# Towards a generalized map algebra: principles and data types

Save this PDF as:

Size: px
Start display at page:

Download "Towards a generalized map algebra: principles and data types"

## Transcription

1 Towards a generalized map algebra: principles and data types Gilberto Câmara, Danilo Palomo, Ricardo Cartaxo Modesto de Souza, Olga Regina Fradico de Oliveira Image Processing Division (DPI) National Institute for Space Research (INPE) Av dos Astronautas, São José dos Campos SP Brazil {gilberto, danilo, cartaxo, Abstract. Map Algebra is a collection of functions for handling continuous spatial data, which allows modeling of different problems and getting new information from the existing data. There is an established set of map algebra functions in the GIS literature, originally proposed by Dana Tomlin. However, the question whether his proposal is complete is still an open problem in GIScience. This paper describes the design of a map algebra that generalizes Tomlin s map algebra by incorporating topological and directional spatial predicates. Our proposal enables operations that are not directly expressible by Tomlin s proposal. One of the important results of our paper is to show that Tomlin s Map Algebra can be defined as an application of topological predicates to coverages. This paper points to a convergence between these two approaches and shows that it is possible to develop a foundational theory for GIScience where topological predicates are the heart of both object-based algebras and field-based algebras. 1. Introduction A map is useful metaphor for dealing with data in geographical information system (GIS) [Smith 1995; Egenhofer, Glasgow et al. 1999]. Of particular interest in GIS are maps associated to a continuous variable or to a categorical classification of space (e.g., soil maps). These types of maps are called coverages [Frank, Volta et al. 1992; Volta and Egenhofer 1993; Erwig and Schneider 2000]. Different functions on coverages such as overlay and reclassification have been proposed in the literature, composing a set of procedures called Map Algebra. They allow the user to model different problems and to get new information from the existing data set [Berry 1987; Frank 1987; Güting 1988; Huang, Svensson et al. 1992]. The main contribution to map algebra comes from the work of Tomlin [1990]. Tomlin s model uses a single data type (a map), and defines three types of functions. Local functions involve matching locations in different map layers, as in classify as high risk all areas without vegetation with slope greater than 15%. Focal functions involve proximal locations in the same layer, as in the expression calculate the local mean of the map values. Zonal functions summarize values at locations in a layer contained in zones defined in another layer, as in the example given a map of city and a digital terrain model, calculate the mean altitude for each city. Tomlin s map algebra has become as a standard way of processing coverages, especially for multicriteria analysis. In recent years, several extensions to map algebra

2 have been proposed. These include the GeoAlgebra of Takeyama and Couclelis [1997], an extension of map algebra that allows for flexible definitions of neighborhoods. Pullar [2001] developed MapScript, a language that allows control structures and dynamical models to be incorporated into map algebra. Ostlander [2004] suggests how map algebra could be embedded in a web service. Mennis et al. [2005] propose an extension of map algebra for spatio-temporal data handling. Frank [2005] discusses how map algebra can be formalized in a functional programming context and how this approach provides support both for spatial and spatio-temporal operations. Nevertheless, all extensions share the ad hoc nature of Tomlin s original proposal. They accept the foundations of Tomlin s algebra as a basis for their work. Therefore, one of the open challenges in spatial information science is to develop a theoretical foundation for map algebra. We need to find out if Tomlin s map algebra can be part of a more general set of operations on coverages. We state these questions as: What is the theoretical foundation for map algebra? Could this theoretical foundation provide support for a more generic map algebra? A second open issue involves the geometrical representations used in map algebra. Conceptually, map algebra could be implemented both on raster and vector representations [Timpf and Frank 1997]. In practice, however, we need topological and directional definitions suitable for using Tomlin s algebra with vector data. The absence of such definitions has limited map algebra implementations to use raster representations. This brings about a further question: How can we define a generalized map algebra suitable for both raster and vector representations? To respond to these questions, we take the topological predicates of Egenhofer and Herring [1991] and the directional predicates of Frank [1992] as a basis for defining an algebra for coverages. Since these predicates are a good foundation for spatial query languages, it is natural to extend them on a map algebra context. Using these predicates, we propose an extended map algebra that includes Tomlin s as a subset. In what follows, we briefly review Tomlin s map algebra in section 2. In section 3, we present our set of map algebra operations. In section 4, we show some examples of its use, including some functions that are not expressible in Tomlin s work. 2. Map algebra: a brief review 2.1. Basic definitions A map is a function f: G A, where: 1. The domain is a two-dimensional geographical extent G R 2 which matches the planar coordinates of a geographical region in space. The extent G is partitioned into disjoint regions r 1,..., r n, such r i = G. 2. The range is a set of attribute values whose domain is A. For any 2D region r G, a map returns a value f (r) = a, where a A. Note that this function knows how to calculate the value for any arbitrary region which is inside the area G. This definition of a map is general enough to include both numerical and categorical coverages defined in both vector and raster representations (here, we follow the OGC s definition of coverage; for more details, see [OGC 1998]).

3 There are two classes of functions in map algebra. First order functions take values as arguments (these are the functions associated to the map values). Higher order functions are functions that have other functions as arguments. Higher order functions are the basis for map algebra operations [Frank 1997]. Since maps are essentially firstorder functions, operations on maps are applications of higher-order functions on all elements of a map. An example is classify as high risk all areas with slope greater than 15%. To perform this operation, every element of a map is checked for the condition slope > 15% and those that satisfy it are labeled as high risk areas. The combination of the selection predicate and the classification function is the higher-order function. There are two ways to define higher-order functions for map algebra. The first is to define explicitly a special set of such functions. This is the approach taken by functional languages [Frank, 1977]. The second is to use implicit definitions. Functions can act has higher-order if they are applied to all elements of a map. The first approach is cleaner mathematically, but the second approach is more intuitive and has been more often used in practice. Examples of such functions include: Single argument mathematical functions: log, exp, sin, cosine, tan, arcsin, arccosine, arctan, sinh, cosineh, tanh, arcsinh, arccosineh, arctanh, sqrt, power, mod, ceiling, floor. Single argument logical function: not. Multiargument functions: sum, product, and, or, maximum, minimum, mean, median, variety, majority, minority, ranking, count Tomlin s map algebra Tomlin s map algebra [1990] defines three types of higher-order functions for coverages. These functions apply a first-order function to all elements of map, according to different spatial restrictions: Local functions. The value of a location in the output map is computed from the values of the same location in one or more input maps. They include logical expressions such as classify as high risk all areas without vegetation with slope greater than 15% (Figure 1.a). Focal functions. The value of a location in the output map is computed from the values of the neighborhood of the same location in the input map. They include expressions such as calculate the local mean of the map values (Figure 1.b). Focal functions use the condition of adjacency, which matches the spatial predicate touch. Zonal functions: The value of a location in the output map is computed from the values of a spatial neighborhood of the same location in an input map. This neighborhood is a restriction on a second input map. They include expressions such as given a map of cities and a digital terrain model, calculate the mean altitude for each city (Figure 1.c). Zonal functions use the condition of topological containment, which matches the spatial predicate inside.

