The Geography of Revolution: The case of Longford, 1917-1921

Why were some areas of the Ireland more active than others during the War of Independence, and why did the areas of most activity change over the course of the war between 1919 and 1921? In the context of the Irish midlands, County Longford stands out as one of the most violent counties surrounded by areas where there was much less activity by the IRA. Even within the county there was a significant difference in the strength of republican activity between north and south Longford. This article will examine the factors that were responsible for the strength of the IRA campaign in this midland enclave, including socio-economic conditions, administrative decisions and failures, and the contemporary political context.
Much of the evidence upon which the paper is based comes from applications made by Longford Volunteers for military service pensions, granted to veterans of the campaign by the Irish government after 1924. Many of these documents are soon to be released by the Irish government. The paper will also include a discussion of these sources and the way in which they can be used by historians to advance our understanding of Ireland’s revolutionary decade.

Use of emerging technologies to assess differences in outdoor physical activity in St. Louis, Missouri.

Introduction: Abundant evidence shows that regular physical activity (PA) is an effective strategy for preventing obesity in people of diverse socioeconomic status (SES) and racial groups. The proportion of PA performed in parks and how this differs by proximate neighborhood SES has not been thoroughly investigated. The present project analyzes online public web data feeds to assess differences in outdoor PA by neighborhood SES in St. Louis, MO, USA.
Methods: First, running and walking routes submitted by users of the website MapMyRun.com were downloaded. The website enables participants to plan, map, record, and share their exercise routes and outdoor activities like runs, walks, and hikes in an online database. Next, the routes were visually illustrated using geographic information systems. Thereafter, using park data and 2010 Missouri census poverty data, the odds of running and walking routes traversing a low-SES neighborhood, and traversing a park in a low-SES neighborhood were examined in comparison to the odds of routes traversing higher-SES neighborhoods and higher-SES parks.
Results: Results show that a majority of running and walking routes occur in or at least traverse through a park. However, this finding does not hold when comparing low-SES neighborhoods to higher-SES neighborhoods in St. Louis. The odds of running in a park in a low-SES neighborhood were 54% lower than running in a park in a higher-SES neighborhood (OR = 0.46, CI = 0.17-1.23). The odds of walking in a park in a low-SES neighborhood were 17% lower than walking in a park in a higher-SES neighborhood (OR = 0.83, CI = 0.26-2.61).
Conclusion: The novel methods of this study include the use of inexpensive, unobtrusive, and publicly available web data feeds to examine PA in parks and differences by neighborhood SES. Emerging technologies like MapMyRun.com present significant advantages to enhance tracking of user-defined PA across large geographic and temporal settings.