BUS606: Operations and Supply Chain Management

Activity space

An activity space represents all locations visited by an individual within a specified time period. Activity spaces are important to consider because residents often engage in a multitude of activities outside of their local environment. The geographical extent of an activity space is likely to be determined by both environmental and individual-level factors. For instance, the proximity of resources dictates how far an individual is required to travel to reach these while at the individual-level factors such as age, gender, access to a motor vehicle, and/or perception of distance and safety all influence the ability and willingness of an individual to access the resource. Mapping an individual's activity space potentially provides a more precise reflection of their true contextual exposures and therefore improves specificity between the exposure and behavioural or health outcomes. Activity spaces may be captured through personal diaries where individuals record daily activities or the use of Global Positioning System (GPS) devices. An individual's travel patterns can be represented as an activity space within a GIS using a variety of methods with two examples being mapping a buffer around the travel routes and locations visited during the day (see Figure 1) or through 3-D visualisation which can be used to display space-time parameters that effectively represent the regularity of travel patterns.

Figure 1

Examples of measures of accessibility. Terms: accessibility, activity space, buffer, centroid (geometric within an administrative unit), network distance. This figure demonstrates the different approaches to measuring boundaries of spatial units used for accessibility measures such as density. Firstly, the point from which the measures will be taken is defined; in this case a geometric centroid of an administrative unit (census collector district, the smallest administrative spatial unit in Australia) is calculated. From this point, two buffers are drawn; the first using Euclidean (straight-line) distance and the other using network distance. The third spatial unit relates to activity space. This relates to an individual's travel patterns over a course of a day with the destinations visited and the travel routes mapped (both of which can be captured using a Global Position System (GPS) device). A buffer is also placed around these to capture exposures nearby to the visited locations and also nearby to their household (represented by the geometric centroid).

Buffer

Buffers are boundaries placed around areas (e.g. the boundary of an administrative unit) or points (e.g. a household or the centroid of an administrative unit) using a predefined scale using either a straight-line (Euclidean) or network distance (Figure 1). Buffers are useful for capturing all features of the built environment that surround a particular location. For example, the number of supermarkets within a buffer might be used to estimate a household's accessibility to supermarkets. However, limitations include the binary representation of a features (e.g. it is either considered in our out of the buffer) which can be overcome with the consideration of a fuzzy (using a decreasing weight function for distances further away) rather than sharp boundary. Buffers are readily created within a GIS once the user has defined the scale, type (Euclidean and network distance), and point they are measuring from (e.g. around a household or centroid). These decisions should be informed by the hypothesised relationship between the exposure and outcome.

Centroid (geometric and population-weighted)

A centroid is a single point, representing the 'centre', of a spatial unit (Figure 1). Centroids may be used as the point from which exposure measures are undertaken such as proximity estimates or the density of features in a buffer. GIS enables the identification of geometric centroids (the geographical centre) or population-weighted centroids (the point that minimises the total distance to all the residents (or households) in an area). Population-weighted centroids are particularly useful when the population is homogeneously distributed in space (such as in rural areas or larger spatial units) and where a geometric centroid will not result in a precise representation of accessibility for most residents. However, neither centroid measure will provide data as precise as individual-level measures (e.g. using individual household location to derive accessibility measures).

Connectivity

Connectivity relates to the availability and directness of travel routes used to move through a network from an origin to a destination. Common approaches to the measurement and assessment of connectivity include: 1) identifying the spacing between streets (with a tight grid formation resulting in higher connectivity); 2) assessing the amount of intersections with connecting streets that provide four or more routes choices (as opposed to t-intersections and dead-ends); and 3) comparing the network distance to the Euclidean distance (a network distance that is only marginally above Euclidean distance indicates a very direct route along the network). High connectivity improves accessibility by providing a more direct route and shortening the required travel distance (Figure 2a). Neighbourhoods with low connectivity might contain numerous cul-de-sacs, large block sizes and fewer intersections (Figure 2b). When investigating connectivity for walking purposes, it is important to also include paths used solely for pedestrian purposes as street-network databases tend to be restricted to parts of the network accessible to motor vehicles. Higher levels of connectivity has been associated with greater levels of physical activity as shorter and more direct routes encourage walking for transport and reduce car dependency. However, high street-network connectivity may also negatively impact on walkability by potentially increasing motor vehicle traffic on residential streets, thus reducing pedestrian safety. Further, whilst connectivity may inform us about the directness of the route, it is only a single aspect related to walkability and, measured alone, it is unlikely to provide sufficient information to determine whether an area is considered walkable.

Figure 2

Comparison of environments with: a) a grid street pattern with high-connectivity; b) a poorly connected street network. Terms: accessibility, connectivity, walkability. Figure 2 demonstrates the differences between high street-network connectivity (Figure 2a) that would provide a more direct route between a origin and destination compared to low street-network connectivity with many cul-de-sacs and dead-ends (Figure 2b) which reduces the directness travel routes.