4 a. Local functions b. Focal functions c. Zonal functions Figure 1. Tomlin s higher-order functions for map algebra (source: Tomlin [1990]) 3. A generalized map algebra for coverages This section presents a proposal that generalizes Tomlin s algebra. Our proposal only considers topological and directional relations between areas. A more complete proposal would also include other types of relations, such as line-area and line-line relations. However, we consider that a map algebra based on area-area predicates to be a useful tool for geographical analysis. The proposed map algebra has nonspatial and spatial higher-order functions. The former are equivalent to Tomlin s local functions and use single values in input maps. The latter generalizes Tomlin s focal and zonal functions using topological and directional predicates Conventions used in the paper To define our map algebra, we need to define the data types and its functions. Types are a means of expressing abstractions in a computer language [Cardelli and Wegner 1985]. Types provide support for expressing abstractions by separating between specification and implementation. In the following definitions, we adopt some conventions: 1. Data types, functions and instances use monospaced font. Types are in boldface and their instances shown in normal font. For lists associate to types, we use list_typename. 2. Type definitions follow usual conventions for abstract data types. Types have an externally viewable set of functions and a set of axioms that are applicable to these functions [Guttag 1977]. 3. Each function has a signature, given as function: typea typeb out_type. TypeA and typeb are the types of the input parameters and out_type is the type of the output. 4. We describe each function in pseudocode. Instructions for flow control (e.g., for each) are in boldface. For attribution, we use :=. For equality test, we use == and the separator is ;.

5 3.2. The map data type Our basic data type is a map. Table 1 shows the definition of the map abstract data type. The definition assumes the existence of basic types region, attr_domain and comparison. They are, respectively, a 2D region, the domain of the attribute associated to the map and a comparison predicate. Type map uses region, attr_domain Function Table 1. The map data type Instances used in the description m: map r: region val: attr_domain Signature getregions map list_region Retrieve the list of regions of a map. contains map region bool Test if a map contains a region. overlaps map region bool insert retrieve new add remove Test if a region overlaps the extent of a map. map region attr_domain map Given a value and region, insert the value in a map. The shorthand for this function is m[r]:= val. map region attr_domain Given a region, get its attribute value. The shorthand for this function is val:= m[r]. map Create a new map. map region map Adds a region to a map. map region map Removes a region from a map. Axioms (getregions (m)) == G (the extent of the map) The union of the regions of the map is its extent. contains (m, r) == true iff r G Tests if a region is inside a map s extent. insert(m,r,val) == error iff contains (m,r) == false Inserting values into regions outside a map is impossible.

7 Figure 2. Topological predicates for area-area based on the 9-intersection matrix. Adapted from Egenhofer and Herring [1991] as adapted by the Open Geospatial consortium [OGC 1998] The predicates in the minimal set { disjoint, equal, touch, inside, overlap } are mutually exclusive and cover all topological cases for area-area relations [Egenhofer and Herring 1991]. The predicate intersects is included for user convenience, following OGC s proposal [OGC 1998]., and intersects is defined as intersects(a,b)! disjoint(a,b) We also use the directional predicates north, south, east, west, center, northwest, northeast, southeast, southwest, originally proposed by Frank [1992]. Definition of these predicates for raster data is straightforward [Winter and Frank 2000] (see Figure 3). For area-area vector representation, Papadias and Egenhofer [1997] provide a set of suitable definitions for directional predicates. Figure 4 shows examples of application of semantic predicates. Figure 3. Directional predicates in raster representation. Source: Winter and Frank [2000]

8 Figure 4. Examples of spatial predicates 3.4. Nonspatial operations Nonspatial operations are higher-order functions that take one value for each input map and produce one value in the output map, using a first-order function as argument. They are the equivalent to Tomlin s local operations (see Figure 1.a). These operators include single argument functions, multiple argument functions, and a selection function, as described in Table 2. Table 2. Nonspatial operators Type nonspatial_operation uses map, attr_domain, func_single, func_multiple, comp (comparison is the type of comparison predicates, func_single is the type of single-varied functions, and func_multiple is the type of multivaried functions) Instances m1, m2: map maplist: list_map r: region reglist : list_region value: attr_domain valuelist: list_attr_domain func1: func_single funcn: func_multiple Function single Signature map func_single map map For all regions in the output map, find the value in the input map and calculate the output value: single (m1, func1, m2) { } for each r in getregions (m2) if (contains (m1,r) == true) else m2[r]:= func1 ( m1[r] ); m2[r] := undefined;

9 multiple maplist func_multiple map map For all regions in the output map, find the values in the input maps and calculate the output value: multiple (maplist, funcn, m1) { } } for each r in getregions (m1) { } init valuelist; for each m in maplist { if (contains (m,r) == true) else value := m[r]; value := undefined; add value to valuelist; m1[r] := funcn (valuelist); select map attr_domain comp map map For all regions in the output map, find the values in the input map and compare with a selected value. The resulting map will be a boolean map: select (map1, value, comp, map2) { for each r in getregions (map2) { }} if (contains (map1, r)) else map2[r]:= (map1[r] comp val); map2[r]:= undefined; Nonspatial operations have a shorthand, as shown in Table 3. We also show some examples of nonspatial operations in Table 4. Table 3. Convenience shorthand for nonspatial operators output_map := single_arg_function (input_map); // for single value functions output_map := multi_arg_function (input_map_list); // for multivalued mathematical functions output_map:= map1 comp map2; // for logical and comparison operators output_map := input_map comp value; // for selection operations

10 Informal description Find the square root of the topography map Find the average of deforestation in the last two years Table 4. Examples of nonspatial operators Map Algebra Expression toposqr := sqrt (topography); defave := mean (defor2004, defor2003); Select areas higher than 500 meters high:= (topography > 500); Select areas higher than 500 meters with temperatures lower than 10 degrees 3.5. Spatial operations high_cold := (topo > 500) and (temp < 10); Spatial operations are higher-order functions that use a spatial predicate. These functions combine a selection function and a multivalued function, with two input maps (the reference map and the value map) and an output map. Spatial operations generalize Tomlin s focal and zonal operations and have two parts: selection and composition. For each location in the output map, the selection function applies a spatial predicate on the matching location in the input map. The result is a set of values that satisfy the predicate. The composition function uses the selected values to produce the result (Figure 5). The selection function takes a region in the output map and finds the matching region on the reference map. Then it applies the spatial predicate between the reference map and the value map and creates a set of values. These values are the input for the multivalued function. The implicit assumption is that the geographical area of the output map is the same as reference map. The spatial operation is described in Table 5. Figure 5. Spatial operations (selection + composition). Adapted from Tomlin [1990].

