1 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 Moreau. GDMS-R: A mixed SQL to manage raster and vector data. GIS 2009, Jan 2009, Ostrava, Czech Republic. <hal > HAL Id: hal Submitted on 4 Feb 2009 HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
2 GIS Ostrava , Ostrava GDMS-R: A mixed SQL to manage raster and vector data Thomas, LEDUC1, Erwan, Bocher2, Fernando, González Cortés3, Guillaume, Moreau4 1IRSTV, CERMA Laboratory, ENSA Nantes, rue Massenet, BP , Nantes, cedex 3, France 2IRSTV, Ecole Centrale de Nantes, rue de la Noë Nantes, cedex 3, France 3IRSTV, CERMA Laboratory, ENSA Nantes, rue Massenet, BP , Nantes, cedex 3, France 4IRSTV, CERMA Laboratory, Ecole Centrale de Nantes, rue de la Noë Nantes, cedex 3, France Abstract. To evaluate urbanization impact on territories, an accurate knowledge of the urban and peri-urban fabrics is unavoidable. To provide advanced characterization of the terrain, modern GIS applications target even wider geographic areas at finer resolutions but they also have to mix data of different types such as Digital Elevation Model (raster layer), buildings (polygonal layer) and roads (polylines layer). Processing both raster and vector data with the same semantic and in an efficient way presents significant challenges to GIS insofar as underlying granularities but also data layout and processing patterns might be absolutely different. We have already focused on the definition and the implementation of an abstraction layer called GDMS (Generic Datasource Management System) to handle and process vector data. Main objectives with GDMS, were to provide the user not only a simple and powerful API but also a spatial SQL derived language. Moreover, as an intermediate layer between the user and the information source, GDMS intends to reduce the coupling between the processes and the specificities of each underlying format. As a consequence, former work may easily be reused in a much larger set of scenarii. The learning curve is consequently even simpler. In this paper, we propose a raster extension to the GDMS layer called GDMS-R. Even if, there is currently no OGC standard concerning raster processing (using well-known SQL language), there already exists a de facto standard called Map Algebra defined by C. D. Tomlin in 1990 and commonly implemented in a wide set of GIS. Our objective is a bit different insofar as we propose to extend SQL language. We present the integration of Map Algebra concepts in GDMS through the GRAP (GeoRAster Processing) language. As for GDMS, reuse is enhanced by the possibility of being vendor-independent (middle-ware approach) and the extension capabilities of the underlying SQL language. To demonstrate the capabilities of GDMS-R, we present a use case relative to the deep impact of increased urbanization on the vulnerability of peri-urban hydro-systems: impact of the linear constraints on the runoff water pathways and accumulation that uses both vector and raster data in an unified way. Keywords: processing language, spatial SQL, raster, GDMS 1 Introduction As mentioned in , the first major component of [a geographic] information system is its data, the second component is a set of data-processing capabilities. Data that need to be processed in a GIS depend on the nature of the spatial phenomenon involved. In a vector or drawing-based GIS, atomic data corresponds to vertices, (chains of) line segments, polygons whereas in a raster or imagebased GIS, the atomic data (or location) corresponds to a pixel or grid cell (i.e. a regularly spaced planimetric position).
3 All data relative to a particular geographic area of interest are stored in a single map called a layer. It corresponds to a bounded plane surface, where all atomic data share the same scale, orientation and planimetric projection. Vector layers most often represent human-drawn zones and are easily scalable, they are compatible with topological operations. They usually represent in a compact manner borders of homogeneous zones whereas raster data is most often the result of an automated acquisition process where the mesh is linked to the nature of the sensor rather than the phenomenon that is analyzed. Their accuracy is limited to mesh resolution and data storing might be an issue. They have the advantage of being able to compare heterogeneous data with very simple operations. To process those layers (the digital cartographic variable), the need for a spatial query formalism has been clearly identified and thoroughly studied over the last years. As noticed by , many studies focusing on the management of vector data in [Spatial DataBase] have progressed rapidly in the past. In this paper, we propose GDMS-R, a raster extension to the GDMS abstraction layer . Even if, there is currently no OGC standard concerning raster processing (using well known SQL language), there already exists a de facto standard called Map Algebra defined by  and commonly implemented in a wide set of GIS. Our objective is a bit different insofar as we propose to extend SQL language. We present the integration of Map Algebra concepts in GDMS through the GRAP (GeoRAster Processing) language. As for GDMS, reuse is enhanced by the possibility of being vendor-independent (middle-ware approach) and the extension capabilities of the underlying SQL language. The rest of the paper is organized as follow; first, we will present the motivation for our architecture (need for a common language) and most common proposals, then we will introduce Map Algebra and the GDMS concepts. Afterwards, we show how they have been combined into GDMS-R. We will finish by a use case that demonstrates the potentialities of our approach. 