Density

Density is a measure of the intensity of exposure to features of the built environment and may be an important determinant of health behaviours as it relates to the accessibility of potentially health promoting and health damaging environmental characteristics. Density may be expressed simply as a count of features within a specified area (e.g. total number within a postcode or a buffer) but is more accurately represented as the relative number of features per population (e.g. number per 10,000 people) or per geographic area (e.g. number per square kilometre). Adjustment for population or geographic area is most useful when trying to explain the distribution of features across areas as these may provide an explanation as to why some features appears in greater numbers in some areas and not others. For parks and open spaces, density may be reflected by the count of features or the geographic area of these features. Continuous measures of density assume that the association between the feature and the health outcome of interest increases linearly with each unit increase, however it is possible that once the density of a feature reaches a certain threshold further increases in density may no longer be linearly associated with the outcome. For example, having access to multiple McDonalds restaurants means accessibility is increased through greater exposure but this exposure is to the same product so it does not improve your product choice or variety.

Kernel density estimation

Kernel density estimation is a technique for transforming point data to a continuous density surface map whereby the density of a feature can be estimated for any point on the surface (Figure 3). To create Kernel density estimates, the entire study region is partitioned into grid cells of a predetermined size. The kernels (which are usually circular in shape with the radius defined by the user) are then placed around the centroid of each cell (or alternatively the crossing point of the grid cells). For each feature within the kernel, weights are assigned as a defined function of distance from the geometric centroid of the kernel. This results in a density value being assigned to each cell so that density values can be calculated across the whole study region. In studies of the built environment and health, the technique has previously been used to calculate robust measures of exposure to one or more environmental features (e.g. access to food outlets or recreational facilities) across a study area. This approach is advantageous compared to traditional density measures because a resource that is located closer to the grid cell is assigned more weight than resources that are located further away, with the weight approaching zero at the boundary of the kernel. Thus, the transition to the boundary represents a fuzzy rather than sharp boundary and can be utilised as a gravity-based measure of accessibility.

Figure 3

An example of a map resulting from kernel density estimation. Terms: accessibility, kernel density estimation. Figure 3 demonstrates the output map resulting from kernel density estimation with the kernel size set at two kilometres. Darker areas indicate where resources are more densely located while lighter colouring relate to areas with reduced accessibility.

Land use and land use mix

Broad categories of typical land uses include (but are not limited to) residential, office, commercial, industrial, and recreational and there are multiple existing measures of land use mix. A specific example of a measure of land use mix is the "dissimilarity index" which measures the evenness of the distribution of a range of land uses across a predefined geographical area. Areas with low land use mix are homogenous in terms of the uses of space (e.g. area is mostly residential or commercial) whereas areas with a high land use mix have a greater variety of land uses (such as recreational, industrial, commercial, educational etc.). Residents of neighbourhoods with a mixture of land uses have higher accessibility to features they may wish to visit and consequently a more confined activity space. Some research has demonstrated people living in neighbourhoods with a high land use mix are more likely to be physically active (through active travel) and have a lower likelihood of overweight or obesity however others have shown that the presence of specific walkable land uses (e.g. parks) may be more important than having equal amounts of different land uses in an area. GIS enables the integration of land use data from a range of sources from which the user can develop measures of land use mix.

Network distance

Network analysis enables the measurement of the distance between an origin and destination along a network of lines which can include road, public transportation, pedestrian and/or cycling network paths. Because distance is measured along the transportation network rather than as Euclidean (straight-line) distance, network distance can provide a more precise measure of accessibility (Figure 1). Within built environments, the network travel distance required to reach a destination may be significantly greater than the straight line distance due to features related to the built environment (e.g. the presence of buildings), natural barriers (e.g. rivers or steep hills), and characteristics of the network itself (e.g. cul-de-sacs, one-way streets). Network distance measures can be readily calculated within GIS provided that accurate network data are available. Measures of travel time can also be derived (e.g. the number of minutes required to travel from a participant's house to the closest swimming pool) in a GIS using information on network distance and the average speed of travel along each segment of the network. It is also feasible to develop more sophisticated measures of travel time that incorporate factors such as traffic density, traffic signals, road surface, and topography, each of which would improve estimates of accessibility.

Proximity

Proximity, or closest facility analysis, is an important indicator of accessibility and is used to determine which feature (e.g. gymnasium) is closest to a particular point (e.g. household location) and/or the actual distance to the nearest feature. Proximity is important because accessibility is increased when features are closer thus potentially influencing their contribution to health behaviours. Proximity can be measured using Euclidean distance, network distance, or the estimated travel time along a network. Proximity measures derived from network analysis are based on a least-cost analysis; that is, the shortest distance or time from an origin to a destination. As the actual travel routes for study subjects are not usually known, least-cost analysis is considered the best approximation as it assumes the subject would use the shortest travel route (or quickest if travel time estimations are used).

Walkability

Walkability can be conceptualised in terms of four key components: functionality, safety, aesthetics, and destinations. Each component of walkability has a number of sub-categories that can be created within GIS. For example, connectivity is one feature of functionality while land use mix relates to the presence and variety of destinations. Whilst there are a number of potential variants to measures of walkability, to date these have not been consistently measured. Residents living in environments considered more 'walkable' have been linked to increased levels of physical activity and lower BMI. Specifically, levels of walking may be enhanced through higher pedestrian-network connectivity and greater land use mix. Since walking is the most common form of physical activity, identifying the key attributes of the physical environment that contribute to walking is of considerable public health importance. Whilst data acquisition for some walkability measures such as presence of traffic control devices or walking paths can often be sourced from existing GIS databases, others related to aesthetics such as litter and graffiti tend to require observers to specifically audit areas.