11 Table 5. Spatial operations Type spatial uses spat_pred, func_multiple, map (func_multiple is the type of multivalued functions) (spat_pred is the type of spatial predicates) Instances m1, refmap, m2 : map r1, r2 : region v_list: list_attr_domain func: func_multiple pred: spat_pred Function spatial Signature map map pred func_multiple map map Given a region in the reference map, it finds all regions in the value map that satisfy the spatial predicate. For all such regions, include the values of the attribute function in a list. The list is the input to a multivalued function which creates the result. spatial (m1, refmap, pred, func, m2) { } } for each r in getregions (refmap) { init v_list; for each r1 in getregions (m1) { } if pred (r,r1) add m1[r1] to v_list; m2 [r] = func (v_list); The spatial operations have a convenience notation shown in Table 6. Table 6. Convenience notation for spatial operations output_map:= multi_arg_function(value_map spat_pred ref_map); The parameters for the spatial operations are: output_map is the map to be filled with the new values. multi_arg_function is a multiargument function as given in section 3.5. value_map is the map whose values are used to calculate the result. spat_pred is the spatial predicate (section 3.4). ref_map is the reference map used as a basis for applying the spatial predicate.

12 4. Examples and comparison with Tomlin s map algebra In this section, we give some examples and compare our proposal to Tomlin s map algebra. First, we consider the operation: Find the local sum of regions in a deforestation map. In Tomlin s algebra, this is a focal operation (Figure 1.b). Considering the deforestation map (defor) as input and the local sum map (lsum) as output, we state the operation as: lsum := sum (defor touch lsum); Figure 6. Local sum of deforestation Note one interesting feature: the result (lsum) is also the reference map for the spatial predicate (defor touch lsum). This syntax may seem odd at first sight, but it follows from the generality of the proposal. By taking the reference map and the result map to be the same, we ensure the outcome satisfies the required condition ( local mean ). Had we used a third map as a reference, the result would be different if this map would not have the same spatial partitioning as the output map. A second example would be the statement given a map of cities and a digital terrain model, find the average height of each city. This is a zonal operation in Tomlin s map algebra (Figure 1.c). Considering the digital terrain model (dtm_map) as the input map and the cities map (cities_map) as the reference, the operation is expressed as: ave_city_height := average (dtm_map inside cities_map); In this case, the output map will have the same spatial partitioning as the cities map. For each region in the output map, the algorithm finds the matching region in the cities map (which is trivial), and then applies the inside predicate between this region and all regions in the digital terrain model (DTM). The result will be a set of regions with associated values. Then, it applies a multivalued function (in this case, average) to get the result. A third example is: Given a map of a native reservations and a deforestation map, find the sum of deforestation in the native reservations. This is also a zonal operation in Tomlin s classification (Figure 1.c). Taking the map of native reservations (reserves) as the reference map and the deforestation map (defor) as the value map, we express the operation as:

13 deforres := sum (defor inside reserves); Deforestation map (grid) and native reservations (polygons) Result (deforres) Figure 7. Calculation of deforestation in native reservations The above examples show how the proposed map algebra expresses the spatial functions of Tomlin s map algebra, using touch and inside. Operators that use topological predicates other than touch and inside have no equivalent in Tomlin s algebra. The following are part of our map algebra and are not directly expressible by Tomlin s algebra: (a) operations involving the overlap, contains and intersects predicates; (b) operations involving directional predicates. One example is the statement: Given a map containing a road and a deforestation map, calculate the mean of the deforestation along the road. We have this input maps: deforestation (defor) and road. We select the areas that satisfy the condition of intersection and take the mean of the resulting values. These steps are illustrated in Figure 8. The expression is defroad := mean (defor intersects road); Figure 8. Calculation of the average deforestation along a oad

14 We present a comparison between the spatial operators and the focal and zonal expressions in Tomlin s map algebra in Table 7, where some operations have no direct equivalent in his algebra. In fairness to Tomlin, we should remember that his proposal mainly considers raster representations with the same spatial resolution, where some of these conditions would not be relevant. For example, the overlap predicate would not be applicable in his case. Table 7. Comparison of spatial operators with Tomlin s map algebra Informal Description Generalized Map Algebra Tomlin Local mean of topography Given a map of cities and a topography map, calculate the mean altitude for each city. lmean:= mean (topo touch lmean) altcit:= mean (topo inside city) FOCALMEAN OF TOPOGRAPHY ZONALMEAN OF TOPOGRAPHY WITHIN CITIES Given a set of national forests, calculate the deforestation at the edges of each forest Caculate the mean of the deforestation along the road Find the deforestation to the north of the Amazon. defbord:= sum (defor overlaps forests) defroad := mean (defor intersects road); defnorth:= sum (defor north amazon) (no equivalent) (no equivalent) (no equivalent) 5. Conclusions This paper presents a generalized map algebra and compares it to the classical proposal by Tomlin. Our map algebra uses topological and directional spatial predicates. It expresses all Tomlin s algebra operations and enables operations that are not directly expressible by his proposal. Further generalizations of the proposed algebra are possible by involving the full set of topological operations. The next step in our work is to design, implement and validate a language that supports the proposed map algebra for spatial databases. One of the important results of our paper is to show that Tomlin s Map Algebra can be seen as an application of topological predicates to coverages. Previously, topological operations on individual objects and those involving coverages have been dealt separately in GIScience theory. This paper points to a convergence between these two approaches and shows that it is possible to develop a foundational theory for GIScience where topological operations are the heart of both operations on objects and operations on fields. This theory would tie different types of spatial operations together into a single unified basis.

