spatial databases and geographic information systems

Spatial Databases and

Geographic Information

Systems

Karl Neumann

Sarah Tauscher Institut für Informationssysteme

Technische Universität Braunschweig

http://www.ifis.cs.tu-bs.de

1.1 Motivation

1.2 Organizational Issues

1.3 Content and Overview

1.4 Literature

Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 2

1 Introduction

• Route

planning

1.1 Motivation

http://www.viamichelin.de/

• Sentiment

1.1 Motivation

[TN12]

• City

1.1 Motivation

visualization tool: [Me12]

• Search for point of interest

1.1 Motivation

• Tree register

1.1 Motivation

http://www.baumbuero.de/ http://falk-weihs.com/

• Railway noise prediction

1.1 Motivation h

• Ship tracking

– "Halunder Jet"

• Ship type:

passenger

• Built: 2003

• Size: 59×12 m

• July, 28th 2014

– Hamburg

Helgoland

– 12:49 h – 13:01 h

1.1 Motivation

http://www.vesselfinder.com/

• Land cover - Germany

1.1 Motivation

http://www.corine.dfd.dlr.de/

• Lecture

– 21.10.2016 – 10.02.2017

– 9:45 – 12:00

– Exercises and detours integrated into lecture

– URL:

www.ifis.cs.tu-bs.de/

teaching/ws-1617/gis

• Credits: 4 or 5

(depending on your

course of study)

• Exams

– Oral exam

– 13. –17. 02.2017

– Register at examination office

– Get an appointment

at the Ifis secretariat

→ Regine Dalkiran

• 2 Spatial Data Modelling

– 2.1 Introduction

– 2.2 Geometry

– 2.3 Conversion between Vector and Raster Models

– 2.4 Topology

– 2.5 Fields

– 2.6 AAA-Project

– 2.7 Operations

– 2.8 Summary

http://skagit.meas.ncsu.edu/~helena/gmslab/interp/F1a.gif

– Spatial object/Geoobject: element to model real world

data in geographic information system

– Are described by spatial data (geodata)

– Spatial information: custom-designed spatial data

– Chief difference to "conventional" objects

("What’s so special about spatial?"):

• Geometry

• Topology

http://speedymole.com/Tubes/Paris/

– Vector: minimum

distance between

a point and a line

– Raster: distance matrices for distance determination

city block metric chessboard metric Euclidean distance of centers

• 3 Mapping of Spatial Data

– 3.1 Properties of Maps

– 3.2 Signatures, Text, Color

– 3.3 Geometric Generalization

– 3.4 Label and Symbol

Placement

– 3.5 Summary

http://homepage.univie.ac.at/.../Janschitz_Text.pdf

– Challenges

• Projection of the 3D surface on two dimensions

(paper, film, screen)

• Selection of the spatial objects and

their attributes to be displayed

• Generalization of geometric and

thematic properties (simplify,

omit depending on scale)

• Exaggeration and displacement

(e.g. river valley with roads and

railway lines)

http://maps.google.de/

– Douglas/Peucker algorithm

1. given: polyline L, threshold g

2. determine line between the start and end point of L,

3. determine the point of L that is furthest from the line segment

4. if distance > g then the point is significant, repeat procedure for both sub-lines, otherwise remove all the points between the start and end point of L

• 4 Spatial Queries

– 4.1 Development of SQL

– 4.2 Core SQL Queries

– 4.3 Spatial Queries and Core SQL

– 4.4 Spatial Extensions of SQL

– 4.5 Evaluation of Spatial Queries

– 4.6 Summary

– Query 4’:

Areas of all buildings on the parcel with id "1222"

Building(id: String, typeOfUse: String, groundPlan: Polygon) Parcel(id, groundPlan: Polygon)

SELECT Area(b.groundPlan) FROM Building b, Parcel p WHERE p.id = ’1222’ AND Contains (p.groundPlan, b.groundPlan);

– Therefore evaluation of spatial queries in two steps

• Filter step

– Determination of possible hits by evaluation on spatial

approximation (lower costs)

• Refinement step

– Evaluation on accurate geometry only for objects of the filter step

• 5 Spatial Access Methods

– 5.1 Quadtree

– 5.2 R-tree

– 5.3 K-d tree

– 5.4 BSP tree

– 5.5 Grid file

– 5.6 Summary

– Problems

• Only suitable for points

• False positives, the solutions supplied by the index have to

be verified

• Adequacy depends on the position of the query window

– Heuristic

• Always insert a new object

into the node resulting in the

smallest increase in volume

• If it lies inside a MBR no

enlargement is necessary

• If there is more than one possibility,

choose the node

with the smallest

volume

• 6 Spatial Data and XML

– 6.1 Standards

– 6.2 XML

– 6.3 GML

– 6.4 SVG

– 6.5 Summary

– Example: a DTD of a city

<!ELEMENT city(name, inhabitants?, area)> <!ELEMENT name (#PCDATA)> <!ELEMENT inhabitants(#PCDATA)> <!ELEMENT area(polygon+)> <!ELEMENT polygon (point, point, point, point, point*)> <!ELEMENT point(x, y)> <!ELEMENT x (#PCDATA)> <!ELEMENT y (#PCDATA)>

