Iccsa 2008 Gotthard Meinel

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High Resolution Analysis of Settlement Structure on Base of Topographical Raster Maps - Method and Implementation Gotthard Meinel Leibniz Institute of Ecological and Regional Development, Dresden/Germany (IOER)

Transcript of Iccsa 2008 Gotthard Meinel

Page 1: Iccsa 2008 Gotthard Meinel

High Resolution Analysis of Settlement Structure on Base of

Topographical Raster Maps - Method and Implementation

Gotthard MeinelLeibniz Institute of Ecological and Regional Development,

Dresden/Germany (IOER)

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Overview

1. Background information2. Goals of development3. Data using4. Method description5. Implementation SEMENTA®6. Applications7. Outlook

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Background for the Development

Currently missing information about settlement structure on base of building levelCurrently missing public information about flats and inhabitants on base of building level (data protection!)Information needs for planning and decisions of many questionsTopographical geodata better and better and base for such calculationsHigh performance geoprocessing (hard- and software) for nation-wide calculations

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Goals of DevelopmentGeometric structuring of settlement areas by description of each building and each urban blocksClassification of each building and each urban blockCalculation of building- and block-oriented parameters (e.g. population, structure density, number of apartments)Visualization in GIS (e.g. ArcMap)Generation of statistical reportsSpatial disaggregation of low resolution statistical and census data

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Problem: Data Access in Germany

Data and privacy protection is very strictly!Statistic and census data only on municipality level or higherLast full census:

Old federal states: 1987!New federal states: 1995!

Next Census: 2011 (Results not before 2013!)Spatial base data partial not public for planners!Land use statistic not fine enough (only cadastre base, low resolution, no connection with other statistical data)

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Requirements on Database

Homogeneous dataAvailable for whole GermanyEstablished data update (defined update intervals)Data available also for historical time slotsFree or low budget data access for allData must visualize all buildings!

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Overview Spatial Base Data (Topographic Geodata) in Germany

Vector based (only blocks, non buildings)ATKIS DLM25 (1:25 000)ATKIS DLM250 (1:250 000)ATKIS DLM1000 (1:1 000 000)

Raster based (with buildings)DTK10 (1:10 000)DTK25 (1:25 000)DTK50 (1:50 000)DTK100 (1:100 000)

Optimum: Topographic geodata baseBuildings: Topographic Maps scale 1:25 000 (DTK25-V, DTK25, TK25)Blocks: ATKIS Basis-DLM

None using of air photographs (not GISready, to expensive, to large scale) or cadastre data ALK/ALKIS (to expensive, to large scale)!

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Process Overview

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Ground plan of DTK25-V Map

Problem: mix of buildings, streets and scripts

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Building extraction

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Building mask

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Ground plan DTK25-V Building extraction

Building indicators Vectorization and measurement

Building Extraction and Measurement

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Example Building parameter

Building perimeter Peri = 247 m

Area of enclosed rectangle

(area minimized)

AreaRect = 2714 m²

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www.ioer.deGotthard Meinel, Vortrag, Datum

Some selected Building ParametersVariable Building indicator Range of ValuesAREAH Building area 87…38042 m²

PERI Building perimeter 42…5408 mAREARECT Area of enclose rectangle 92…188714 m²

RATIOAREA Ratio AREAH/AREARECT 0,12…1,0

RECTRATIO Ratio length/width of enclose rectangle 1…12

RECTLENG Length enclose rectangle 10…843 m

RECTWID Width of enclose rectangle (=approximate building width) 6…258 m

PATIO Number of inside courts 0…6MAXWIDTH Maximal building width (=maximal inner circle) 1…69 m

MAXDIAM Diameter of enclose circle 8…852 m

HR Affiliation probability to class “high rise“ 0/0,5/1

MINBUDIST Minimal building distance in 100m-Buffer 0…100 m

MEANBUDIS Mean building distance in 100-Buffer 0…81 m

MINBLDIST Minimal building distance to block boundary 0…143 m

NUALLJUNC Number of building junction 0…15

PCLLENGTH Length of building basic line 8…1701 m

overall 46 building parameters

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Building Typology for ClassificationLevel 1 Level 2

Code Name Code Short-Name

Name

1 Multi-family house in closed block structure

11 MFH-G Multi-family house, traditional in closed structure

12 MFH-F Multi-family house, (traditional or new) free-standing

2 Multi-family house in open block structure

21 MFH-TZ Multi-family house, traditional in rows

22 MFH-IZ Multi-family house, industrial in rows

23 MFH-HH Tower block >50m

3 Detached, semi-detach. and row houses

31 EZFH Detached and semi-detached houses (EZFH)

32 RH Row houses

33 DH Traditional village-style house

4 Non-residential 41 IG Industry/commerce

42 BFP Strong functional profile such as administration, health/social, education/research, culture etc.

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www.ioer.deGotthard Meinel, Vortrag, Datum

Building classification - first level

LegendeKlassifikation nach Gestalt

1 - Sehr kleiner Gebäudeverband

2 - Kleiner Gebäudeverband

3 - Zeilenförmiger Gebäudeverband

4 - Sehr breiter Gebäudeverband

5 - Komplexer Gebäudeverband

Building classification by shape- Evaluation of building style (e.g.

line, small)- using only building parameters

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www.ioer.deGotthard Meinel, Vortrag, Datum

Building classification - second levelLegendeGebäudetyp

11- Mehrfamilienhaus (geschlossen)

12 - Mehrfamileinhaus (offen)