15 Acknowledgments Gilberto Camara s work is partially funded by CNPq (grants PQ / and /2005-0) and FAPESP (grant 04/ ). Danilo Palomo s and Olga Oliveira s work is funded by CAPES. Gilberto Câmara would like to thank Andrew Frank for many stimulating discussions on the subjects of map algebra and functional programming. References Berry, J. K. (1987). "Fundamental Operations in Computer-Assisted Map Analysis." International Journal of Geographical Information Systems 1(2): Cardelli, L. and P. Wegner (1985). "On Understanding Type, Data Abstraction, and Polymorphism." ACM Computing Surveys 17(4): Egenhofer, M., J. Glasgow, O. Gunther, et al. (1999). "Progress in Computational Methods for Representing Geographic Concepts." International Journal of Geographical Information Science 13(8): Egenhofer, M. and J. Herring (1991). Categorizing Binary Topological Relationships Between Regions, Lines, and Points in Geographic Databases. Orono, ME, Department of Surveying Engineering, University of Maine. Erwig, M. and M. Schneider (2000). Formalization of Advanced Map Operations. 9th Int. Symp. on Spatial Data Handling Beijing, IGU. Frank, A. (1987). Overlay Processing in Spatial Information Systems. AUTO-CARTO 8, Eighth International Symposium on Computer-Assisted Cartography. N. R. Chrisman. Baltimore, MD: Frank, A. (1992). "Qualitative Spatial Reasoning about Distances and Directions in Geographic Space." Journal of Visual Languages and Computing 3(4): Frank, A. (1997). Higher order functions necessary for spatial theory development. Auto-Carto 13, Seattle, WA, ACSM/ASPRS. Frank, A. (2005). Map Algebra Extended with Functors for Temporal Data. Perspectives in Conceptual Modeling: ER 2005 Workshops, Klagenfurt, Austria, Springer. Frank, A., G. Volta and M. McGranaghan (1992). Formalization of Families of Categorical Coverages, University of Maine. Güting, R. (1988). Geo-Relational Algebra: A Model and Query Language for Geometric Database Systems. Advances in Database Technology EDBT '88 International Conference on Extending Database Technology, Venice, Italy. J. Schmidt, S. Ceri and M. Missikoff. New York, NY, Springer-Verlag. 303: Guttag, J. (1977). "Abstract Data Types And The Development Of Data Structures." Communications of the ACM 20(6): Huang, Z., P. Svensson and H. Hauska (1992). Solving Spatial Analysis Problems with GeoSAL, a Spatial Query Language. 6th Int. Working Conf. on Scientific and Statistical Database Management.

16 Mennis, J., R. Viger and D. Tomlin (2005). "Cubic Map Algebra Functions for Spatio- Temporal Analysis." Cartography and Geographic Information Science 32(1): OGC (1998). OpenGIS Simple Features Specification for SQL. Boston, Open GIS Consortium. OGC (1998). The OpenGIS Specification Model: The Coverage Type and Its Subtypes. Wayland, MA, Open Geospatial Consortium. Ostländer, N. (2004). Interoperable Services for Web-Based Spatial Decision Support. 7th AGILE Conference on Geographic Information Science, Heraklion, Greece, AGILE. Papadias, D. and M. Egenhofer (1997). "Hierarchical Spatial Reasoning about Direction Relations." GeoInformatica 1(3): Pullar, D. (2001). "MapScript: A Map Algebra Programming Language Incorporating Neighborhood Analysis." GeoInformatica 5(2): Smith, B. (1995). On Drawing Lines on a Map. Spatial Information Theory A Theoretical Basis for GIS, International Conference COSIT '95, Semmering, Austria. A. Frank and W. Kuhn. Berlin, Springer Verlag. 988: Takeyama, M. and H. Couclelis (1997). "Map Dynamics: Integrating Cellular Automata and GIS through Geo-Algebra." International Journal of Geographical Information Systems 11(1): Timpf, S. and A. Frank (1997). Using hierarchical spatial data structures for hierarchical spatial reasoning. Spatial Information Theory - A Theoretical Basis for GIS (Int. Conference COSIT'97), Berlin, Springer-Verlag. Tomlin, C. D. (1990). Geographic Information Systems and Cartographic Modeling. Englewood Cliffs, NJ, Prentice-Hall. Volta, G. and M. Egenhofer (1993). Interaction with GIS Attribute Data Based on Categorical Coverages. Spatial Information Theory, European Conference COSIT '93, Marciana Marina, Elba Island, Italy. A. Frank and I. Campari. New York, NY, Springer-Verlag. 716: Winter, S. (1995). Topological Relations between Discrete Regions. Advances in Spatial Databases 4th International Symposium, SSD 95, Portland, ME. M. Egenhofer and J. Herring. Berlin, Springer-Verlag. 951: Winter, S. and A. Frank (2000). "Topology in Raster and Vector Representaion." GeoInformatica 4(1):

### SPATIAL ANALYSIS IN GEOGRAPHICAL INFORMATION SYSTEMS. A DATA MODEL ORffiNTED APPROACH

POSTER SESSIONS 247 SPATIAL ANALYSIS IN GEOGRAPHICAL INFORMATION SYSTEMS. A DATA MODEL ORffiNTED APPROACH Kirsi Artimo Helsinki University of Technology Department of Surveying Otakaari 1.02150 Espoo,

### The Architecture of a Flexible Querier for Spatio-Temporal Databases

The Architecture of a Flexible Querier for Spatio-Temporal Databases Karine Reis Ferreira, Lúbia Vinhas, Gilberto Ribeiro de Queiroz, Ricardo Cartaxo Modesto de Souza, Gilberto Câmara Divisão de Processamento

### Algebraic Formalism over Maps

Algebraic Formalism over Maps João Pedro Cordeiro 1, Gilberto Câmara 1, Ubirajara F. Moura 2, Cláudio C. Barbosa 1, Felipe Almeida 3 1 Divisão de Processamento de Imagens Instituto Nacional de Pesquisas

### Oracle8i Spatial: Experiences with Extensible Databases

Oracle8i Spatial: Experiences with Extensible Databases Siva Ravada and Jayant Sharma Spatial Products Division Oracle Corporation One Oracle Drive Nashua NH-03062 {sravada,jsharma}@us.oracle.com 1 Introduction

### Raster Operations. Local, Neighborhood, and Zonal Approaches. Rebecca McLain Geography 575 Fall 2009. Raster Operations Overview

Raster Operations Local, Neighborhood, and Zonal Approaches Rebecca McLain Geography 575 Fall 2009 Raster Operations Overview Local: Operations performed on a cell by cell basis Neighborhood: Operations

### Introduction to GIS (Basics, Data, Analysis) & Case Studies. 13 th May 2004. Content. What is GIS?

Introduction to GIS (Basics, Data, Analysis) & Case Studies 13 th May 2004 Content Introduction to GIS Data concepts Data input Analysis Applications selected examples What is GIS? Geographic Information

### DEVELOPMENT OF AN INTEGRATED IMAGE PROCESSING AND GIS SOFTWARE FOR THE REMOTE SENSING COMMUNITY. P.O. Box 515, São José dos Campos, Brazil.

DEVELOPMENT OF AN INTEGRATED IMAGE PROCESSING AND GIS SOFTWARE FOR THE REMOTE SENSING COMMUNITY UBIRAJARA MOURA DE FREITAS 1, ANTÔNIO MIGUEL MONTEIRO 1, GILBERTO CÂMARA 1, RICARDO CARTAXO MODESTO SOUZA

### GEOGRAPHIC INFORMATION SYSTEMS CERTIFICATION

GEOGRAPHIC INFORMATION SYSTEMS CERTIFICATION GIS Syllabus - Version 1.2 January 2007 Copyright AICA-CEPIS 2009 1 Version 1 January 2007 GIS Certification Programme 1. Target The GIS certification is aimed

### Reading Questions. Lo and Yeung, 2007: 2 19. Schuurman, 2004: Chapter 1. 1. What distinguishes data from information? How are data represented?

