Data Requirements, Collection and Management

3.3.1 Data requirements

According to the key components involved in the concept of accessibility (land use, transportation, temporal and people), and guided by the requirements for

implementing the modified 2SFCA and 3SFCA measures of accessibility, the data collection efforts in this study have been aimed at clarifying the following three key issues: (1) spatial distribution of population at fine spatial resolution, or based on the smallest possible residential areas; (2) spatial distribution of green spaces that are accessible freely by the residents most of the time; and (3) spatial configuration of local road networks that connecting population at local residential areas and neighbourhood green spaces (Table 3.3.1).

3.3.2 Data collection

The smallest spatial unit at which the 2011 ABS census was released is called Statistical Area Level 1 (SA1), which is represented by a unique seven-digit code and contains such population information usage by year and sex. For the 2011 Census, there are about 37,000 SA1 throughout Australia (this includes the Other Territories of Christmas and Cocos (Keeling) Islands and Jervis Bay). On average, each urban SA1 has about 225 dwellings; but in rural areas, the number of

dwellings per SA1 declines as population density decreases.

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Table 3.3.1 A summary of data requirements

According to ABS (http://www.abs.gov.au/), the spatial units of SA1 are designed based on the following considerations:

 SA1s should be consistent with both their role as a useful spatial unit and building block capable of aggregation into broader level Australia Statistical Geography Classification (ASGC) spatial units, and with the collectors' workload requirements.

 The chosen SA1 boundaries should, if possible, be readily identifiable on the ground and be defined in terms of permanent features; follow the centre of a road or river if these features are used; and delimit SA1s which conform to existing and proposed land uses.

 The use of major roads as SA1 boundaries in rural areas is avoided, where possible, to minimise splitting of identifiable rural localities.

 SA1s should conform where possible to existing/gazetted suburb boundaries, and must not cross Statistical Local Area (SLA) boundaries and, as a

consequence, any other ASGC spatial unit boundary.

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 SA1s in aggregate must cover the whole of Australia without gaps or overlaps.

 SA1s are created in response to significant changes in population within a given area, or if boundaries of larger geographical areas change. For example, if the population within an existing SA1 increases to the point of being too large for one collector, the SA1 may be split into two or more SA1s.

If growth in the population of a locality or urban centre results in expansion of its boundary, new SA1s may be created by division of the SA1s into which the growth intrudes, so that the new boundary may adequately reflect the urban growth in census results (this process is often referred to as

fragmentation). Where necessary, SA1s are created or boundaries adjusted to conform with changes to LGA boundaries.

These considerations are aimed at maintaining as much comparability between censuses as possible. New SA1 boundaries are designed with reference to

information obtained from government authorities, census collector comments from the previous census, local knowledge, field inspections, and aerial photography.

Mesh Blocks (MB), as the smallest geographical regions in the ASGC scheme (SA1 is the smallest population units), thus enable a ready comparison of statistics

between geographical areas. Age-specific population data for SA1 and digital files containing SA1 and MB spatial boundaries can be downloaded directly from the ABS website (http://www.abs.gov.au/).

All green spaces included in this study are open green spaces that are freely accessible to the public most of the time. Green spaces excluded from this study include all green spaces in school campuses (which are not accessible to the public during schooling hours) and all fee-charging green spaces (including golf courses, stadium etc). In addition, all green spaces whose area size is less than 0.02 ha (200 m²) are also excluded from this study.

Based on the understanding gained from the literature review and, in addition to the size and quietness of the green space, 9 types of green space facilities have been chosen for this study, including playground, bench, toilet, walking track, sport oval, sport court, and water body. Data on most green space facilities can be downloaded from the Parks Victoria website (http://parkweb.vic.gov.au/), the Department of Sustainability and Environment website (http://www.dse.vic.gov.au/), and the LGAs

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websites, with some uncertain facilities clarified by personal observation on the Google or in the field.

3.3.3 Data Management

In this study, all datasets collected and prepared are stored and managed with a geodatabase in ArcGIS; and all spatial datasets are projected onto a coordinate system to enable their integration with other geographical data layers within a common coordinate framework, "GDA_1994_MGA_Zone_55", i.e. zone 55 of the Map Grid of Australia, based on Transverse Mercator projection and the Geocentric Datum of Australia introduced in 1994.

A geodatabase combines "geo" (spatial data) with "database" (data repository) to create a central data repository for spatial data storage and management. The geodatabase in ArcGIS is based on a series of simple yet essential relational database concepts to leverage the strengths of the underlying database

management system (DBMS). Simple tables and well-defined attribute types are used to store the schema, rule, base, and spatial attribute data for each

geographical dataset. This approach provides a formal model for storing and working with spatial and non-spatial datasets. Through this approach, structured query language (SQL) based relational functions and operators can be used to create, modify, and query tables and their data elements.

A geodatabase consists of a set of tables, feature classes and feature datasets (Figure 3.3.1). Feature classes are homogeneous collections of common features, each having the same spatial representation, such as points, lines, or polygons, and a common set of attribute columns, for example, a line feature class for representing road centrelines. The four most commonly used feature classes in the geodatabase are points, lines, polygons, and annotation. A feature dataset is a collection of related feature classes that share a common coordinate system. Feature datasets used to spatially or thematically integrate related feature classes. Their primary purpose is for organizing related feature classes into a common dataset for building a topology, a network dataset, a terrain dataset, or a geometric network.

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Figure 3.3.1 Organization of source datasets into a geodatabase in ArcGIS