21 - Mehrfamilienhaus in Zeile (traditionell)

31 - Ein- und Zweifamilienhaus

41/42 - Nichtwohnnutzung

Building classification by type- Evaluation of block parameters

(e.g. Density, gap, land use class)- Calculation of more parameters

(area part in block)

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www.ioer.deGotthard Meinel, Vortrag, Datum

Block classification (on base on buildings)LegendeStrukturtyp

11 - Geschlossene Bebauung

12 - Offene Blockbebauung

21 - Zeilenbebauung - traditionell

31 - Ein- und Zweifamilienhäuser

41/42 - Nichtwohnnutzung

Nichtwohnnutzung (Freifläche)

Block classification- dominant building type determine block type- combination of area- and count dominant principle

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Calculation of flat and inhabitant densitiesImplementation

Calculation of fix reference densities for inhabitants and flats per sq.m of each building typeCalculation of fix floor numbers for each building type

ApplicationChoice the right reference density for each buildingCalculation of inhabitants and flats by building footprint and floor number for each buildingAggregation of inhabitants and flats for a well know statistical unit (e.g. municipality)Comparison and calculation of a correction factor Correction of inhabitants and flats for each building

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Processing Results17 indicators for all urban blocks in study area:

block type (7 residential and 2 non-residential types)number of buildings (total buildings in each block)building density (number of buildings per hectare block area) total closed area (total of building footprints in block m²) footprint density (ratio of total closed space to area of block)average number of floors (weighted average)total floor space, floor space density building volume, density of building volumenumber of flats, flat densitynumber of residents, density of population

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Overview Statistic ResultsWhole area statistics (e.g. community)- Number of blocks- Block area (ha)- Number of buildings- Total building footprint (ha) - Density of building footprint(ha/ha)- Number of Inhabitants- Number of flats- Building volume (ha³)

Block statistics (for each block type) - Inhabitants, Density of Inhabitants (1/ha)- Number of flats, Flat density (1/ha)- Number of Buildings, Density of Buildings (1/ha)- Total building footprint (m²), Density of building footprints (m²/m²)- Building volume (m³), Density of building volume (m³/m²)- Floor space (m²), Density of floor space (m²/m²)- Average number of floors

Building statistic (for each building type)- Number of inhabitants- Number of flats- Building area (m²)- Building volume (m³)

Each Output with mean, standard deviation, minimum and maximum in cvs-format

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Results (inhabitants density for city Bonn)

Calculation Reference

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Program Implementation

SettlementAnalyzer - SEMENTA®

(patent procedure is running)

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Requirements for Program Implementation

Only using of basic topographic geodata setsOperational utilization for whole countriesUsability for cities and rural areas Objective results All results on base of buildingsCompletely automated procedure Easy-to-use graphical user interface in GISPotential for monitoring

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ArcGIS Extension SEMENTA®

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Parameter settings in SEMENTA®

choice input data output vector or raster regional density parameters

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Status information in SEMENTA®

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Implementation of SEMENTA®

Intuitively designed user interfaceRealize as a GIS-Functionality (AML-Script in ArcInfo)Realize of the image processing steps in VisualBasic.NET under using of Runtime Library of HALCONUser Interface in C# under using of ArcObjects-LibrariesUser Interface -> Extension for ArcGIS Implementation as Toolbar

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Output of SEMENTA®

ArcMap-Project with 17 Layers and intelligent cartographic visualization (predefined legend)

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Results for Dresden - Footprint Density Grundflächendichte

≤ 0,10

> 0,10 - ≤ 0,20

> 0,20 - ≤ 0,30

> 0,30 - ≤ 0,50

> 0,50 - ≤ 1,00

500 0 500 1.000 1.500250 Meter

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Result for Dresden – Flat Density (100m-Raster)

Wohnungszahl(1 ha - Zellen)

0 - < 25

25 - < 50

50 - < 75

75 - < 100

100 - < 125

125 - < 150

150 - < 175

175 - < 200

≥ 200

Gebäude

500 0 500 1.000250m

Leibniz-Institut fürökologische Raumentwicklung e.V. Dresden

Copyright © 2006 IÖR Dresden

Dresden - Center

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Application Fields of SEMENTA®Planning

City-, regional- und state planningPlanning of public infra structure (e.g. technical infrastructure: gas, electric power, water, telecommunication, social infrastructure)Outline of settlement areas (city kernel, suburban, outskirts) on base of objective criteria'sCivil protection, risk mapping and catastrophe managementTransport system planning (emission of noise and air pollution for inhabitants, planning of public transport system)

StatisticDisaggregation of statistical values (e.g. inhabitants, land use, buildings, residential areas) and there visualization

Geomarketinglocation planning, customer distribution

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Further DevelopmentsBetter results by better input data (DTK25)Optimization of building and block indicatorsRefinement of building and block classification scheme (better differentiation of floor numbers and building types in rural areas)Improve building and block classification (e.g. neuronal networks)Regionalization of building reference density values (inhabitants and flats)Increase of performance

Handling with historical maps -> monitoring of settlement development

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OutlookUsing SEMENTA® in a Germany-wide monitor for settlement and open space developmentBetter information for planning and evaluation of the sustainability of land use developmentSpecial SEMENTA® tools for applications in transport system, infrastructure, risk maps and catastrophe managementSEMENTA-Adaption on maps from other countries -> international utilization

Example for combining image processing of raster maps , statistical analysis, geoprocessing and user friendly implementation

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Thanks for your attention!Contact: [email protected]