Reading Questions Week two Lo and Yeung, 2007: 2 19. Schuurman, 2004: Chapter 1. 1. What distinguishes data from information? How are data represented? 2. What sort of problems are GIS designed to solve?

### Vector Overlay Processing - Specific Theory

Vector Overlay Processing - Specific Theory Cartographic Modelling and GIS. Cartographic modelling is a general, but well-defined methodology that is used to address diverse applications of GIS in a clear

### ADVANCED GEOGRAPHIC INFORMATION SYSTEMS Vol. II - Using Ontologies for Geographic Information Intergration Frederico Torres Fonseca

USING ONTOLOGIES FOR GEOGRAPHIC INFORMATION INTEGRATION Frederico Torres Fonseca The Pennsylvania State University, USA Keywords: ontologies, GIS, geographic information integration, interoperability Contents

### Fields as a Generic Data Type for Big Spatial Data

Fields as a Generic Data Type for Big Spatial Data Gilberto Camara 1,2, Max J. Egenhofer 3, Karine Ferreira 1, Pedro Andrade 1, Gilberto Queiroz 1, Alber Sanchez 2, Jim Jones 2, Lúbia Vinhas 1 1 Image

### GDMS-R: A mixed SQL to manage raster and vector data

GDMS-R: A mixed SQL to manage raster and vector data Thomas Leduc, Erwan Bocher, Fernando González Cortés, Guillaume Moreau To cite this version: Thomas Leduc, Erwan Bocher, Fernando González Cortés, Guillaume

### THE ARCHITECTURE OF ARC/INFO Scott Morehouse Environmental Systems Research Institute 380 New York Street Redlands, California

THE ARCHITECTURE OF ARC/INFO Scott Morehouse Environmental Systems Research Institute 380 New York Street Redlands, California ABSTRACT Arc/Info is a generalized system for processing geographic information.

### Draft Martin Doerr ICS-FORTH, Heraklion, Crete Oct 4, 2001

A comparison of the OpenGIS TM Abstract Specification with the CIDOC CRM 3.2 Draft Martin Doerr ICS-FORTH, Heraklion, Crete Oct 4, 2001 1 Introduction This Mapping has the purpose to identify, if the OpenGIS

### Spatial SQL: A Query and Presentation Language

Spatial SQL: A Query and Presentation Language Max J. Egenhofer, Member, IEEE Abstract Recently, attention has been focused on spatial databases which combine conventional and spatially related data such

### Middle Grades Mathematics 5 9

Middle Grades Mathematics 5 9 Section 25 1 Knowledge of mathematics through problem solving 1. Identify appropriate mathematical problems from real-world situations. 2. Apply problem-solving strategies

### A RDF Vocabulary for Spatiotemporal Observation Data Sources

A RDF Vocabulary for Spatiotemporal Observation Data Sources Karine Reis Ferreira 1, Diego Benincasa F. C. Almeida 1, Antônio Miguel Vieira Monteiro 1 1 DPI Instituto Nacional de Pesquisas Espaciais (INPE)

### Objectives. Raster Data Discrete Classes. Spatial Information in Natural Resources FANR 3800. Review the raster data model

Spatial Information in Natural Resources FANR 3800 Raster Analysis Objectives Review the raster data model Understand how raster analysis fundamentally differs from vector analysis Become familiar with

### An architecture for open and scalable WebGIS

An architecture for open and scalable WebGIS Aleksandar Milosavljević, Leonid Stoimenov, Slobodanka Djordjević-Kajan CG&GIS Lab, Department of Computer Science Faculty of Electronic Engineering, University

### Fields as a Generic Data Type for Big Spatial Data

Fields as a Generic Data Type for Big Spatial Data Gilberto Camara 1,2, Max J. Egenhofer 3, Karine Ferreira 1, Pedro Andrade 1, Gilberto Queiroz 1, Alber Sanchez 2, Jim Jones 2, Lubia Vinhas 1 1 Image

### GIS & Spatial Modeling

Geography 4203 / 5203 GIS & Spatial Modeling Class 4: Raster Analysis I Some Updates Readings discussions Reminder: Summaries!!! Submitted BEFORE discussion starts Labs next week: Start (delayed) of lab

### AIMMS Function Reference - Arithmetic Functions

AIMMS Function Reference - Arithmetic Functions This file contains only one chapter of the book. For a free download of the complete book in pdf format, please visit www.aimms.com Aimms 3.13 Part I Function

### Assessment of Workforce Demands to Shape GIS&T Education

Assessment of Workforce Demands to Shape GIS&T Education Gudrun Wallentin, Barbara Hofer, Christoph Traun gudrun.wallentin@sbg.ac.at University of Salzburg, Dept. of Geoinformatics Z_GIS, Austria www.gi-n2k.eu

### ADVANCED DATA STRUCTURES FOR SURFACE STORAGE

1Department of Mathematics, Univerzitni Karel, Faculty 22, JANECKA1, 323 of 00, Applied Pilsen, Michal, Sciences, Czech KARA2 Republic University of West Bohemia, ADVANCED DATA STRUCTURES FOR SURFACE STORAGE

### GIS & Spatial Modeling

Geography 4203 / 5203 GIS & Spatial Modeling Class 2: Spatial Doing - A discourse about analysis and modeling in a spatial context Updates Class homepage at: http://www.colorado.edu/geography/class_homepages/geog_4203

### Requirements engineering for a user centric spatial data warehouse

Int. J. Open Problems Compt. Math., Vol. 7, No. 3, September 2014 ISSN 1998-6262; Copyright ICSRS Publication, 2014 www.i-csrs.org Requirements engineering for a user centric spatial data warehouse Vinay

### CHAPTER-24 Mining Spatial Databases

CHAPTER-24 Mining Spatial Databases 24.1 Introduction 24.2 Spatial Data Cube Construction and Spatial OLAP 24.3 Spatial Association Analysis 24.4 Spatial Clustering Methods 24.5 Spatial Classification

VistaPro User Guide Custom Variable Expressions IES Virtual Environment Copyright 2015 Integrated Environmental Solutions Limited. All rights reserved. No part of the manual is to be copied or reproduced

### A Hybrid Architecture for Mobile Geographical Data Acquisition and Validation Systems

A Hybrid Architecture for Mobile Geographical Data Acquisition and Validation Systems Claudio Henrique Bogossian 1, Karine Reis Ferreira 1, Antônio Miguel Vieira Monteiro 1, Lúbia Vinhas 1 1 DPI Instituto

