua Greenfeld (c) 2

22 The GIS&T Body of Knowledge Knowledge Area 5: Data Modeling (DM) Objective: Representation of formalized spatial and spatiotemporal reality through data models and the translation of these data models into data structures that are capable of being implemented within a computational environment Basic Storage and retrieval structure Database management systems (Relational DBMS, Object-oriented, Extensions of the relational model) Tessellation data models (partitions a continuous surface into a set of nonoverlapping polygons) Vector and object data models (spaghetti, topological, network model, Object-based spatial databases) Modeling 3-D, Temporal and uncertain Phenomena The GIS&T Body of Knowledge Knowledge Area 6: Data Manipulation (DN) Three general classes of operation:. Transformation into formats that facilitate subsequent analysis, 2. Generalization and aggregation 3. Transaction management (tracking of changes, versioning, and updating. Representation transformation (of data structures, data models, projections, and data representation) Generalization and aggregation Transaction management of geospatial data (Modeling database change, Reconciling database change, versioning) The GIS&T Body of Knowledge Knowledge Area 7: Geocomputation (GC) Methods designed to simulate, model, analyze, and visualize a range of highly complex, often non-deterministic, nonlinear problems Computational aspects and neurocomputing Cellular automata (CA) models (computational models in a cell-based space) Heuristics Genetic algorithms (GA) Agent-based models (actors that have heterogeneous characteristics and/or behaviors and interact with a heterogeneous environment) Simulation modeling Uncertainty Fuzzy Sets Geocomputation Some geographical systems can be difficult to model or analyze well with traditional statistical approaches due to a combination of data complexity, invalid assumptions and computational demands. Geocomputational methods are often drawn from machine learning and simulation research, and include a variety of methods designed to simulate, model, analyze, and visualize a range of highly complex, often non-deterministic, non-linear problems. Methods include, but are not limited to, cellular automata, neural networks, agent-based models, genetic algorithms, and fuzzy sets. 84 Dr. Joshua Greenfeld (c) 2

23 Fuzzy logic Fuzzy logic and fuzzy set techniques allow for the geospatial analysis of a more nuanced approach to data analysis. Fuzzy logic, rather then Boolean algebra models, can be useful for representing real world boundaries that are not crisp and abrupt (e.g. soils classification). Challenges are deriving methods for fuzzy aggregation such as intersect and union and fuzzy weighting schemes in terms of uncertainty and error propagation. The GIS&T Body of Knowledge Knowledge Area 8: Geospatial Data (GD) Objective: Measurements of locations and attributes of phenomena at or near Earth's surface. Earth Geometry Land pationing systems Georeferencing systems (Geographic, Plane coordinate systems, Tessellated, Linear referencing systems) Datums (Horizontal, Vertical datums) Map projections Data quality (Geometric, Thematic) Land surveying and GPS Digitizing 85 The GIS&T Body of Knowledge Knowledge Area 8: Geospatial Data (GD) Field data collection (Sample size Sample intervals Field data technologies) Aerial imaging and photogrammetry Satellite and shipboard remote sensing Metadata, standards and infrastructures The GIS&T Body of Knowledge Knowledge Area 9: GIS&T and Society (GS) Cost, political, ideological, legal, and personal issues associated with GIS ventures Legal aspects Economic aspects Use of geospatial information in public sector Geospatial information as property Dissemination of geospatial information Ethical aspects of geospatial information and technology Critical GIS (thinking critically about GIS) Dr. Joshua Greenfeld (c) 22

24 The GIS&T Body of Knowledge Knowledge Area : Organizational and Institutional Aspects (OI) Management of geographic information systems (GIS)- including hardware, software, data, and workforce-within and among private and public organizations Origins of GIS&T Managing GIS operations and infrastructure Organizational structures and Procedures GIS&T workforce themes The GIS&T Body of Knowledge Knowledge Area : Organizational and Institutional Aspects (OI) Institutional and inter-institutional aspects (Spatial data infrastructures, standards, Technology transfer, sharing among organizations) Coordinating organizations (national and international) (Federal agencies, State and regional coordinating bodies, Professional organizations, Publications) GIS Paths Surveying Professional Paths Outcomes Outcomes Career Career 2 Business & Management Computer & Information Sciences Quantitative Methods Photogrammetry GIS Applications GPS Applications humanities Core Physical Science Business & Management Core Academia Social Science General Education Engineering Boundary Cadastre Special Surveys Research Software Development 9 92 Dr. Joshua Greenfeld (c) 23

