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1 – 10 of 31Zachary Hornberger, Bruce Cox and Raymond R. Hill
Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces…
Abstract
Purpose
Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces errors. Significant theoretical research has been performed related to the modifiable areal unit problem and the zone definition problem. Minimal research has been accomplished related to the specific issues inherent to spatiotemporal demand data, such as search and rescue (SAR) data. This study provides a quantitative comparison of various aggregation methodologies and their relation to distance and volume based aggregation errors.
Design/methodology/approach
This paper introduces and applies a framework for comparing both deterministic and stochastic aggregation methods using distance- and volume-based aggregation error metrics. This paper additionally applies weighted versions of these metrics to account for the reality that demand events are nonhomogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand data set of SAR events in the Pacific Ocean. Comparisons using these metrics are conducted between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering.
Findings
As quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while using fewer zones. However, the higher fidelity aggregations detrimentally affect volume error. Additionally, by splitting the SAR data set into training and test sets this paper shows the stochastic zonal distribution aggregation method is effective at simulating actual future demands.
Originality/value
This study indicates no singular best aggregation method exists, by quantifying trade-offs in aggregation-induced errors practitioners can utilize the method that minimizes errors most relevant to their study. Study also quantifies the ability of a stochastic zonal distribution method to effectively simulate future demand data.
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Luke Keele, Scott Lorch, Molly Passarella, Dylan Small and Rocío Titiunik
We study research designs where a binary treatment changes discontinuously at the border between administrative units such as states, counties, or municipalities, creating a…
Abstract
We study research designs where a binary treatment changes discontinuously at the border between administrative units such as states, counties, or municipalities, creating a treated and a control area. This type of geographically discontinuous treatment assignment can be analyzed in a standard regression discontinuity (RD) framework if the exact geographic location of each unit in the dataset is known. Such data, however, is often unavailable due to privacy considerations or measurement limitations. In the absence of geo-referenced individual-level data, two scenarios can arise depending on what kind of geographic information is available. If researchers have information about each observation’s location within aggregate but small geographic units, a modified RD framework can be applied, where the running variable is treated as discrete instead of continuous. If researchers lack this type of information and instead only have access to the location of units within coarse aggregate geographic units that are too large to be considered in an RD framework, the available coarse geographic information can be used to create a band or buffer around the border, only including in the analysis observations that fall within this band. We characterize each scenario, and also discuss several methodological challenges that are common to all research designs based on geographically discontinuous treatment assignments. We illustrate these issues with an original geographic application that studies the effect of introducing copayments for the use of the Children’s Health Insurance Program in the United States, focusing on the border between Illinois and Wisconsin.
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The purpose of the paper is to verify whether the version of neighbourhoods created from the lowest geographical level improve a predictive accuracy of hedonic model in comparison…
Abstract
Purpose
The purpose of the paper is to verify whether the version of neighbourhoods created from the lowest geographical level improve a predictive accuracy of hedonic model in comparison with those based on upper geographical levels.
Design/methodology/approach
The paper proposes a method for defining neighbourhoods using Thiessen polygons. The clustering technique is based on fuzzy equality. Clustering is started at different geographical levels: municipalities, traffic analysis zones, and apartment blocks' Thiessen polygons. Delineated neighbourhoods are incorporated into hedonic model of apartment prices, the applied methodologies are ordinary least squares and spatial error.
Findings
With ordinary least squares regression, the slight superiority of Thiessen polygons is found in both in‐sample analysis and ex‐sample prediction. With spatial error technique, the clusters of Thiessen polygons do not always provide the best outcome, and their superiority is contested by the highest geographical level of municipalities.
Research limitations/implications
This paper is the first attempt to apply the proposed method, which not always demonstrates clear superiority. In future study, the method of neighbourhood delineation could be used in combination with market segmentation.
Practical implications
The proposal to use Thiessen polygons as a transition from points to continuous space can outline a base for the use of different clustering techniques, which are applicable to delineate neighbourhoods in housing market studies, in particular for the assessment purpose. The fuzzy equality clustering algorithm itself can be applied to polygonal data.
Originality/value
The originality of the proposed method is that it defines neighbourhoods starting from individual observations applying fuzzy equality. Its advantages are an increased independence from existing boundaries, self‐determination of a number of clusters, and total coverage of an area.
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The purpose of this paper is to investigate access to grocery retailing in Nantes, France.
Abstract
Purpose
The purpose of this paper is to investigate access to grocery retailing in Nantes, France.
Design/methodology/approach
The spatial distribution of all grocery retailers in Nantes was mapped. Socio‐demographic data as supplied by INSEE was mapped for Nantes, and these data used to determine areas of poor access to healthy food, e.g. fresh fruit and vegetables retailing.
Findings
There are six areas of Nantes which appear to have both poor physical access to grocery retailing and a socio‐demographic profile which suggests people living there may have difficulties in travelling to remote shops. These six areas generally do not coincide with the officially‐recognised ZUS deprived areas of Nantes.
