New Wave of Meso‐scale Spatial Analysis:

F t d thi ki tFrom aggregated thinking to non‐aggregated thinkinggg g g

Ko‐Ko LwinYuji Murayama

(Division of Spatial Information Science, ( p ,University of Tsukuba

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Previous analysisーDivision of space－

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Previous analysisーDivision of timeー

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トト

t t+1 t+23

time 4

………

time 2

time 3

time 1

time 2

Geographical matrix cubeGeographical matrix cube

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Spatial SegregationSpat a Seg egat o

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L i K Y M 2009 A GIS hLwin, K., Y. Murayama. 2009. A GIS approach to estimation of building population for micro-spatial analysis. Transactions in GIS,13, 401-414..

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M E T H O D O L O G Y

A GIS approach to estimation of building populationAreametric method

A GIS approach to estimation of building population

Volumetric methodNumber of floor approachNumber of floor approach

Average building height approach

Total building volume approach Apply GIS Theory and

Practice

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E S T I M A T I O N O F B U I L D I N G P O P U L A T I O NStudy Area

Total Census Tracts: 94Population: 84,955Area: 5,959.13HectaresPlace: Part of Tsukuba City

Reasons:Population integrityHeterogeneous landscapeHeterogeneous landscapeRich of geo‐informationHome of University of Tsukuba

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E S T I M A T I O N O F B U I L D I N G P O P U L A T I O NList of Data

List of Data

LIDAR LIDAR Point (Provided by PASCO Corp.)

OrthoS i l l i 8 X 8Spatial resolution = 8cm X 8cmCensus TractsPolygon (Population)yg ( p )

Building footprintsPolygon (Name, Floors, Block No, …)

iTownpage (NTT)Business information in CSV format

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E S T I M A T I O N O F B U I L D I N G P O P U L A T I O NData Processing

Generation of DHM and DVMGeneration of DHM and DVM

Feature to TIN

TIN : Triangulated Irregular Network  Model TIN to RASTER

Advantages:Faster than other methods (IDW, Spline, Kirging, ….)Eliminate mosaicking process ArcGIS TIN Engine allows multiple scenes processingmultiple scenes processing 

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Chapter3 E S T I M A T I O N O F B U I L D I N G P O P U L A T I O N

Data Processing

Extraction of Building Height and Volume AttributeArcGIS (Zonal Statistic as Table)

Zonal statistic function summarizes thevalues of a raster (DHM or DVM) withinthe zones of another dataset (buildingfootprints) and reports the results to a table.

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Chapter3 E S T I M A T I O N O F B U I L D I N G P O P U L A T I O N

Data Processing

Separation of Residential and Non­Residential BuildingStep 1: Filter by zero census tractsU h• University campus, research centers, …Step 2: Filter by Building Area and HeightS f 20 2 d h i ht 2• Surface area < 20m2 and height < 2m• Removing cars, bicycle stand roofs, garbage boxes, porticos, etc.Step 3: Filter by Public Facilities• Financial institutions, governmental organizations, educational organizations (using NTT iTownpage)educational organizations, … (using NTT iTownpage)Step 4: Manual Filtering•Multi‐storey car parking lots, ….Multi storey car parking lots, ….

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Chapter3 E S T I M A T I O N O F B U I L D I N G P O P U L A T I O N

Result and Validation

Estimated Building Population

Result and Validation

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O N L I N E M I C R O–S P A T I A L A N A L Y S I SMap Layers and Data Sources

L O d L N D i ti F t Vi ibilit SLayer Order Layer Name Description Feature Visibility Source1 BUILDING Building footprints with estimated population  Polygon Visible Zmap‐TOWNII2 FACILITY Facility locations Point Visible NTT Townpage3 ROAD Road outlines Line Visible Zmap‐TOWNII4 ROAD_NODE Road nodes Point Hidden GSI5 ADMIN‐BND Administration boundary Polygon Visible Zmap‐TOWNII

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O N L I N E M I C R O–S P A T I A L A N A L Y S I SMeasurements

Online Micro spatial Pop lation Anal sis ( SPA)Urban planners

Online Micro­spatial Population Analysis (µSPA)http://land.geo.tsukuba.ac.jp/microspa

Utilize GIS estimated building population for micro‐spatial analysis C i imicro‐spatial analysisWhat We MeasureMeasure three spatial elements interactively• Building Population

Connectivity

Building Population• Facility Locations• Connectivity networksWho Can Use

Residents Business ownersLocal residents, business owners, urban planners, potential home buyers, … FacilityBuilding Populationy15

O N L I N E M I C R O–S P A T I A L A N A L Y S I SMeasurements

M t  f M  C tMeasurement of Mean CentersMean Center                         Weighted Mean CenterHow We Measure

Weighted Mean Center•W. Population Mean Center•W Facility Mean CenterW. Facility Mean Center• Connectivity Mean CenterSpatial Indices

Dangling nodeReal node

Spatial Indices• Population Mean Center Index• Facility Mean Center Index• Connectivity Mean Center Indexy

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O N L I N E M I C R O–S P A T I A L A N A L Y S I SMeasurements

Measurement of IndicesPopulation mean centerFacility mean centerConnectivity mean centeryUser defined point

Indices Measurement• Population Mean Center Index• Facility Mean Center Index• Connectivity Mean Center Index

Index = d / rd = Mean center distancer= Circle radius

dr= Circle radius

Value = 0 ~ 1

r

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O N L I N E M I C R O–S P A T I A L A N A L Y S I SMap Tools and Analysis Domains

Example of Polygon ToolANALYSIS TYPE: Find LocationANALYSIS TOOL: PolygonDOMAIN AREA: 683400.22 m2

TOTAL BLDG. POPULATION: 4184WBLDG POP MEAN CENTER 23993 29 7783 57W.BLDG. POP. MEAN CENTER: 23993.29; 7783.57FACILITY MEAN CENTER: 23887.44; 7917.41CONNECTIVITY MEAN CENTER: 23983.76; 7741.37

Potential ApplicationsppLocal community center allocationPublic facility site sitting …

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O N L I N E M I C R O–S P A T I A L A N A L Y S I SMap Tools and Analysis Domains

Example of Line Tool

Local community bus route planningTraffic noise impactPlanners                                             Residents

Traffic noise impact19

C O N C L U S I O NPotential Applications

Improve Accuracy in Micro­spatial Analysis

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C O N C L U S I O N

Dasymetric Mapping

Potential Applications

Dasymetric mapping based on GIS estimated building populationGIS estimated building population

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C O N C L U S I O N

3D Visualization

Potential Applications

Applications are numerous … … …22

The

END Thank You!

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