### SPATIAL DATA ANALYSIS

SPATIAL DATA ANALYSIS P.L.N. Raju Geoinformatics Division Indian Institute of Remote Sensing, Dehra Dun Abstract : Spatial analysis is the vital part of GIS. Spatial analysis in GIS involves three types

### SPATIAL DATABASES AND GEOGRAPHICAL INFORMATION SYSTEMS (GIS)

zk SPATIAL DATABASES AND GEOGRAPHICAL INFORMATION SYSTEMS (GIS) HANAN SAMET COMPUTER SCIENCE DEPARTMENT AND CENTER FOR AUTOMATION RESEARCH AND INSTITUTE FOR ADVANCED COMPUTER STUDIES UNIVERSITY OF MARYLAND

### Spatial Data Warehouse and Mining. Rajiv Gandhi

Spatial Data Warehouse and Mining Rajiv Gandhi Roll Number 05331002 Centre of Studies in Resource Engineering Indian Institute of Technology Bombay Powai, Mumbai -400076 India. As part of the first stage

### ANALYSIS 3 - RASTER What kinds of analysis can we do with GIS?

ANALYSIS 3 - RASTER What kinds of analysis can we do with GIS? 1. Measurements 2. Layer statistics 3. Queries 4. Buffering (vector); Proximity (raster) 5. Filtering (raster) 6. Map overlay (layer on layer

### Language. Johann Eder. Universitat Klagenfurt. Institut fur Informatik. Universiatsstr. 65. A-9020 Klagenfurt / AUSTRIA

PLOP: A Polymorphic Logic Database Programming Language Johann Eder Universitat Klagenfurt Institut fur Informatik Universiatsstr. 65 A-9020 Klagenfurt / AUSTRIA February 12, 1993 Extended Abstract The

### Big Ideas in Mathematics

Big Ideas in Mathematics which are important to all mathematics learning. (Adapted from the NCTM Curriculum Focal Points, 2006) The Mathematics Big Ideas are organized using the PA Mathematics Standards

### Spatial data analysis: retrieval, (re)classification and measurement operations

CHAPTER 7 Spatial data analysis: retrieval, (re)classification and measurement operations In chapter 5 you used a number of table window operations, such as calculations, aggregations, and table joining,

### Knowledge Discovery in Spatial Databases: the PADRÃO s qualitative approach

KNOWLEDGE DISCOVERY IN SPATIAL DATABASES The PADRÃO s qualitative approach Maribel Santos and Luís Amaral Abstract Knowledge discovery in databases is a complex process concerned with the discovery of

### Topology. Shapefile versus Coverage Views

Topology Defined as the the science and mathematics of relationships used to validate the geometry of vector entities, and for operations such as network tracing and tests of polygon adjacency Longley

### Chapter 2: Spatial Concepts and Data Models

Chapter 2: Spatial Concepts and Data Models 2.1 Introduction 2.2 Models of Spatial Information 2.3 Three-Step Database Design 2.4 Extending ER with Spatial Concepts 2.5 Summary Learning Objectives Learning

### ADVANCED GEOGRAPHIC INFORMATION SYSTEMS Vol. I - Spatial Query Languages - Agnès Voisard

SPATIAL QUERY LANGUAGES Agnès Voisard Computer Science Institute, FreieUniversität Berlin, Germany Keywords: spatial database management system, geographic information system, data definition language,

### Classification of Fuzzy Data in Database Management System

Classification of Fuzzy Data in Database Management System Deval Popat, Hema Sharda, and David Taniar 2 School of Electrical and Computer Engineering, RMIT University, Melbourne, Australia Phone: +6 3

### Vector storage and access; algorithms in GIS. This is lecture 6

Vector storage and access; algorithms in GIS This is lecture 6 Vector data storage and access Vectors are built from points, line and areas. (x,y) Surface: (x,y,z) Vector data access Access to vector

### TerraML: a Language to Support Spatial Dynamic Modeling

TerraML: a Language to Support Spatial Dynamic Modeling BIANCA PEDROSA GILBERTO CÂMARA FREDERICO FONSECA 2 TIAGO CARNEIRO RICARDO CARTAXO MODESTO DE SOUZA INPE National Institute for Space Research, Caixa

### Raster Data Structures

Raster Data Structures Tessellation of Geographical Space Geographical space can be tessellated into sets of connected discrete units, which completely cover a flat surface. The units can be in any reasonable

### DERIVATION OF THE DATA MODEL

ARC/INFO: A GEO-RELATIONAL MODEL FOR SPATIAL INFORMATION Scott Morehouse Environmental Systems Research Institute 380 New York Street Redlands CA 92373 ABSTRACT A data model for geographic information

### Spatial Data Preparation for Knowledge Discovery

Spatial Data Preparation for Knowledge Discovery Vania Bogorny 1, Paulo Martins Engel 1, Luis Otavio Alvares 1 1 Instituto de Informática Universidade Federal do Rio Grande do Sul (UFRGS) Caixa Postal

### Discrete Mathematics Lecture 3 Elementary Number Theory and Methods of Proof. Harper Langston New York University

Discrete Mathematics Lecture 3 Elementary Number Theory and Methods of Proof Harper Langston New York University Proof and Counterexample Discovery and proof Even and odd numbers number n from Z is called

### Introduction to GIS. Dr F. Escobar, Assoc Prof G. Hunter, Assoc Prof I. Bishop, Dr A. Zerger Department of Geomatics, The University of Melbourne

Introduction to GIS 1 Introduction to GIS http://www.sli.unimelb.edu.au/gisweb/ Dr F. Escobar, Assoc Prof G. Hunter, Assoc Prof I. Bishop, Dr A. Zerger Department of Geomatics, The University of Melbourne

### Tutorial 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

### Curriculum Map by Block Geometry Mapping for Math Block Testing 2007-2008. August 20 to August 24 Review concepts from previous grades.