2 Related work 2.1 Spatial query formalism Numbers of approaches have been proposed to address the need for formalizing spatial queries. Some of them have implemented spatial extension to the well-known Structured Query Language (SQL), assuming that it was much more powerful and easy to retrieve and analyze spatial data using the familiar select - from - where statement instead of procedural commands like macro language . Indeed, the SQL is a world famous declarative programming language dedicated to CRUD (CRUD stands for the ability to Create new entry, as well as Read, Update and Delete existing entry in a data set) operations in the context of Relational DataBases Management System (R-DBMS). In their reviews of proposed query languages, among many other spatial data manipulation languages, [6, 7] successively mention: , its graphic query language (GQL) structured in SQL fashion is designed to conform with ISO standard 7942 (graphical kernel system) and ISO standard 9075 (database language SQL) ; , its Geoql is an extension of SQL which provides spatial operators, , its Spatial SQL is defined as a query and presentation language designed to preserve SQL concepts but also to incorporate spatial operations and relationships. The processing of vector-encoded data through a spatially enabled and extended SQL language has thus already been normalized by the Open Geospatial Consortium in the Open-GIS Simple Features Specification for SQL [9, 10]. There also exist several efficient and industrial implementations that spatially enable R-DBMS (such as the PostGIS, a spatial extension for PostgreSQL). 2.2 Motivations Even if, as noticed by , the amount of raster data has exploded recently as a result of the quick development of the remote sensing technique, no real framework to handle raster data type in a
4 GIS Ostrava , Ostrava spatial SQL way has been defined. Indeed, there is no OGC standard relative to raster processing. Nevertheless, there already exists a de facto standard called Map Algebra. As written in  the term Map Algebra was first introduced in the late 1970s  and has since been used in loose reference to a set of conventions, capabilities, and analytical techniques that have been widely adopted for rasterbased GIS [20, 21]. The purpose of this algebra-like language is to create new map layers using existing map layers (treated as input variables) and sequenced dedicated operations. It takes a location-oriented perspective or worm s eye view by computing new values for locations based on the location itself, its neighborhood, a related zone, or the entire dataset.  makes an inventory of some recent extensions to Map Algebra. Thus, among some others, it mentions successively: the GeoAlgebra presented in , an extension of map algebra that allows for flexible definitions of neighborhoods, the MapScript language presented in  that allows control structures and dynamical models to be incorporated into map algebra,  propose an extension of map algebra for spatio-temporal data handling. Map algebra has been incorporated in many GIS and remote sensing image processing softwares (such as ArcGIS Spatial Analyst developed by ESRI Inc. or GRASS raster analysis modules ). For  One of the reasons for the widespread adoption of conventional map algebra for raster processing in GIS is its simple syntax and the ability to string together multiple functions to create more complex models. In the DBMS context, there already exists efficient raster data support in Oracle 10g (with the Oracle GeoRaster object) and some prototype in PostgreSQL/PostGIS (with the PGRaster object) With its GSQL-R,  demonstrates that an extended SQL provides a convenient way for raster data access and manipulation. 3 Map Algebra and GDMS concepts 3.1 Overall view of GDMS GDMS  is related with a similar layer used in the gvsig project to manage the alphanumeric data access. This layer is called GDBMS  and one of its main limitation is that it can only be used for alphanumeric purposes. GDMS work is a general refactoring that mainly adds spatial functionalities and a more powerful SQL processor to GDBMS. As noticed in , the heterogeneity of source types makes difficult the reuse of algorithms that are tightly coupled with the specified file format, database vendor, etc. With an intermediate layer between the user and the information source, the work developed by the former will not be coupled with the specificities of each format but with the intermediate layer itself, letting the work to be reused in a much more wide set of scenarios and of course simplify the learning curve for new developers. One of the problems that such a layer arises is that it has to be able to access any potential source type. As the current number of source types is huge and can grow indefinitely, the user has to be able to extend the access capabilities of the layer. By introducing the concept of driver, GDMS intends to potentially fit any situation, even if the types of sources are not well known or currently the layer does not have the driver to access it. With GDMS, the idea was to postpone all problems of interoperability across data repositories, but also about the SQL semantics by developing a highly flexible, portable and standards compliant tool to build SQL queries. Currently, GDMS is dedicated for GIS clients but in the future it will also be a software component for SDI to process spatial data according to Web Processing Service. GDMS provides an API that lets the user operate independently of source type. However, this API is
5 not friendly enough for an end-user and reduces the acceptance of the solution by final users. With the purpose of simplifying both access and manipulation of data sources, GDMS provides a SQL processor that lets the execution of the common Data Manipulation Language statements against any source mapped by a driver. To avoid introducing a new grammar, GDMS fully preserves the SQL-92 grammar and adds to this standard geometric concepts and spatial functions as in OGC simple features SQL specification. As an analogy to spatial SQL for RDBMS, GDMS provides an extended SQL query language on heterogeneous data types. It is the main purpose of GDMS to improve data creation and sharing. As in an SDI the consumption of data is as important as the production and sharing of data, the SQL processor in GDMS allows data feedback as if they were new data sources (materialized views). This means that the result of SQL queries can easily be integrated into the SDI as a new data source. Those data will be ready to be used by further SQL statements as any other existing data source. Finally, to improve further code reuse, GDMS introduces the concept of Geo-knowledge repository. GDMS allows an extension to the semantics of the SQL language in terms of functions and custom queries. Functions and custom queries are artifacts that contain the implementation of some operations on the data and can be reused just by referencing its name into an SQL statement. This way, some user can implement a buffer operation and