– Path instruction for complex shapes, lines and areas

• Element’s attributes include the attribute "d" for specifying

the points of the path

• Attribute "d" contains further commands for the

interpretation of the points

• E.g. a point may be the begin or the end of a curve or of a

• 7 Application Programming

– 7.1 Connecting SQL with Programming Languages

– 7.2 PostgreSQL and JDBC

– 7.3 Oracle Spatial and JDBC

– 7.4 Processing of GML-

based Data

– 7.5 Summary

[KGB11]

– For processing geometric attributes special Java

classes and methods are offered

– They are subclasses of class "PGobject":

PGpoint, PGlseg, PGline

PGbox, PGpolygon, PGcircle

– Each subclass includes

• Constructor

• equals compares two objects

of the same type

• getValue returns the object as a string

• 8 Architecture of Geographic Information

Systems

– 8.1 GIS Hardware and

Software

– 8.2 Hybrid vs. Integrated

Systems

– 8.3 Clients and Servers

– 8.4 Web-based Spatial

Databases

– 8.5 Summary

[SX08]

– Architecture of GIS can be roughly classified

• File based

• Hybrid

• Layer-oriented

• Integrated

– PostGIS extends PostgreSQL by implementing missing

types and functions

– Is certified as a compliant "Simple Features for SQL"

database by the OGC

SELECT ST_AsText(ST_Intersection( ST_GeomFromText( 'MULTIPOLYGON(((-77 56,-52 18,-88 -27, -10 -13,-11 38,-77 56)))'), ST_GeomFromText( 'MULTIPOLYGON(((-49 63,-32 24,-39 -7,-66 -19, -72 -9,-74 31,-49 63)))')));

• 9 Remote Sensing

– 9.1 Physical Basics

– 9.2 Recording Techniques

– 9.3 Image Processing

– 9.4 Thematic Classification

– 9.5 Summary

http://saturn.unibe.ch/.../Fotogrammetrie-Bildflug.pdf

– System characteristics

• Recording techniques

– Radiometric resolution

– Geometric resolution

• Platform

– Kind of platform

– Altitude

– Orbit

– Period

• Mission

– Temporal coverage

– Spatial coverage

http://www.wdr.de/tv/quarks/ http://www.dlr.de/

http://www.chip.de/

http://www.maritime-technik.de/

– Example: digital

aerial orthophotos

of Braunschweig

• Central projection

• Planimetrically corrected

• 30. March 2014

https://www.braunschweig.de/

• 10 Location Based Services

– 10.1 Positioning

– 10.2 Car Navigation

– 10.3 Map Matching

– 10.4 Privacy

– 10.5 Summary

– Global Positioning System (GPS)

• Enables three dimensional positioning near the earth

• Measuring the runtime of signals between the satellite and

the GPS-receiver, from which the distance and the position

can be deduced (trilateration)

• The transmitted signal describes

a circular sphere centered at the

satellite on whose surface the

signal is received at the same

time → circular baseline of

equal receiving times

on earth

http://www.uni-giessen.de/ilr/frede/ lehrveranstaltungen/MP_51/2.6-GPS.pdf

– Sensor fusion

• Combining of

data so that

the resulting

information is

"better" than

when the

sources were

individually

• Complex task

[MMG12]

• 11 Environmental Information Systems

– 11.1 Environmental Data

– 11.2 Collection of Environmental Data

– 11.3 Monitoring Networks

– 11.4 Laboratory Data

– 11.5 Summary

http://www.wuz-paderborn.de/

– Example: TAO monitoring network

• "Tropical Atmosphere-Ocean Array"

• World's largest marine monitoring network

• Approx. 8000 miles near the equator through the Pacific,

from New Guinea to Panama

• Completed

in 1995, still

operating

http://tao.ndbc.noaa.gov/

• 12 Review and Outlook

– 12.1 Review

– 12.2 Outlook

[TN16]

• [AS14] Albertz, J.; Scholten, F.: Einführung in die Fernerkundung. 5. Aufl., Wissenschaftliche Buchgesellschaft, 2014.

• [Ba05] Bartelme, N.: Geoinformatik – Modelle, Strukturen, Funktionen. 4. Aufl., Springer, 2005.