Curriculum Map by Geometry Mapping for Math Testing 2007-2008 Pre- s 1 August 20 to August 24 Review concepts from previous grades. August 27 to September 28 (Assessment to be completed by September 28)

### Modelling Spatial Relations by Generalized Proximity Matrices

Modelling Spatial Relations by Generalized Proximity Matrices Ana Paula Dutra de Aguiar, Gilberto Câmara 1, Antônio Miguel Vieira Monteiro, Ricardo Cartaxo Modesto de Souza 1 Image Processing Division,

### TerraAmazon - The Amazon Deforestation Monitoring System - Karine Reis Ferreira

TerraAmazon - The Amazon Deforestation Monitoring System - Karine Reis Ferreira GEOSS Users & Architecture Workshop XXIV: Water Security & Governance - Accra Ghana / October 2008 INPE National Institute

### GIS Initiative: Developing an atmospheric data model for GIS. Olga Wilhelmi (ESIG), Jennifer Boehnert (RAP/ESIG) and Terri Betancourt (RAP)

GIS Initiative: Developing an atmospheric data model for GIS Olga Wilhelmi (ESIG), Jennifer Boehnert (RAP/ESIG) and Terri Betancourt (RAP) Unidata seminar August 30, 2004 Presentation Outline Overview

### Weka-GDPM Integrating Classical Data Mining Toolkit to Geographic Information Systems

Weka-GDPM Integrating Classical Data Mining Toolkit to Geographic Information Systems Vania Bogorny, Andrey Tietbohl Palma, Paulo Martins Engel, Luis Otavio Alvares Instituto de Informática Universidade

### REAL-TIME DATA GENERALISATION AND INTEGRATION USING JAVA

REAL-TIME DATA GENERALISATION AND INTEGRATION USING JAVA Lars Harrie and Mikael Johansson National Land Survey of Sweden SE-801 82 Gävle lars.harrie@lantm.lth.se, micke.j@goteborg.utfors.se KEY WORDS:

### Introduction. Introduction. Spatial Data Mining: Definition WHAT S THE DIFFERENCE?

Introduction Spatial Data Mining: Progress and Challenges Survey Paper Krzysztof Koperski, Junas Adhikary, and Jiawei Han (1996) Review by Brad Danielson CMPUT 695 01/11/2007 Authors objectives: Describe

### The process of database development. Logical model: relational DBMS. Relation

The process of database development Reality (Universe of Discourse) Relational Databases and SQL Basic Concepts The 3rd normal form Structured Query Language (SQL) Conceptual model (e.g. Entity-Relationship

### Lesson 15 - Fill Cells Plugin

15.1 Lesson 15 - Fill Cells Plugin This lesson presents the functionalities of the Fill Cells plugin. Fill Cells plugin allows the calculation of attribute values of tables associated with cell type layers.

### Lecture 3: Models of Spatial Information

Lecture 3: Models of Spatial Information Introduction In the last lecture we discussed issues of cartography, particularly abstraction of real world objects into points, lines, and areas for use in maps.

### A quick overview of geographic information systems (GIS) Uwe Deichmann, DECRG

A quick overview of geographic information systems (GIS) Uwe Deichmann, DECRG Why is GIS important? A very large share of all types of information has a spatial component ( 80

### From Topographic Maps to Digital Elevation Models. Anne Graham Daniel Sheehan MIT Libraries IAP 2013

From Topographic Maps to Digital Elevation Models Anne Graham Daniel Sheehan MIT Libraries IAP 2013 Which Way Does the Water Flow? A topographic map shows relief features or surface configuration of an

### What is Where? Getting Started With Geographic Information Systems Chapter 5

What is Where? Getting Started With Geographic Information Systems Chapter 5 You can use a GIS to answer the question: What is where? WHAT: Characteristics of attributes or features WHERE: In geographic

### Visual Interfaces for Geographic Data

Visual Interfaces for Geographic Data Robert Laurini, INSA-Lyon, http://liris.insa-lyon.fr/robert.laurini SYNONYMS Cartography, Visualizing spatial data, interactive capture, interactive layout DEFINITION

### Chapter Contents Page No

Chapter Contents Page No Preface Acknowledgement 1 Basics of Remote Sensing 1 1.1. Introduction 1 1.2. Definition of Remote Sensing 1 1.3. Principles of Remote Sensing 1 1.4. Various Stages in Remote Sensing

### Optimal Cell Towers Distribution by using Spatial Mining and Geographic Information System

World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 Vol. 1, No. 2, -48, 2011 Optimal Cell Towers Distribution by using Spatial Mining and Geographic Information System

GEOGRAPHIC INFORMATION PROCESSING USING A SQL-BASED QUERY LANGUAGE Kevin J. Ingram William W. Phillips Kork Systems, Inc. 6 State Street Bangor, Maine 04401 ABSTRACT The utility of a Land Records or Natural

### IMPLEMENTING SPATIAL DATA WAREHOUSE HIERARCHIES IN OBJECT-RELATIONAL DBMSs

IMPLEMENTING SPATIAL DATA WAREHOUSE HIERARCHIES IN OBJECT-RELATIONAL DBMSs Elzbieta Malinowski and Esteban Zimányi Computer & Decision Engineering Department, Université Libre de Bruxelles 50 av.f.d.roosevelt,

### (MM IS) (Join) GIS CAD, O GIS. ( ERDAS Spatial , GIS Tsinghua Tongfang Optical Disc Co., Ltd. All rights reserved. Vol. 16 No. 4 Nov.

16 4 2000 11 Geography and Territorial Research Vol. 16 No. 4 Nov. 2000,, ( GIS 100871) : (Object) ( Field) GIS,,, : ; ; : TP311. 138 :A :1001-8107 (2000) 04-0073 - 04 [1 ] [2 ], ( ) ( ) (, ) ( ) 20, GIS

### SPATIAL DATA CLASSIFICATION AND DATA MINING

, pp.-40-44. Available online at http://www. bioinfo. in/contents. php?id=42 SPATIAL DATA CLASSIFICATION AND DATA MINING RATHI J.B. * AND PATIL A.D. Department of Computer Science & Engineering, Jawaharlal

### Continuous Spatial Data Warehousing

Continuous Spatial Data Warehousing Taher Omran Ahmed Faculty of Science Aljabal Algharby University Azzentan - Libya Taher.ahmed@insa-lyon.fr Abstract Decision support systems are usually based on multidimensional

### ArcGIS Data Models Practical Templates for Implementing GIS Projects

ArcGIS Data Models Practical Templates for Implementing GIS Projects GIS Database Design According to C.J. Date (1995), database design deals with the logical representation of data in a database. The

### Geospatial Information with Description Logics, OWL, and Rules

Reasoning Web 2012 Summer School Geospatial Information with Description Logics, OWL, and Rules Presenter: Charalampos Nikolaou Dept. of Informatics and Telecommunications National and Kapodistrian University

### Spatial data quality assessment in GIS

Recent Advances in Geodesy and Geomatics Engineering Spatial data quality assessment in GIS DANIELA CRISTIANA DOCAN Surveying and Cadastre Department Technical University of Civil Engineering Bucharest

### Spatial Data Preparation for Knowledge Discovery

Spatial Data Preparation for Knowledge Discovery Vania Bogorny 1, Paulo Martins Engel 1, Luis Otavio Alvares 1 1 Instituto de Informática Universidade Federal do Rio Grande do Sul (UFRGS) Caixa Postal

### Algebra 1 2008. Academic Content Standards Grade Eight and Grade Nine Ohio. Grade Eight. Number, Number Sense and Operations Standard