other can reuse it just by calling buffer in an SQL query: SELECT buffer(the_geom, 20) FROM mydata. The collection of all this artifacts is the Geo-knowledge repository. The purpose of GDMS is to maintain such a repository and encourage the feedback of new artifacts from the user to make it a growing knowledge base. The Geo-knowledge repository is more than a library of functions that can be extended by new userdefined add-ons; we aim to go a step further with this concept. Indeed, the goal is to provide a high level of spatial semantics: checking: a just in time validation process of input data type, range made by a dedicated spatial SQL pre-processor, sharing: future releases will embed network-group-ware capacity because users need also to share spatial process and not only geographic data, interoperability: the GDMS core is fully OGC compliant, so it is possible to copy and paste spatial SQL scripts from/into PostgreSQL/PostGIS. At last, our Geo-knowledge repository provides not only spatial functionalities but also description and (sometimes) useful use-cases for each of them. 3.2 Coupling GDMS with Map Algebra GDMS features are inspired by the relational model theory for database management and its tuple calculus realization. So, in GDMS, a DataSource is an array of rows, each of them may contains several columns (i.e. fields of any Value type: from a JTS Geometry  to an alphanumeric attribute value). In term of granularity, classical GDMS is able to deal with atomic objects such as JTS Point, LineString, Polygon or integer values. In the pure imaging and computer vision domains, the atomic element is the pixel (which stands for picture element). But, for efficiency reason (images encoding through pixels array are suitable for wide areas such as the one used by remote sensing technologies or geo-computational simulations it would be a nonsense to explicitly deal with SQL tables of pixels fields), the single data type used by Tomlin s model is the map. The mixed SQL we will present does not explicitly process pixels, but layers or maps (that can be stored as arrays of pixels).  notices that Tomlin s Map Algebra 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
6 GIS Ostrava , Ostrava layer, as in example given a map of a city and a Digital Terrain Model, calculate the mean altitude for each city. In , Tomlin mentions a fourth major class of map algebraic operation called incremental functions. The incremental operations compute a new value for every location as a function of its lineal, areal, or surficial form on a specified layer. These operations may indicate each location s length or shape as part of a lineal network; its surface area, frontage, or shape as part of an areal pattern; or its slope, aspect, drainage direction(s), or volume as part of a surficial form. As an example, we present two operations using the spatial SQL syntax defined in GDMS-R. The first one is an instance of a focal function (a mean computation), while the second one belongs to the fourth major class (even if the incremental classification label does not really suit us): SELECT FocalMean(raster, rectangle, 3, 3) FROM mydem; SELECT CropRaster(raster, the_geom) FROM mydem, zoneofinterest; A mapping exists between GDMS-R and Map Algebra so all operations can be executed in a similar manner. For example, to know the maximum slope by parcels, the following query is used: SELECT slope(raster) FROM mydem into slope; SELECT zonalmax(parcels.raster, slope.raster) FROM parcels, slope; 4 Implementation details 4.1 GDMS architecture and data model GDMS has a layered architecture (fig. 1) where the upper layer contains the SQL interface and the Geo-knowledge repository and the lower one is the driver layer. It contains details that are specific to each the source types. In case of the ESRI Shapefile driver, it contains implementation details to open files, decode the information and transform the contents into the GDMS internal model. PostgreSQL drivers will open the connection and will transform the information of the table into the internal model. This layer also provides extensibility to GDMS in terms of data source supported types. It is possible to create a driver to support potentially any type of data source. Fig. 1. GDMS layered architecture In spite of its extensibility, the driver layer is absolutely heterogeneous and building a SQL processor on top of such a layer would be very complicated. To solve this problem we place the Datasource layer on top of the drivers one. The main responsibility of this layer is the creation of a common API to manage all source types. The SQL processor is built on top of this Datasource layer so it uses the common interface it provides to access any of the available data source types. Consequently it is possible to mix different types of data sources in the same SQL statement. For example it is possible to join a shapefile with a CSV file and a PostgreSQL table in one single SQL query. To reference the sources in a SQL statement it is
7 necessary to map a name to each involved data source before. We have seen that the Datasource layer deals with the heterogeneity of the drivers and provides a common interface to the upper layers. We will now focus on the data model that is used to implement all features. It consists of one main entity called DataSource. A DataSource is an abstraction that allows the management of one unique data source. This means that it is necessary to have as many DataSource instances as the number of sources to be accessed. This abstraction has a typical tabular structure that uses rows to store each of the elements of the data source. This structure is close to the JDBC standard, where each row consists of a set of fields with a common structure for all rows in the DataSource. It is possible to see a relationship between this model and the GeoAPI model (currently being standardized in ISO 19123) where FeatureCollection and Feature respectively match DataSource and its rows. Finally the field values of each row match the attribute values of a Feature. All the information about field types is stored into a Metadata object that contains all field names, field types and restrictions for the types, such as length for string values, and coordinate reference system for spatial types. The model in this point allows a wide range of types for the fields that are compliant with the JDBC standard for alphanumeric types or with the OpenGIS Simple Features Specification for spatial types. Also note that there is no restriction in field types for a DataSource, it can have zero or more fields of any type so it is as possible to have several spatial fields as to have just alphanumeric. 