The present historical moment may seem a particularly inopportune time to review Bowling Alone, Robert Putnam's latest exploration of civic decline in America. After all, the outpouring of volunteerism, solidarity, patriotism, and self-sacrifice displayed by Americans in the wake of the September 11 terrorist attacks appears to fly in the face of Putnam's central argument: that \"social capital\" -defined as \"social networks and the norms of reciprocity and trustworthiness that arise from them\" (p. 19)'has declined to dangerously low levels in America over the last three decades. However, Putnam is not fazed in the least by the recent effusion of solidarity. Quite the contrary, he sees in it the potential to \"reverse what has been a 30to 40-year steady decline in most measures of connectedness or community.\"' As an example of how the current \"war on terrorism\" could generate a durable civic renewal, Putnam points to the burst in civic practices that occurred during and after World War II, which he says \"permanently marked\" the generation that lived through it and had a \"terrific effect on American public life over the last half-century.\" 3 If Americans can follow this example and channel their current civic

Bowling alone: The collapse and revival of American community

A parameter-free model predicts patterns of commuting, phone calls and trade using only population density at all intermediate points. Since the 1940s, planners needing to predict population movement, transport-network usage and even epidemics have turned to a model based on the 'gravity law'. This assumes that the number of individuals travelling between two locations is proportional to the population at the source and destination, and decays with distance. This approach has its limitations, because it looks at the flow between two specific points only. Here, Albert-Laszlo Barabasi and colleagues present an alternative model that takes into account population density at all intermediate points. Their parameter-free radiation model predicts a range of phenomena — from commuting and migrations to phone calls — much more accurately than the gravity model. Needing only data on population densities, which are easy to measure, the system can be used to predict commuting and transport patterns even in areas where data are not collected systematically. Introduced in its contemporary form in 1946 (ref. 1), but with roots that go back to the eighteenth century2, the gravity law1,3,4 is the prevailing framework with which to predict population movement3,5,6, cargo shipping volume7 and inter-city phone calls8,9, as well as bilateral trade flows between nations10. Despite its widespread use, it relies on adjustable parameters that vary from region to region and suffers from known analytic inconsistencies. Here we introduce a stochastic process capturing local mobility decisions that helps us analytically derive commuting and mobility fluxes that require as input only information on the population distribution. The resulting radiation model predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena, from long-term migration patterns to communication volume between different regions. Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes11,12,13,14,15,16,17,18,19,20,21,22,23.

A universal model for mobility and migration patterns

In this paper the examination of the modifiable areal unit problem is extended into multivariate statistical analysis. In an investigation of the parameter estimates from a multiple linear regression model and a multiple logit regression model, conclusions are drawn about the sensitivity of such estimates to variations in scale and zoning systems. The modifiable areal unit problem is shown to be essentially unpredictable in its intensity and effects in multivariate statistical analysis and is therefore a much greater problem than in univariate or bivariate analysis. The results of this analysis are rather depressing in that they provide strong evidence of the unreliability of any multivariate analysis undertaken with data from areal units. Given that such analyses can only be expected to increase with the imminent availability of new census data both in the United Kingdom and in the USA, and the current proliferation of GIS (geographical information system) technology which permits even more access to agg...

The Modifiable Areal Unit Problem in Multivariate Statistical Analysis

HEALTH AND DEPRIVATION: Inequality and the north

Preface Readership Acknowledgements Introduction Part I. The Context for Spatial Data Analysis: 1. Spatial data analysis: scientific and policy context 2. The nature of spatial data Part II. Spatial Data: Obtaining Data And Quality Issues: 3. Obtaining spatial data through sampling 4. Data quality: implications for spatial data analysis Part III. The Exploratory Analysis of Spatial Data: 5. Exploratory analysis of spatial data 6. Exploratory spatial data analysis: visualisation methods 7. Exploratory spatial data analysis: numerical methods Part IV. Hypothesis Testing in the Presence of Spatial Autocorrelation: 8. Hypothesis testing in the presence of spatial dependence Part V. Modeling Spatial Data: 9. Models for the statistical analysis of spatial data 10. Statistical modeling of spatial variation: descriptive modeling 11. Statistical modeling of spatial variation: explanatory modeling Appendices References Index.

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Spatial Data Analysis: Theory and Practice

List of figures List of tables List of boxes List of contributors Preface Acknowledgements 1 Introduction 2 Philosophies underlying human geography research Section A Preparing for the research project 3 Choosing a topic 4 Finding previous work on the topic 5 Secondary data Section B Collecting primary data 6 Questionnaire design and sampling 7 Tell me about ...: using interviews as a research methodology 8 Focus groups 9 'Participatory' approaches and diagramming techniques 10 Participant observation Section C Analysis of data 11 Analysing numerical spatial data 12 Analysing categorical data 13 Analysing qualitative materials 14 Textual analysis: reading culture and context 15 Visual methodologies: what you see is not always what you get 16 Geographical information systems and spatial analysis Section D Producing the report 17 Designing the report 18 Writing the report 19 Illustrating the report 20 Conclusion References Index

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Methods in Human Geography : A guide for students doing a research project

Tell me about . . . : using interviews as a research methodology

In this paper [the authors] suggest an alternative method for fitting the gravity model. In this method the interaction variable is treated as the outcome of a discrete probability process whose mean is a function of the size and distance variables. This treatment seems appropriate when the dependent variable represents a count of the number of items (people vehicles shipments) moving from one place to another. It would seem to have special advantages where there are some pairs of places between which few items move. The argument will be illustrated with reference to data on the numbers of migrants moving in 1970-1971 between pairs of the 126 labor market areas defined for Great Britain.... (EXCERPT)

A method of fitting the gravity model based on the poisson distribution

Neighbourhood effects on health: does it matter where you draw the boundaries?

In geographical research the data of interest are often in the form of counts. Standard regression analysis is inappropriate for such data, but if certain assumptions are met, a form of regression based on the Poisson distribution can be used. This paper illustrates the use of Poisson regression in the computer package GLIM with an example from historical geography. Apprentice migration to Edinburgh is regressed on a combination of categorical, count, and continuous explanatory variables.

Robin Flowerdew

Papers

Methods in Human Geography : A guide for students doing a research project

Tell me about . . . : using interviews as a research methodology

A method of fitting the gravity model based on the poisson distribution

Neighbourhood effects on health: does it matter where you draw the boundaries?

Analysis of count data using poisson regression