In this paper, we describe how the sausage buffering approach was developed to be repeatable across platforms and places. hardware and operating systems)? Second, does such an approach accurately measure where people can get to from a starting point and the environments they experience along the way? That is, does it measure relevant parts of the local environment?
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First, is it possible to create an approach that can be consistently and easily replicated across software versions and platforms (i.e. This paper explores an alternative to using proprietary algorithms-constructing a network buffer from scratch.
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This change, made by a popular GIS developer, highlights the proprietary nature of much GIS software and its design for professional rather than research contexts. Esri (originally Environmental Systems Research Institute) is the dominant developer of GIS software with a 30 % market share internationally in 2009, almost double that of its closest competitor, Integraph. However, in the mid-2000s, Esri ArcGIS software abruptly changed its method for calculating such buffers making older analyses not directly comparable to recent assessments. Network buffers have been made easy to use in the past decade and a half by algorithms in GIS programs previous to the development of these algorithms, creating them manually was a tedious task. In contrast, network or street distance buffers try to more closely approximate the experience of moving around an area by measuring out a certain distance on the street network and then using some method of joining the dots. Straight line buffers (also called circular, crow flies, or airline buffers) simply go out a certain distance in a straight line from the facility or place, creating a circle (if the item being buffered is a point). In order to understand environmental contexts of populations, researchers have often used “buffers” of a certain distance around key environments such as homes, schools, work sites, and parks. In the past two decades GIS techniques have been increasingly used for these purposes. Accurately capturing exposure and access to different food and physical activity environments is of great interest to researchers engaged in identifying environmental correlates of dietary patterns, physical activity and obesity.