4.2 GDMS-R: a data model fully integrated into GDMS GRAP (GeoRAster Processing) provides the core functionality required to support the use and the process of images. It is based on the ImageJ [17, 1] library. ImageJ is an image processing and analysis program (but it is also an API) that support a huge amount of image formats, written in Java, by Wayne Rasband at the U.S. National Institutes of Health. In our own GRAP library, a raster is wrapped in a common object called a GeoRaster. A GRAP GeoRaster object is an association of an array of pixels stored in an ImageJ Image-Plus object and a set of some spatial fields (mandatory to locate the raster in the earth!) such as: a projection system, an envelop, a pixel size GRAP s metadata matches the world file specifications. Indeed, the first pixel of the pixels grid (with row and column indexes both equals to zero, at the upper left corner) corresponds, in the real world, to the centroid of the corresponding UpperLeft rectangle. In GDMS presentation relative to the internal data model, we have written that the DataSource was the abstract entry point for each unique data source (of geometry type). This assertion remains true for data source of raster type. Indeed, DataSource abstraction may also be convenient to store and to handle tabular set of multiple GeoRaster columns. The same way we did for GeometryValue (it is the type of any geometric field, based on the JTS Geometry object class), we have implemented a RasterValue (it is the type of any GeoRaster field, see fig. 2). Fig. 2. GDMS-R extended data model The main advantage of this approach, is that with this kind of GRAP and GDMS coupling, we are able to handle in a single DataSource quite heterogeneous and complex data set. Indeed a DataSource is able to store multiple rows of multiple columns of any Value type. Due to the object-oriented programming paradigm we use, and because both Raster-Value and GeometryValue are subclasses
8 GIS Ostrava , Ostrava of Value (that is to say more specialized versions of Value ), we are able to store in the same row several geometric and raster fields! In the same way, it is also possible to store in a single GDMS DataSource as many rows of raster than there are small elements of image maps covering a larger area in tiling placement technique! 4.3 GDMS-R extensibility through raster functions Even if ImageJ is a quite comprehensive library to process raster data, GRAP API offers the possibility to implement new process on GeoRaster. This extension mechanism has to be made through GRAP Operation (which is a Java interface dedicated to the implementation of GeoRaster processing). Since new GRAP Operation has been implemented, it is possible to benefit from GDMS extension mechanisms. Indeed, GDMS might be customizable trough queries that are able to deal with DataSource of any kind (as input or output variables). The only requirement is to fulfill some preprogrammed prerequisites concerning both input and output DataSources. Indeed, polymorphism is not always available for each GDMS SQL query. 5 Example case study In this section, the main objective is to present a proof of concept concerning the relevance of this mixed SQL in advanced spatial hydrological analysis. What will be presented, will be scripted using the GDMS-R spatial SQL language independently from the input layer type. Here is a general survey of the main steps of the processing suite: select a Region Of Interest in the DEM and crop it, compute the D8 flow direction grid (using the D8 method defined by ), compute the flow accumulation grid, compute the flow accumulation grid taking linear constraints into account, compute the watersheds and convert them to vector. The need of a mixed SQL is obvious here because we want to take linear constraints into account to increase the accuracy of the simulations (in both flow routing and flow accumulation algorithms). Two input data are used: a DEM (Digital Elevation Model) stored in an ESRI ASCII grid format and a roads ESRI Shapefile that contains a set of polylines (see fig. 3). Fig. 3. Input data (DEM, roads in red) and area of interest (orange fence) At first, we decide to limit the analysis zone to a smaller area of interest (also represented in fig. 3). This operation is expressed as: SELECT CropRaster(raster, the_geom) AS raster FROM dem, fence; where fence is the limited area. By calling the create table statement, we can store the Crop-Raster result into a GDMS-R internal format. CREATE TABLE smalldem AS SELECT CropRaster(raster, the_geom) AS raster FROM dem, fence;
9 The following examples show a sequence of spatial hydrological analysis to compare the impact of roads on the runoff paths. We use algorithms: D8 grid direction and D8 accumulation . This first one computes for every cell the maximum downslope (see fig. 4) and sets the cell with an integer encoded value (such as 1 for East, 2 for North-East ). The second computes the contributing area for each grid cell according to the grid direction. For a cell, it represents its own contribution added to the contribution of upslope neighbors than drain into it. It is used to evaluate the potential water flow accumulation. D8 algorithms are applied this way: CREATE TABLE direction AS SELECT D8Direction(raster) AS raster FROM smalldem; CREATE TABLE accumulation AS SELECT D8Accumulation(raster) AS raster FROM direction; Fig. 4. Original DEM and D8 grid direction result To take into account the impact of roads, we have modified the grid accumulation algorithm. A new function has been created; it requires both a grid direction and a constrained grid. The latter represents the roads as a set of connected pixels with values greater than or equal to 1. For this, the road file is transformed into a raster using the RasterizeLine function (see fig. 5). Finally, the output raster datasource is included to the D8ContrainedAccumulation call. Fig. 5. Roads rasterization process: from a roads as shapefile (left) to a roads as grid (right) SELECT RasterizeLine(the_geom, raster, 1) AS the_geom FROM roads, smalldem; SELECT D8ConstrainedAccumulation(d.raster,c.raster) AS raster FROM direction AS d,rasterizedroads AS c; Fig. 6 presents a comparison between those two types of accumulation grid. As it can be seen, the blue water flow is stopped by the road. Fig. 6. D8 grid accumulation (not constrained and constrained)