• [BCKO10] Berg, M. de; Cheong, O.; Kreveld, M. van; Overmars, M.: Computational Geometry – Algorithms and Applications. 3. Ed. , Springer, 2010.

1.4 Literature – Books

Chap. 9

Chap. 2, 3

• [Be08]

Bernhardsen, T.: Geographic Information

Systems – An Introduction.

3. Ed., Wiley, 2008.

• [Be11]

Bertin, J.: Semiology of Graphics: Diagrams,

Networks, Maps. ESRI Press, 2011.

• [Bi16]

Bill, R.: Grundlagen der Geo-Informations-

systeme. 6. Aufl., Wichmann, 2016.

Chap. 4, 5

• [CW11]

Campbell, J.B.; Wynne, R.H.: Introduction to

Remote Sensing. 5. Ed., Guilford Publ., 2011.

• [EE04]

Eckstein, R.; Eckstein, S.: XML und

Datenmodellierung. dpunk, 2004.

• [Gü10]

Günther, O.: Environmental Information

Systems. Springer, 2010.

Chap. 9

• [He14]

Hennermann, K.: Kartographie und GIS –

Eine Einführung. 2. Aufl., Wissen-

schaftliche Buchgesellschaft, 2014.

• [HGM02] Hake, G.; Grünreich, D.; Meng, L.: Kartographie.

8. Aufl., de Gruyter, 2002.

• [KGB11]

Kothuri, R.; Godfrind, A.; Beinat, E.: Pro Oracle

Spatial. Apress, 2011.

Chap. 2.4, 3

Chap. 7.3

Chap. 2, 3, 4

• [KO09]

Kraak, M.-J.; Ormeling, F.: Cartography –

Visualization of Spatial Data. 3. Ed.,

Pearson Education Limited, 2009.

• [La13]

Lange, N. de: Geoinformatik in Theorie und

Praxis. 3. Aufl., Springer, 2013.

• [LKC15]

Lillesand, T.; Kiefer, R.; Chipman, J.: Remote

Sensing and Image Interpretation. 7. Ed.,

Wiley, 2015.

Chap. 5.1, 5.2

Chap. 9

• [Sa06] Samet, H.: Foundations of Multidimensional and Metric Data Structures. Elsevier Science & Technology, 2006.

• [SX08] Shekhar, S.; Xiong, H. (Eds.): Encyclopedia of GIS. Springer, 2008.

• [WD04] Worboys, M.; Duckham, M.: GIS: A Computing Perspective. 2. Ed., CRC Press, 2004.

Chap. 3, 4

Chap. 5

• [BO79] Bentley, J.L.; Ottmann, T.: Algorithms for Reporting and

Counting Geometric Intersections. IEEE Transactions on

Computers 28(9), 1979, pp. 643–647.

• [DP73]

Douglas, D.H.; Peucker, T.K.: Algorithms for the Reduction

of the Number of Points required to represent a

digitized Line or its Caricature. The Canadian

Cartographer 10(2), 1973, pp. 112–122.

1.4 Literature – Articles

• [Gu84]

Guttman, A.: R-Trees: A Dynamic Index Structure for

Spatial Searching. In Proc. "SIGMOD Conference", 1984,

pp. 47–57.

• [HBS11]

Huang, H.; Brenner, C.; Sester, M.: 3D Building Roof

Reconstruction from Point Clouds via Generative

Models. In Proc. "19th ACM SIGSPATIAL International

Symposium on Advances in Geographic Information

Systems, ACM-GIS", 2011, pp. 16–24.

• [MMG12]

Marti, E.D.; Martin, D.; Garcia, J.; de la Escalera, A.; Molina,

J.M.; Armingol, J.M.: Context-Aided Sensor Fusion for

Enhanced Urban Navigation. Sensors, 2012, 12,

pp. 16802–16837.

• [NGNSW07]

Neumann, K.; Grutza, M.; Nordmann, T.; Schlutow, F.; Wolf,

C.: Metaautomation der Liegenschaftskarte. Mitteilungen

des Bundesamtes für Kartographie und Geodäsie

(MdBKG), Band 39, 2007, pp. 15–29.

• [NHS84]

Nievergelt, J.; Hinterberger, H.; Sevcik, K.C.: The

Grid File: An Adaptable, Symmetric Multikey File

Structure. ACM Transactions on Database

Systsems, 9(1), 1984, pp. 38–71.

• [NKP08]

Neumann, K.; Kupfer, A.; Panse, F.: Generierung

von Gebäude-Präsentationsobjekten für NAS-

Bestandsdatenauszüge. MdBKG, Band 41, 2008,

pp. 83–92.

