M. John Hodgson

Measuring Neighbourhood Spatial Accessibility to Urban Amenities: Does Aggregation Error Matter?

Neighbourhood spatial accessibility (NSA) refers to the ease with which residents of a given neighbourhood can reach amenities. NSA indicators have been used to inform urban policy issues, such as amenity provision and spatial equity. NSA measures are, however, susceptible to numerous methodological problems. We investigate one methodological issue, aggregation error, as it relates to the measurement of NSA. Aggregation error arises when, for the purpose of distance calculations, a single point is used to represent a neighbourhood, which in turn represents an aggregation of spatially distributed individuals. NSA to three types of recreational amenities (playgrounds, community halls, and leisure centres) in Edmonton, Alberta, Canada is used to assess whether aggregation error affects NSA measures. The authors use exploratory spatial data analysis techniques, including local indicators of spatial association, to examine aggregation-error effects on NSA. By integrating finer resolution data into NSA measures, we demonstrate that aggregation error does affect NSA indicators, but that the effect depends on the type of amenity under investigation. Aggregation error is particularly problematic when measuring NSA to amenities that are abundant and have highly localized service areas, such as playgrounds. We recommend that, when analyzing NSA to these types of amenities, researchers integrate finer resolution data to indicate the spatial distribution of individuals within neighbourhoods better, and hence reduce aggregation error.

A Covering Tour Model for Planning Mobile Health Care Facilities in SuhumDistrict, Ghama

Providing Primary Health Care (PHC) in developing countries is very troublesome. The dilemma of having enough facilities to be geographically accessible, yet few enough of them to be properly stocked and staffed is a major stumbling block. Accissibility problems are exacerbated by extensive rainy seasons in which travel is possible only on paved roads. We investigate the ability of using mobile facilities to resolve this dilemma in Suhum District, Ghana.

A network location-allocation model trading off flow capturing and p -median objectives

The flow capturing and thep-median location—allocation models deal quite differently with demand for service in a network. Thep-median model assumes that demand is expressed at nodes and locates facilities to minimize the total distance between such demand nodes and the nearest facility. The flow-capturing model assumes that demand is expressed on links and locates facilities to maximize the one-time exposure of such traffic to facilities. Demand in a network is often of both types: it is expressed by passing flows and by consumers centred in residential areas, aggregated as nodes. We here present a hybrid model with the dual objective of serving both types of demand. We use this model to examine the tradeoff between serving the two types of demand in a small test network using synthetic demand data. A major result is the counter-intuitive finding that thep-median model is more susceptible to impairment by the flow capturing objective than is the flow capturing model to thep-median objective. The results encourage us to apply the model to a real-world network using actual traffic data.

Applying the flow-capturing location-allocation model to an authentic network: Edmonton, Canada

Traditional location-allocation models aim to locate network facilities to optimally serve demand expressed as weights at nodes. For some types of facilities demand is not expressed at nodes, but as passing network traffic. The flow-capturing location-allocation model responds to this type of demand and seeks to maximize one-time exposure of such traffic to facilities. This new model has previously been investigated only with small and contrived problems. In this paper, we apply the flow-capturing location-allocation model to morning-peak traffic in Edmonton, Canada. We explore the effectiveness of exact, vertex substitution, and greedy solution procedures; the first two are computationally demanding, the greedy is very efficient and extremely robust. We hypothesize that the greedy algorithms robustness is enhanced by the structured flow present in an authentic urban road network. The flow-capturing model was derived to overcome flow cannibalization, wasteful redundant flow-capturing; we demonstrate that this is an important consideration in an authentic network. We conclude that real-world testing is an important aspect of location model development.

Heuristic concentration for the p-median: an example demonstrating how and why it works

We map certain combinatorial aspects of the p-median problem and explore their effects on the efficacy of a common (1-opt) interchange heuristic and of heuristic concentration (HC) for the problems solution. Although the problems combinatorial characteristics exist in abstract space, its data exist in two-dimensional space and are therefore mappable. By simultaneously analysing the problems patterns in geographic space and its combinatorial characteristics in abstract space, we provide new insight into what demand node configurations cause problems for the interchange heuristic and how HC overcomes these problems.

The P-Centroid Problem on an Inclined Plane

We model a log harvesting problem as one of how to slide blocks along an inclined plane. To analyze this problem we develop and test a P-centroid location-allocation model that minimizes aggregate work. The model provides insight into the ideal shapes, locations and numbers of harvesting sites.

Optimal facility location with multi-purpose trip making

Abstract We address the optimal location problem for two different types of service, designated as type-A and type-B, where some users may obtain both types of service in multi-purpose trips. We consider three user groups: (i) users of only the type-A service; (ii) users of only the type-B service; and (iii) users of both services in a single trip. We seek to locate three types of facilities: (i) type-A; (ii) type-B; and (iii) joint facilities that offer both services. We formulate a p-median-based model that minimizes the total travel distance and use it to investigate the effect of multi-purpose trip-makers on optimal facility types and locations. Examples are used to show that services tend to cluster in joint facilities, even when the proportion of multi-purpose behavior is small.

Testing a Bicriterion Location-Allocation Model with Real-World Network Traffic: The Case of Edmonton, Canada

The p-median model in a network treats demand for service as weights expressed at nodes. The flow-capturing location-allocation model treats demands expressed by traffic (OD) flows. We consider a bicriterion model, a hybrid of these two, which trades off node- and flow-based demand, and apply it to realworld data. These are journey to work data in a system of 177 traffic zones in Edmonton, Canada. The system is relatively complex, comprising 703 nodes, 2198 links and 23,350 OD pairs.

A hierarchical location-allocation model with travel based on expected referral distances

The hierarchical p-median location-allocation model assumes that patrons always travel to the closest facility of appropriate level and that their interests are best served when the distances they must travel to do this are minimized. This assumption about travel behavior is unrealistic, patrons in the real world are known, for instance, to bypass lower level facilities that can serve their needs to attend higher level facilities. We introduce the concept of “expected distance under referral” to deal with such irrationality and incorporate it into a location-allocation model that minimizes the negative effects of such irrational behavior. We demonstrate the model with several types of non-optimal travel behavior.

Multi-Service Facility Location Models

The multi-service facility (MSF) concept proposes the co-location of a range of human services under a single roof. Thus, for example, services for preschoolers, teens, and seniors might be co-located within a joint facility. MSFs are a response to economies of scale; co-location reduces the monetary costs of providing a variety of services. Yet, the spatial distributions of different societal groups are different within a city – an MSF system will not provide optimum geographical accessibility to individual groups. We introduce two p-median based location–allocation models that trade off the fixed costs of providing services and opening facilities with the travel costs of three societal groups. We observe that some mixes of single- and multi-service facilities can provide efficient service systems without unduly compromising the accessibility needs of individual groups.