10 GIS Ostrava , Ostrava We will now proceed with further GDMS-R capabilities in computing all the watersheds as well as their area and their maximum slopes (see fig. 7). Fig. 7. Watershed as raster data and as polygons CREATE TABLE watersheds AS SELECT D8Watersheds(rs) AS rs FROM direction; CREATE TABLE watershedspolygons AS SELECT RastertoPolygons(rs) FROM watersheds; SELECT Area(the_geom) AS the_geom, * FROM watershedspolygons; The last statement builds the maximum slope for each watershed. It applies the Tomlin zonal syntax and shows how to combine several functions. CREATE TABLE maxslope AS SELECT ZonalMax(w.raster,D8Slope(d.raster)) AS raster, * FROM watersheds AS w, dem AS d; where watersheds is an integer-valued raster that identifies the zone for each cell. DS8lope is a function that computes the maximum slope for each cell around a 3x3 neighborhood. 6 Conclusion and future works In this paper, we have presented GDMS-R a raster extension to the GDMS abstraction layer: it provides a convenient and efficient way to address and to process spatial data of both vector and raster types. This layer is thus able to produce either a raster, either a vector resulting DataSource layer, starting from any type of input DataSource (it depends of course on the corresponding spatial process). What is now obvious, is that this abstraction layer is a major performer in the domain of the spatial data geo-processing. Indeed, on the client-side, it has already been strongly coupled with a desktop GIS called OrbisGIS6. On the server-side, a prototype based on GDMS-R has also been developed. Future work requires addressing level of detail (i.e. sampling level) as far as raster data are concerned; whatever the resolution of the input data, it will be useful to be able to specify the adequate resolution for a geo-process. This will also lead to include further algorithms dedicated to raster-vector transform, thus being able to overcome differences in meshing methods, mesh size... Some work has already been started in that direction in order to study the sensitivity of mesh type, direction and size . The other main working direction deals with Spatial Data Infrastructure and we will look forward at encapsulating GDMS-R, on the server/dispatcher-side, in a Web Processing Service. This is one of the future main steps in our roadmap. 7 Acknowledgments The AVuPUR project was funded by the French Agence Nationale de la Recherche (ANR) under contract ANR-07-VULN-01.
11 8 Availability A GDMS-R implementation included in the OrbisGIS frontend and developed under GPL license, is available for public download at: References  M. Abramoff, P. Magelhaes, and S. Ram. Image Processing with ImageJ. In Biophotonics International, volume 11, pages 36 42,  A. Anguix and G. Carrin. gvsig: Open Source Solutions in spatial technologies. In GIS Planet, Estoril, Portugal, may-june  E. Bocher, T. Leduc, G. Moreau, and F. González Cortés. GDMS: an abstraction layer to enhance Spatial Data Infrastructures usability. In 11th AGILE International Conference on Geographic Information Science - AGILE 2008, Girona, Spain, may  G. Câmara, D. Palomo, R. Cartaxo Modesto de Souza, and O. R. Fradico de Oliveira. Towards a generalized map algebra: principles and data types. In Proceedings of the 7th Brazilian Symposium on GeoInformatics - GEOINFO 2005, Campos do Jordão, Brazil, November ISBN:  M. Davis and J. Aquino. JTS Topology Suite - Technical Specifications. Technical report, VIVID Solutions, October Technical Specs.pdf.  J. L. de Oliveira and C. B. Medeiros. User interface issues in geographic information systems. Technical Report IC-96-06, July  M. J. Egenhofer. Spatial SQL: A Query and Presentation Language. IEEE Transactions on Knowledge and Data Engineering, 6(1):86 95,  P.-C. Goh. A graphic query language for cartographic and land information systems. International Journal of Geographical Information Systems, 3(3): ,  J. R. Herring. OpenGIS Implementation Specification for Geographic information Simple feature access - Part 1: Common architecture, October  J. R. Herring. OpenGIS Implementation Specification for Geographic information Simple feature access - Part 2: SQL option, October  Y. Liu, Y. Wang, Y. Zhang, X. Lin, and S. Qin. GSQL-R: A query language supporting raster data. In EEE International Geoscience and Remote Sensing Symposium, IGARSS, volume 7, pages , september  N. Long, E. Bocher, T. Leduc, and G. Moreau. Sensitivity of spatial indicators for urban terrain characterization. In IEEE International Geoscience and Remote Sensing Symposium, IGARSS- 2008, Boston, Massachusetts, U.S.A., july  J. Mennis, R. Viger, and C. D. Tomlin. Cubic map algebra functions for spatio-temporal analysis. Cartography and Geographic Information Science, 32(1):17 32,  J. O Callaghan and D. Mark. The extraction of drainage networks from digital elevation data. Computer vision, graphics, and image processing, 28(3): ,  B. C. Ooi. Efficient Query Processing in Geographic Information Systems, volume 471 of Lecture Notes in Computer Science. Springer,  D. Pullar. Mapscript: A map algebra programming language incorporating neighborhood analysis. Geoinformatica, 5(2): ,  W. S. Rasband. ImageJ U. S. National Institutes of Health, Bethesda, Maryland, USA.  M. Takeyama and H. Couclelis. Map dynamics: integrating cellular automata and GIS through Geo-Algebra. International Journal of Geographical Information Systems, 11:73 91,  D. G. Tarboton. A new method for the determination of flow directions and contributing areas in grid digital elevation models. Water Resources Research, 33(2): ,  C. D. Tomlin. Geographic Information Systems and Cartographic Modeling. Prentice Hall,  C. D. Tomlin. Cartographic modeling. In D. R. M. Goodchild, D. Maguire, editor, Geographical information systems: principles and applications, pages Harlow, Essex, United Kingdom: Longman Group Ltd, Harlow, Essex, United Kingdom,  C. D. Tomlin. Map algebra: one perspective. Landscape and Urban Planning, Elsevier Science, 30:3 12, 1994.