• [NPW06]

Neumann, K.; Petri, J.; Wolf, C.: Erzeugung karten-

ähnlicher Graphiken: XML-basierte Verdrängung

und Platzierung von Punktsignaturen. MdBKG,

Band 36, 2006, pp. 89–98.

• [NSe01]

Neumann, K.; Selke, M.: Elemente der Program-

miersprache Java vorgestellt an einer Modifikation

des Douglas/Peucker-Algorithmus zur Erhaltung

rechter Winkel. MdBKG, Band 20, 2001, pp. 87–97.

• [PP97]

Petzold, I.; Plümer, L.: Platzierung der Beschriftung in

dynamisch erzeugten Bildschirmkarten. Nachrichten aus dem

Karten- und Vermeesungswesen (NaKaVerm),

Reihe I, Nr. 117, 1997, pp. 95–113.

• [Se00]

Sester, M.: Automatische Generalisierung mittels

Ausgleichung. MdBKG, Band 17, 2000, pp. 105–113.

• [Se07]

Sester, M.: Generierung von kartographischen Präsentationen

im Maßstab 1:25.000 und 1:50.000 mit PUSH und TYPIFY.

MdBKG, Band 39, 2007, pp. 93–101.

• [SH95]

Schoppmeyer, J.; Heisser, M.: Behandlung von Geometrie-

typwechseln in GIS. NaKaVerm, Reihe I, Nr. 113,1995,

pp. 209–224.

• [St12]

Storandt, S.: Quick and Energy-Efficient Routes –

Computing Constrained Shortest Paths for Electric

Vehicles. In Proc. "ACM SIGSPATIAL Int. Workshop

on Computational Transportation Science", 2012,

pp. 20–25.

• [TN12]

Tauscher, S.; Neumann, K.: Combining Web Map Services and Opinion Mining to generate Sentiment Maps of Touristic Locations. In Proc. "Int. Symposium on Service Oriented Mapping", M. Jobst (Ed.), Wien 2012, pp. 277–286.

• [TN16]

Tauscher, S.; Neumann, K.: A Displacement Method for Maps Showing Dense Sets of Points of Interest. In "Progress in Cartography", Lecture Notes in Geoinformation and Cartography, Springer International Publishing, 2016, pp. 3–16.

• [Bu10] Budgenhagen, B.: Automatische Visualisierung von Quadtrees mit XFig. Studienarbeit, TU Braunschweig, 2010.

• [Bu11] Budgenhagen, B.: Visuelle Darstellung des Überganges von

Polygonen zu Linienzügen durch Rasterisierung und topologische Skelettierung. Diplomarbeit, TU Braunschweig, 2011.

• [Da12] Darda, R.: Visuelle Darstellung von Rasterisierung und

Blockcodierung vektorbasierter Polygone. Studienarbeit, TU Braunschweig, 2012.

1.4 Literature – Student Works

• [Hu14]

Huy, M.: Ein durch Kriging berechnetes Höhenmodell aus Wikipedia-Daten. Masterarbeit, TU Braunschweig, 2014.

• [Lu13]

Luthmann, L.: Erzeugung von speziellen zweidimen-sionalen Parkettierungen. Bachelorarbeit, TU Braunschweig, 2013.

• [Me11]

Menzel, M.: Übergang von Polygonen zu Linienzügen:

Implementierung des Petzold/Plümer-Verfahrens. Studienarbeit, TU Braunschweig, 2011.

• [Me12] Menzel, M.:Verknüpfung von Radar-Höhendaten und OSM-

Flächendaten zur Realisierung eines einfachen Stadtmodells am Beispiel von Braunschweig. Diplomarbeit, TU Braunschweig, 2012.

• [Ra10] Rath, C.: Visualisierung von unregelmäßig verteilten Messpunkten durch Delaunay-Triangulation mit XFig, CGAL-Methoden und Java. Studienarbeit, TU Braunschweig, 2010.

• [Sc11] Schön, A.: Erstellung aktueller Windkarten für Deutschland durch Web-Mining und Inverse-Distance-Weighting. Masterarbeit, TU Braunschweig, 2011.

• [Sp10] Spannknebel, J.: Visualisierung der Splitmöglichkeiten beim R-Baum. Studienarbeit, TU Braunschweig, 2010.

• [St11] Stachowiak, N.: Greedy- und Delaunay-Triangulation von Polygonen im XFig-Format. Studienarbeit, TU Braunschweig, 2011.

• [Zo13] Zorigoo-Graap, S.: Konstruktion und anspruchsvolle

Visualisierung von experimentellen Variogrammen mit XFig und Java. Studienarbeit, TU Braunschweig, 2013.

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