25 Three Suggested Roles Played by Surveyors in GIS&T Category User Specialist Scholar Level of involvement Routine use of basic GIS technology GIS application design and development GIS research and development 93 Levels of competencies (Anderson & Krathwohl, 2) Remembering: Retrieving, recognizing, and recalling relevant knowledge from long-term memory. Understanding: Constructing meaning from oral, written, and graphic messages through interpreting, exemplifying, classifying, summarizing, inferring, comparing, and explaining. Applying: Carrying out or using a procedure through executing, or implementing. Analyzing: Breaking material into constituent parts, determining how the parts relate to one another and to an overall structure or purpose through differentiating, organizing, and attributing. Evaluating: Making judgments based on criteria and standards through checking and critiquing. Creating: Putting elements together to form a coherent or functional whole; reorganizing elements into a new pattern or structure through generating, planning, or producing. 94 Levels of competencies (Greenfeld (et. al, 28) Recognition represents a reasonable level of familiarity with a concept but lacks the knowledge to specify and procure solutions without additional expertise. Understanding implies a thorough mental grasp and comprehension of a concept or topic. Understanding typically requires more than abstract knowledge. Ability is a capability to perform with competence. As one grows professionally, his/her abilities also develop so that more challenging and difficult problems can be solved. 95 GIS BoK for Surveying Knowledge Area: Analytical Methods (AM) Query operations and query languages U A A Geometric measures A A A Basic analytical operations A A A Basic analytical methods A A A Analysis of surfaces A A A Spatial statistics U U A Geostatistics R U A Spatial regression and econometrics R R R Data mining R U Network analysis U U Optimization and location-allocation modeling R A 96 Dr. Joshua Greenfeld (c) 24

26 GIS BoK for Surveying Knowledge Area: Conceptual Foundations (CF) GIS BoK for Surveying Knowledge Area: Cartography and Visualization (CV) Philosophical foundations U U A Cognitive and social foundations R U R Domains of geographic information U A A Elements of geographic information A A A Relationships U A A Imperfections in geographic information U A A History and trends A A A Data considerations U A A Principles of map design A A A Graphic representation techniques A A A Map production U A U Map use and evaluation A A A GIS BoK for Surveying Knowledge Area: Design Aspects (DA) GIS BoK for Surveying Knowledge Area: Data Modeling (DM) The scope of GlS&T U A A system design R A A Project definition R A A Resource planning R A A Database design A A Analysis design A A Application design A A System implementation A A Basic storage and retrieval structures A A A Database management systems U A A Tessellation data models R U A Vector and object data models A A A Modeling 3D, temporal, and uncertain phenomena R U A 99 Dr. Joshua Greenfeld (c) 25

27 GIS BoK for Surveying Knowledge Area: Data Manipulation (DN) Representation transformation A A A Generalization and aggregation R U A Transaction management of geospatial R R A data GIS BoK for Surveying Knowledge Area: Geocomputation (GC) Emergence of geocomputation R U A Computational aspects and neurocomputing A Cellular Automata (CA) models Heuristics Genetic algorithms (GA) Agent-based models Simulation modeling A Uncertainty R A Fuzzy sets A A A A A 2 GIS BoK for Surveying Knowledge Area: Geospatial Data (GD) Earth geometry A A A Land partitioning systems A A A Georeferencing systems A A A Datums A A A Map projections A A A Data quality A A A Land surveying and GPS A A A Digitizing A A A Field data collection A A A Aerial imaging and photogrammetry A A A Satellite and shipboard remote sensing A A A GIS BoK for Surveying Knowledge Area: GIS&T and Society (GS) Legal aspects A A U Economic aspects R U U Use of geospatial information in the public R U U sector Geospatial information as property A A U Dissemination of geospatial information U A U Ethical aspects of geospatial R A U information and technology Critical GIS U Metadata, standards, and infrastructures U A 3 A 4 Dr. Joshua Greenfeld (c) 26

28 GIS BoK for Surveying Knowledge Area: Organizational and Institutional Aspects (OI) Origins of GIS&T R U U Managing GIS operations and R A U infrastructure Organizational structures and procedures A U GIS&T workforce themes U R Institutional and inter-institutional A R aspects Coordinating organizations (national and international) A GIS Education for surveyors Routine user R R P - Specialist R R R P Scholar R P P R 5 6 The role of surveyors The relationship of surveyors and civil engineers to the GIS market is of critical importance. Civil engineers design and build this infrastructure while surveyors collect and deliver highly accurate geospatial data, fueling multiple industries and levels of government. Using GIS technology, surveyors and civil engineers play a key role in the GIS data improvement process and contribute their expertise on design, measurement, accuracy, and precision to improve the quality/accuracy of GIS data. Thank you! 7 8 Dr. Joshua Greenfeld (c) 27