12 GIS Ostrava , Ostrava  C. D. Tomlin and J. K. Berry. A mathematical structure for cartographic modeling in environmental analysis. In American Congress on Surveying Mapping, 39th annual symposium, pages , Falls Church, Virginia, U.S.A., 1979.
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
OGRS 2009 Lab «Practical introduction to OrbisGIS 2.1» 9th, July 2009 Ecole Centrale of Nantes Authors Petit Gwendall (IRSTV - FR CNRS 2488 - Ecole Centrale de Nantes, France) Lepetit Arnaud (Laboratoire
INTRODUCTION TO ARCGIS SOFTWARE I. History of Software Development a. Developer ESRI - Environmental Systems Research Institute, Inc., in 1969 as a privately held consulting firm that specialized in landuse
A Geographic Information System (GIS) integrates hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information. GIS allows us to view,
SQL SUPPORTED SPATIAL ANALYSIS FOR WEB-GIS Jun Wang Jie Shan Geomatics Engineering School of Civil Engineering Purdue University 550 Stadium Mall Drive, West Lafayette, IN 47907 ABSTRACT Spatial analysis
An Automatic Reversible Transformation from Composite to Visitor in Java Akram To cite this version: Akram. An Automatic Reversible Transformation from Composite to Visitor in Java. CIEL 2012, P. Collet,
gvsig: A GIS desktop solution for an open SDI. ALVARO ANGUIX1, LAURA DÍAZ1, MARIO CARRERA2 1 IVER Tecnologías de la Información. Valencia, Spain. Tel. +34 96 316 34 00; Fax. +34 96 316 34 00 Email: email@example.com
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
Expanding Renewable Energy by Implementing Demand Response Stéphanie Bouckaert, Vincent Mazauric, Nadia Maïzi To cite this version: Stéphanie Bouckaert, Vincent Mazauric, Nadia Maïzi. Expanding Renewable
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
A Web services solution for Work Management Operations Venu Kanaparthy Dr. Charles O Hara, Ph. D Abstract The GeoResources Institute at Mississippi State University is leveraging Spatial Technologies and
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?
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.
F.J. Zarazaga-Soria, J. Lacasta, J. Nogueras-Iso, M. Pilar Torres, P.R. Muro-Medrano17 A Java Tool for Creating ISO/FGDC Geographic Metadata F. Javier Zarazaga-Soria, Javier Lacasta, Javier Nogueras-Iso,
A Method Using ArcMap to Create a Hydrologically conditioned Digital Elevation Model High resolution topography derived from LiDAR data is becoming more readily available. This new data source of topography
Sensors 2009, 9, 2320-2333; doi:10.3390/s90402320 OPEN ACCESS sensors ISSN 1424-8220 www.mdpi.com/journal/sensors Article An Integrated Photogrammetric and Spatial Database Management System for Producing
An Introduction to Open Source Geospatial Tools by Tyler Mitchell, author of Web Mapping Illustrated GRSS would like to thank Mr. Mitchell for this tutorial. Geospatial technologies come in many forms,
VR4D: An Immersive and Collaborative Experience to Improve the Interior Design Process Amine Chellali, Frederic Jourdan, Cédric Dumas To cite this version: Amine Chellali, Frederic Jourdan, Cédric Dumas.
Contribution of Multiresolution Description for Archive Document Structure Recognition Aurélie Lemaitre, Jean Camillerapp, Bertrand Coüasnon To cite this version: Aurélie Lemaitre, Jean Camillerapp, Bertrand
Assessment of Workforce Demands to Shape GIS&T Education Gudrun Wallentin, Barbara Hofer, Christoph Traun firstname.lastname@example.org University of Salzburg, Dept. of Geoinformatics Z_GIS, Austria www.gi-n2k.eu
A model driven approach for bridging ILOG Rule Language and RIF Valerio Cosentino, Marcos Didonet del Fabro, Adil El Ghali To cite this version: Valerio Cosentino, Marcos Didonet del Fabro, Adil El Ghali.
Finding Data There are various ways to find data using the Hennepin County GIS Open Data site: Type in a subject or keyword in the search bar at the top of the page and press the Enter key or click the
Mobility management and vertical handover decision making in heterogeneous wireless networks Mariem Zekri To cite this version: Mariem Zekri. Mobility management and vertical handover decision making in
3D Model of the City Using LiDAR and Visualization of Flood in Three-Dimension R.Queen Suraajini, Department of Civil Engineering, College of Engineering Guindy, Anna University, India, email@example.com
INTEROPERABLE IMAGE DATA ACCESS THROUGH ARCGIS SERVER Qian Liu Environmental Systems Research Institute 380 New York Street Redlands, CA92373, U.S.A - firstname.lastname@example.org KEY WORDS: OGC, Standard, Interoperability,
Providing GRASS with a Web Processing Service Interface Johannes Brauner Institute for Geoinformatics, University of Münster email@example.com Abstract. The process of bringing GIS functionality into
Global Identity Management of Virtual Machines Based on Remote Secure Elements Hassane Aissaoui, P. Urien, Guy Pujolle To cite this version: Hassane Aissaoui, P. Urien, Guy Pujolle. Global Identity Management
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
Geospatial Multistate Archive and Preservation Partnership (GeoMAPP) Archival Challenges Associated with the Esri Personal Geodatabase and File Geodatabase Formats December 6, 2011 Introduction Spatial
ESRI India: Corporate profile ESRI India A profile India s Premier GIS Company Strategic alliance between ESRI Inc. and NIIT Technologies Adjudged as India s Best GIS Solutions Company - Map India 2001
Institute of Natural Resources Departament of General Geology and Land use planning Work with a MAPS Lecturers: Berchuk V.Y. Gutareva N.Y. Contents: 1. Qgis; 2. General information; 3. Qgis desktop; 4.
