New Opportunities for Urban Analysis

Identification of the urban area by using population surface models based on postcode information

Urban System Analysis

Generally, urban systems consist of: • socio-economic functions

– people and what they do

• Built environmentIdentifying urban areas and their growth (urban morphology),

therefore, might be through one or more of the following ways:• Identification of built form • Identification of human activity pattern, for example:

residential densities• Measures of dynamics

The disadvantages of RS in Urban Analysis

RS has been used for a long time in urban analysis (Mesev, 1997). However, it is a rather indirect and inferential method. Urban surfaces always provide very difficult challenges for RS, i.e.:

• To determine land use providing perspective upon the functioning of urban settlements (Mesev, Batty, &Longley, 1996; Longley, Mesev, 2001),

• To measure change (Brouwer, Valenzuela, Calencia, &Sijmons, 1990)

The New Opportunity in Urban Analysis

• Code-point data

• Address-point data

The increasing availability of new disaggregate, frequently updated data pertaining to socio-economic characteristics is making possible new kinds of urban analysis and modelling (M. Goodwin, D. Unwin, 2000):

Summary of data sourcesCode-Point Address-Point

Date vendor

First data collection Geography Frequency of updates Sampling unit Average number of households per unit Data-type Resolution  

Content

Ordnance Survey (GB) 1997 (as Data-Point) GB 6 months Unit Postcode About 12 (GB)  Attributed point Most postcode centroids precise to 1m Total number of residential and commercial mail delivery points per postcode

Ordnance Survey (GB) 1996 GB 6 months Mail delivery point Should be 1 in the majority of cases Attributed point Most points precise to 0.1m  Full address and National Grid Reference of individual delivery points.

Source: New Data and Approaches for Urban Analysis, R. Harris and P. Longley, 2000

Some Features of the Above New socio-economic Data

• More positional precise— non-aggregate/or low aggregation

• More frequently updated

• Providing a better prospective upon reflecting human activity patterns more directly

Two Cases

With these new available data, some urban analysis has been done by using

the method of population surface models.

1. Modelling residential densities in Bristol area (R. Harris, P. Longley, 2000)

Address-point enables the identification of buildings with considerable precision

2. Defining and delineating the central areas of towns (Thurstain-Goodwin, Unwin, 2000)

Code-point data makes the spatial resolution of the result much higher than that achieved using the ED.

Code-point data affords the possibility of much greater precision in the statistical aggregations of town centres (and indeed of any other geographically identifiable object).

Both of these two researches support the view that identification of urban land area or town centre can be done faster and more precisely based on the new data-point dataset through the methodology of population surface models.

Population Surface Model

• In principle, the PSM approach involves the redistribution of data points into the cells of a (raster) output grid. A user-defined search window is positioned over each data point in turn. The count associated with the current point is then distributed into the cells falling within the window, according to weighting derived from a distance-decay function.

The Distance-decay Function put at its simplest, it is a model parameter that influences the weight given to cells close to the current point within the search window thus controlling the form of the distribution.

Weighting

Distance

Central Point j

Wij

dij K

The weighting of each cell Wij on the basis of a variable width search window k

Benefits of Surface Approach

• Simple to visualise big datasets– ease of communication– identification of errors in raw data (Thurstain-Goodwin,

2001)

• Overlay for composite indicators– Simple addition where attribute metric is the

same (Thurstain-Goodwin, 2001)• i.e. can be used for overlays in GIS

The Problems of Population Surface Modelling

Barr and Barnsley (1997) argue that the PSM approach has practical and philosophical problems.

• In practical terms, the optimum size of the search window must normally be determined experimentally and is not allowed to vary throughout the study region.– Use of adaptive kernels (though still dependent on original search

window size)The original model also has other problems where the data point density is low, consisting of

individual isolated data point ( Martin, et.al, 2000 ).

• In such a situation, population is artificially dispersed in a symmetrical or isotropic fashion around each data point location although actually it should be displayed as a linear or strongly clustered anisotropic form

My interests(I like food and videos. My favourite TV program is The

Simpsons.)Aim:Therefore, bearing the RS and PSM issues in mind, my current interest is to experiment with

various types of point-pattern analysis of space-filling raster modelling such as the PSM, to produce models of urban population density and urban boundary locations based on the postcode data.

Objectives:• To measure how good the procedure of identifying urban area by using

PSM is• To measure how it compares with traditional RS-based methodologies• To measure how sensitive are the results to the various modelling

parameters• Improving the model

DATA

Current data available: • Code-point data (Source: Ordnance Survey)

• Urban boundaries data (provided by Prof. John Shepherd, created based on Ordnance Survey maps)

• Address-Point

Urban Boundaries Data Code-Point Data

What I am going to do next?Rationale: Taken urban boundaries data file as a basic line to measure the

effect of urban area identified by using code-point data and Population Surface Models.

1. ArcGIS software will be used.

2. Selecting study area---a specific city,based on some criteria:

– It should be a manageable area

– It should have no policy constraint

– It should belong to fast/fair growing area.

3. Getting the corresponding data from urban boundaries data and code-pint data

Code-point data of Bristol

Urban Boundaries data of Bristol

4. Using PSM to identify urban area based on code-point data

– the distance-decay function - and kernel width K will be changed to measure how good the procedure and how sensitive of the results will be.

5. Go to the the pub!