By Frank M. Howell, Jeremy R. Porter, Stephen A. Matthews
With a special concentrate on middle-range concept, this publication info the appliance of spatial research to demographic learn as a manner of integrating and higher knowing different transitional parts of the final demographic transition.
This ebook first information key suggestions and measures in glossy spatial demography and exhibits how they are often utilized to middle-range idea to higher comprehend humans, areas, groups and relationships through the international. subsequent, it indicates middle-range concept in perform, from utilizing spatial facts as a proxy for social technological know-how records to studying the impression of "fracking” in Pennsylvania at the formation of latest coalitions between environmental advocacy businesses. The publication additionally strains destiny advancements and gives a few capability ideas to selling and facilitating guideline in spatial demography.
This quantity is a perfect source for complicated undergraduate and graduate scholars enrolled in classes related to spatial analyses within the social sciences, from sociology and political technology to economics and academic examine. additionally, students and others attracted to the function that geographic context performs with regards to their study will locate this booklet a worthy consultant in additional constructing their paintings.
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Extra resources for Recapturing Space: New Middle-Range Theory in Spatial Demography
2001; Spielman and Yoo 2009; Speilman et al. 2013). , Fotheringham and Wong 1991; Moon et al. 2005; Root 2012). Readers should be aware that using smaller geographic units may provide better contextmeasures (Clapp and Wang 2006; Coulton et al. 2011; Hipp 2007), but the call for smaller and smaller nesting units can lead to a non-beneficial degree of reduction (see later example based on Duncan et al. 1961). 3 As noted in Matthews (Chap. 17, this volume), the instructional opportunities in the area of spatial demography do not necessarily facilitate or promote exposure to the geographical literature.
2004; Snijders and Bosker 1999). Although multilevel modeling—as all statistical techniques—has some limitations (Greenland 2000), the advantage of using a multilevel modeling approach was that it allowed the inclusion of a contextual measure by enabling “specification of appropriate error structures, including random intercepts and random coefficients” (Raudenbush 1988, p. , pupils, within classrooms, within schools, within districts or within sectors) and among organizational structures (Raudenbush and Bryk 1986).
2002). This led us to propose the use of local spatial clustering at the tract level (the same local Moran’s i as used in studies mentioned above to identify spatial clusters of crime or disease) to identify statistically significant clusters, and we treated these larger areas as the groups’ ethnic neighborhoods (see also Logan and Zhang 2004). Grannis (1998) reaches a somewhat similar result by a different route, proposing to identify what he calls t-neighborhoods within areas bounded by major streets and validating this designation with evidence of social homogeneity within these areas.