IntroClassJava: A Benchmark of 297 Small and Buggy Java Programs Thomas Durieux, Martin Monperrus To cite this version: Thomas Durieux, Martin Monperrus. IntroClassJava: A Benchmark of 297 Small and Buggy
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
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
Research on the Data Conversion Specification for Chinese Agricultural Product Quantity Safety Kaimeng Sun To cite this version: Kaimeng Sun. Research on the Data Conversion Specification for Chinese Agricultural
Leveraging ambient applications interactions with their environment to improve services selection relevancy Gérald Rocher, Jean-Yves Tigli, Stéphane Lavirotte, Rahma Daikhi To cite this version: Gérald
Technical Memorandum No. 2 DATA SHARING AND SPATIAL QUERY Raghavan Srinivasan Spatial Sciences Laboratory Texas Agricultural Experiment Station, Texas A&M University Submitted to El Paso Water Utilities
Introduction to GIS http://libguides.mit.edu/gis 1 Overview What is GIS? Types of Data and Projections What can I do with GIS? Data Sources and Formats Software Data Management Tips 2 What is GIS? 3 Characteristics
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
ibalance-abf: a Smartphone-Based Audio-Biofeedback Balance System Céline Franco, Anthony Fleury, Pierre-Yves Guméry, Bruno Diot, Jacques Demongeot, Nicolas Vuillerme To cite this version: Céline Franco,
DATABASE MANAGEMENT Last day we looked at spatial data structures for both vector and raster data models. When working with large amounts of data, it is important to have good procedures for managing the
Faut-il des cyberarchivistes, et quel doit être leur profil professionnel? Jean-Daniel Zeller To cite this version: Jean-Daniel Zeller. Faut-il des cyberarchivistes, et quel doit être leur profil professionnel?.
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
Paper Reference No.: PN-253 GIS AS A DECISION SUPPORT FOR SUPPLY CHAIN MANAGEMENT Sanjay Kumar 1 and Suneeta Agrawal 2 1. M. Tech. (GIS & Remote Sensing); GIS Cell; MNNIT, Allahabad, India (E-mail: firstname.lastname@example.org)
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
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
Managing a Geographic Database From Mobile Devices Through OGC Web Services Nieves R. Brisaboa 1, Miguel R. Luaces 1, Jose R. Parama 1, and Jose R. Viqueira 2 1 Database Laboratory, University of A Coruña,
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
What's new in gvsig Desktop 2.0 What are the novelties? 2.0 1.12 Migrating and building... Some examples... Please pardon our appearance during construction Pie and bar chart legends Table in layout 1.12
USING ONTOLOGIES FOR GEOGRAPHIC INFORMATION INTEGRATION Frederico Torres Fonseca The Pennsylvania State University, USA Keywords: ontologies, GIS, geographic information integration, interoperability Contents
Invited Contribution to GeoInformatics. Deadline September 15, 2001. Integration of location based services for Field support in CRM systems By P. Álvarez, J.A. Bañares, P.R. Muro-Medrano and F.J. Zarazaga
Cartography and GIS CARTOGRAPHIC ASPECTS OF CREATION OF PLANS FOR BOTANICAL GARDEN AND CONSERVATORIES Dr. Zdena Dobesova Mr. Ales Vavra Mr. Rostislav Netek Palacký University, Olomouc, Czech Republic ABSTRACT
IBM Informix Reference Documentation on Why Informix Spatial for GIS Projects Page 1 of 10 Contents Compliant with OGC... 3 Addressing the SQL standards... 3 Native Spatial, Intuitive Data Types... 3 Powerful
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
TOWARDS AN AUTOMATED HEALING OF 3D URBAN MODELS J. Bogdahn a, V. Coors b a University of Strathclyde, Dept. of Electronic and Electrical Engineering, 16 Richmond Street, Glasgow G1 1XQ UK - email@example.com
A few elements in software development engineering education Vincent Ribaud, Philippe Saliou To cite this version: Vincent Ribaud, Philippe Saliou. A few elements in software development engineering education.