Academic Content Standards Grade Eight and Grade Nine Ohio Algebra 1 2008 Grade Eight STANDARDS Number, Number Sense and Operations Standard Number and Number Systems 1. Use scientific notation to express

### DATA QUALITY AND SCALE IN CONTEXT OF EUROPEAN SPATIAL DATA HARMONISATION

DATA QUALITY AND SCALE IN CONTEXT OF EUROPEAN SPATIAL DATA HARMONISATION Katalin Tóth, Vanda Nunes de Lima European Commission Joint Research Centre, Ispra, Italy ABSTRACT The proposal for the INSPIRE

### Digital Cadastral Maps in Land Information Systems

LIBER QUARTERLY, ISSN 1435-5205 LIBER 1999. All rights reserved K.G. Saur, Munich. Printed in Germany Digital Cadastral Maps in Land Information Systems by PIOTR CICHOCINSKI ABSTRACT This paper presents

### Development of 3D Cadastre System to Monitor Land Value and Capacity of Zoning (Case study: Tehran)

8 th International Congress on Advances in Civil Engineering, 15-17 September 2008 Eastern Mediterranean University, Famagusta, North Cyprus Development of 3D Cadastre System to Monitor Land Value and

### What is GIS? Geographic Information Systems. Introduction to ArcGIS. GIS Maps Contain Layers. What Can You Do With GIS? Layers Can Contain Features

What is GIS? Geographic Information Systems Introduction to ArcGIS A database system in which the organizing principle is explicitly SPATIAL For CPSC 178 Visualization: Data, Pixels, and Ideas. What Can

### Page 1 of 7 (document version 1)

Lecture 2 - Data exploration This lecture will cover: Attribute queries Spatial queries Basic spatial analyses: Buffering Voronoi tessellation Cost paths / surfaces Viewsheds Hydrological modelling Autocorrelation

### Spatial Data Analysis

14 Spatial Data Analysis OVERVIEW This chapter is the first in a set of three dealing with geographic analysis and modeling methods. The chapter begins with a review of the relevant terms, and an outlines

### HYBRID MODELLING AND ANALYSIS OF UNCERTAIN DATA

HYBRID MODELLING AND ANALYSIS OF UNCERTAIN DATA Michael GLEMSER, Ulrike KLEIN Institute for Photogrammetry (ifp), Stuttgart University, Germany Geschwister-Scholl-Strasse 24D, D-7074 Stuttgart Michael.Glemser@ifp.uni-stuttgart.de

### Understanding Raster Data

Introduction The following document is intended to provide a basic understanding of raster data. Raster data layers (commonly referred to as grids) are the essential data layers used in all tools developed

### GIS Data in ArcGIS. Pay Attention to Data!!!

GIS Data in ArcGIS Pay Attention to Data!!! 1 GIS Data Models Vector Points, lines, polygons, multi-part, multi-patch Composite & secondary features Regions, dynamic segmentation (routes) Raster Grids,

### Introduction to PostGIS

Tutorial ID: IGET_WEBGIS_002 This tutorial has been developed by BVIEER as part of the IGET web portal intended to provide easy access to geospatial education. This tutorial is released under the Creative

### Making features smart

Making features smart GEOG 419/519: Advanced GIS Dr. Lembo Qualities of features (p. 76) Features have shapes Geometry feature class Points and multipoints Polylines (line segments that may or may not

### Catalogue or Register? A Comparison of Standards for Managing Geospatial Metadata

Catalogue or Register? A Comparison of Standards for Managing Geospatial Metadata Gerhard JOOS and Lydia GIETLER Abstract Publication of information items of any kind for discovery purposes is getting

### Building Geospatial Ontologies from Geographic Database Schemas in Peer Data Management Systems

Building Geospatial Ontologies from Geographic Database Schemas in Peer Data Management Systems Danúbia Lima 1, Antonio Mendonça 1, Ana Carolina Salgado 2, Damires Souza 1 1 Federal Institute of Education,

### PROGRESSIVE TRANSMISSION AND VISUALIZATION OF VECTOR DATA OVER WEB INTRODUCTION

PROGRESSIVE TRANSMISSION AND VISUALIZATION OF VECTOR DATA OVER WEB Tinghua Ai and Jingzhong Li School of Resource and Environment Sciences Wuhan University, China ABSTRACT The progressive transmission

### GIS 101 - Introduction to Geographic Information Systems Last Revision or Approval Date - 9/8/2011

Page 1 of 10 GIS 101 - Introduction to Geographic Information Systems Last Revision or Approval Date - 9/8/2011 College of the Canyons SECTION A 1. Division: Mathematics and Science 2. Department: Earth,

### GIS Analysis Concepts

GIS Analysis Concepts Copyright C. Schweik 2011 (Some material adapted from Heywood et al 1998; Theobald, 1999 ) 1 What kinds of analysis can we do with GIS? 1. Measurements 2. Layer statistics 3. Queries

### GEOSTAT13 Spatial Data Analysis Tutorial: Raster Operations in R Tuesday AM

GEOSTAT13 Spatial Data Analysis Tutorial: Raster Operations in R Tuesday AM In this tutorial we shall experiment with the R package 'raster'. We will learn how to read and plot raster objects, learn how

### Data model specifying system objects and the relationships amongst them (ER diagram)

Data model specifying system objects and the relationships amongst them (ER diagram) Document for activity 5.3 WP 5 TESAF with the support of IGR and UM 1 Contents 1 Introduction... 3 1.1 Spatial database

### Assessment Tasks Pass theory exams at > 70%. Meet, or exceed, outcome criteria for projects and assignments.

CENTRAL OREGON COMMUNITY COLLEGE: GEOGRAPHIC INFORMATION SYSTEM PROGRAM 1 CENTRAL OREGON COMMUNITY COLLEGE Associate Degree Geographic Information Systems Program Outcome Guide (POG) Program Outcome Guide

### Survey On: Nearest Neighbour Search With Keywords In Spatial Databases

Survey On: Nearest Neighbour Search With Keywords In Spatial Databases SayaliBorse 1, Prof. P. M. Chawan 2, Prof. VishwanathChikaraddi 3, Prof. Manish Jansari 4 P.G. Student, Dept. of Computer Engineering&

### GEO-OMT. An Object-Oriented Method Supporting the Development of Facilities Management Systems. Graça ABRANTES and Mário R. GOMES

GEO-OMT An Object-Oriented Method Supporting the Development of Facilities Management Systems Graça ABRANTES and Mário R. GOMES This paper presents the support that Geo-OMT, an extension of the Object

### Software Verification: Infinite-State Model Checking and Static Program

Software Verification: Infinite-State Model Checking and Static Program Analysis Dagstuhl Seminar 06081 February 19 24, 2006 Parosh Abdulla 1, Ahmed Bouajjani 2, and Markus Müller-Olm 3 1 Uppsala Universitet,