Research limitations/implications
Data on obesity and related medical conditions were absent from INSEE, limiting the analysis that could be performed. The data were also liable to errors such as MAUP and ecological fallacy; however, the spatial detail was sufficient for meaningful conclusions to be drawn.
Practical implications
Previous food and dietary research in France has concentrated on economic factors mediating diet. There has been less research on spatial access to food and any correlations with areas of poverty or areas with other populations, e.g. pensioners, who may find travel to remote shops difficult. This research investigates these spatial linkages. Officially‐recognised areas of poverty in Nantes (ZUS areas) are not the areas presenting the most problematical physical access to healthy food retailing, therefore research based on financial aspects alone may miss some areas of difficult food access.
Originality/value
The spatial patterns of food access in Nantes, and the implications for targeting research and policy initiatives to these areas, have not previously been researched.
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Makoto Chikaraishi, Akimasa Fujiwara, Junyi Zhang and Dirk Zumkeller
Purpose — This study proposes an optimal survey design method for multi-day and multi-period panels that maximizes the statistical power of the parameter of interest under the…
Abstract
Purpose — This study proposes an optimal survey design method for multi-day and multi-period panels that maximizes the statistical power of the parameter of interest under the conditions that non-linear changes in response to a policy intervention over time can be expected.
Design/methodology/approach — The proposed method addresses balances among sample size, survey duration for each wave and frequency of observation. Higher-order polynomial changes in the parameter are also addressed, allowing us to calculate optimal sampling designs for non-linear changes in response to a given policy intervention.
Findings — One of the most important findings is that variation structure in the behaviour of interest strongly influences how surveys are designed to maximize statistical power, while the type of policy to be evaluated does not influence it so much. Empirical results done by using German Mobility Panel data indicate that not only are more data collection waves needed, but longer multi-day periods of behavioural observations per wave are needed as well, with the increase in the non-linearity of the changes in response to a policy intervention.
Originality/value — This study extends previous studies on sampling designs for travel diary survey by dealing with statistical relations between sample size, survey duration for each wave, and frequency of observation, and provides the numerical and empirical results to show how the proposed method works.
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This paper aims to investigate the statistical and geographical links between the prevalence of obesity and a range of socio‐economic indicators in a major UK city
Abstract
Purpose
This paper aims to investigate the statistical and geographical links between the prevalence of obesity and a range of socio‐economic indicators in a major UK city
Design/methodology/approach
The geographical pattern of fresh fruit and vegetable retailing was mapped across Birmingham (UK), and this data was combined with UK census data from Neighbourhood Statistics to investigate possible correlations between obesity and the social geography of this city. To further elicit the varying underlying links between obesity and social conditions, a methodology of partial correlations was used to create “social transects” across Birmingham so the operational effects of social conditions upon poor diet could be investigated across a range of Birmingham neighbourhood types.
Findings
Across Birmingham as a whole, people whose ethnic or social make‐up did not fit the dominant group in their neighbourhood were more likely to be obese than those of the majority socio‐ethnic group for that area. The level of qualifications was the dominant influencer on obesity and diet. Particularly, less wealthy people in the more affluent areas of Birmingham were likely to suffer financial difficulties in eating healthily. However, in less affluent areas, being in (low‐paid) work actually increased the chances of being obese, as compared to being unemployed in these districts.
Research limitations/implications
Changes in the pattern of retailing or changes in individual's social status over the period of this research may confound the results; however the research may be regarded as a snapshot of conditions in Birmingham in ca.2006. The areal analysis may be confounded by the MAUP problem, although as distance to shops does not emerge as a major predictor of obesity, the results are still valid. The research applies to only one city (Birmingham), although a wide range of neighbourhood types typical of other British cities are covered.
Practical implications
Time limitations emerge as a significant factor in diet, especially in the less‐affluent areas of Birmingham. The significance of a range of social indicators upon diet is greatly affected by the range of neighbourhood types sampled. Factors barring access to a healthy diet can vary upon very small scales, even down to the individual household. Distance to shops has an effect upon diet, but only as a “moderating factor” acting in conjunction with a wider range of economic and social factors.
Social implications
The effects of poverty, and especially unemployment, have very different effects upon diet and obesity in poor as compared to affluent areas; and in poorer areas, time limitations upon households operate so as to worsen the diet of those in low‐paid work. This implies that dietary improvement initiatives aimed at the less well off should aim for a compromise between health and convenience; otherwise such initiatives will merely widen health inequalities. Minority groups in all areas, whether a minority by ethnicity, age, or wealth, need special attention by dietary investigators.
Originality/value
The use of partial correlations to elicit the different responses to socio‐economic conditions as regards diet has not been applied before to a major UK city. The distance to shops for all residential areas for a major UK city has not been previously mapped.