Oklahoma s Open Source Spatial Data Clearinghouse: OKMaps Presented by: Mike Sharp State Geographic Information Coordinator Oklahoma Office of Geographic Information MAGIC 2014 Symposium April 28-May1,
Territorial Intelligence and Innovation for the Socio-Ecological Transition Jean-Jacques Girardot, Evelyne Brunau To cite this version: Jean-Jacques Girardot, Evelyne Brunau. Territorial Intelligence and
SUMMER SCHOOL ON ADVANCES IN GIS Six Workshops Overview The workshop sequence at the UMD Center for Geospatial Information Science is designed to provide a comprehensive overview of current state-of-the-art
GIS for Educators Topic 5: Raster Data Objectives: Keywords: Understand what raster data is and how it can be used in a GIS. Raster, Pixel, Remote Sensing, Satellite, Image, Georeference Overview: In the
Study of GML-Based Geographical Data Visualization Strategy ZHANG LIN 1, CHEN SHI-BIN 2 1 College of Information Technology, ZheJiang University of Finance & Economics, HangZhou 310012, China 2 College
First Semester Development 1A On completion of this subject students will be able to apply basic programming and problem solving skills in a 3 rd generation object-oriented programming language (such as
FP-Hadoop: Efficient Execution of Parallel Jobs Over Skewed Data Miguel Liroz-Gistau, Reza Akbarinia, Patrick Valduriez To cite this version: Miguel Liroz-Gistau, Reza Akbarinia, Patrick Valduriez. FP-Hadoop:
Automatic Generation of Correlation Rules to Detect Complex Attack Scenarios Erwan Godefroy, Eric Totel, Michel Hurfin, Frédéric Majorczyk To cite this version: Erwan Godefroy, Eric Totel, Michel Hurfin,
Lecture 8 Online GIS Lecture 8: Outline I. Online GIS 1. Google Earth 2. MSN Live Maps II. Open Source GIS III. ArcGIS Server and the ESRI suite of online software utility options IV. Advanced Data Mining
An introduction to Geographic Information Systems and Careers in GIS Context: Why GIS? Many of the issues in our world have a critical spatial component: Land management Property lines, easements, right
Cloud application for water resources modeling Assist. Prof. Dr. Blagoj Delipetrev 1, Assist. Prof. Dr. Marjan Delipetrev 2 1 Faculty of Computer Science, University Goce Delcev Shtip, Republic of Macedonia
Development tools to create Web-GIS applications DbMAP ASJ the best solution to easily publish GIS data from existing spatial databases and distributed GIS data sources Easily designs, produces, and publishes
OGRS International Opensource Geospatial Research Symposium 2009 Ecole Centrale Nantes France July 08-09 -10, 2009 Call for papers OGRS 2009: International Opensource Geospatial Research Symposium July
TerraAmazon - The Amazon Deforestation Monitoring System - Karine Reis Ferreira GEOSS Users & Architecture Workshop XXIV: Water Security & Governance - Accra Ghana / October 2008 INPE National Institute
GeoKettle: A powerful open source spatial ETL tool FOSS4G 2010 Dr. Thierry Badard, CTO Spatialytics inc. Quebec, Canada firstname.lastname@example.org Barcelona, Spain Sept 9th, 2010 What is GeoKettle? It is
Advantages and disadvantages of e-learning at the technical university Olga Sheypak, Galina Artyushina, Anna Artyushina To cite this version: Olga Sheypak, Galina Artyushina, Anna Artyushina. Advantages
GEOG 482/582 : GIS Data Management Lesson 10: Enterprise GIS Data Management Strategies Overview Learning Objective Questions: 1. What are challenges for multi-user database environments? 2. What is Enterprise
DATA VISUALIZATION GABRIEL PARODI STUDY MATERIAL: PRINCIPLES OF GEOGRAPHIC INFORMATION SYSTEMS AN INTRODUCTORY TEXTBOOK CHAPTER 7 Contents GIS and maps The visualization process Visualization and strategies
Ontology-based Tailoring of Software Process Models Ricardo Eito-Brun To cite this version: Ricardo Eito-Brun. Ontology-based Tailoring of Software Process Models. Terminology and Knowledge Engineering
UPDATING OBJECT FOR GIS DATABASE INFORMATION USING HIGH RESOLUTION SATELLITE IMAGES: A CASE STUDY ZONGULDAK M. Alkan 1, *, D. Arca 1, Ç. Bayik 1, A.M. Marangoz 1 1 Zonguldak Karaelmas University, Engineering
Web and Mobile GIS Applications Development Presented by : Aamir Ali Manager Section Head (GIS Software Customization) Pakistan Space and Upper Atmosphere Research Commission (SUPARCO) Geographical Information
Cookbook 23 September 2013 GIS Analysis Part 1 - A GIS is NOT a Map! Overview 1. A GIS is NOT a Map! 2. How does a GIS handle its data? Data Formats! GARP 0344 (Fall 2013) Page 1 Dr. Carsten Braun 1) A
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
18 th World IMACS / MODSIM Congress, Cairns, Australia 13-17 July 2009 http://mssanz.org.au/modsim09 Introducing the Open Source CUAHSI Hydrologic Information System Desktop Application (HIS Desktop) Ames,
APPLICATIONS AND RESEARCH ON GIS FOR THE REAL ESTATE Chengda Lin, Lingkui Meng, Heping Pan School of Remote Sensing Information Engineering Wuhan University, 129 Luoyu Road, Wuhan 430079, China Tel: (86-27)-8740-4336
Web Map Service Architecture for Topographic Data in Finland Teemu Sipilä National Land Survey of Finland Abstract. Since 2012 National Land Survey of Finland has been renewing its web map services and
Analyse échographique de la cicatrisation tendineuse après réparation arthroscopique de la coiffe des rotateurs Martin Schramm To cite this version: Martin Schramm. Analyse échographique de la cicatrisation
Knowledge based tool for modeling dynamic systems in education Krassen Stefanov, Galina Dimitrova To cite this version: Krassen Stefanov, Galina Dimitrova. Knowledge based tool for modeling dynamic systems
B Data access and management CONTENTS B.1 Introduction... B-1 B.2 Data requirements and availability... B-1 B.3 Data access... B-2 B.4 Overall procedures... B-2 B.5 Data tools and management... B-4 Appendix