Browsing by Subject "spatial dependence"

Now showing 1 - 2 of 2
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    Not ‘Islands, Entire of Themselves’: Exploring the Spatial Context of City-level Robbery Rates
    (2008-12) Deane, G.; Messner, S. F.; McGeever, K.; Stucky, Thomas D.; Kubrin, C. E.
    The current study examines spatial dependence in robbery rates for a sample of 1,056 cities with 25,000 or more residents over the 2000–2003 period. Although commonly considered in some macro-level research, spatial processes have not been examined in relation to city-level variation in robbery. The results of our regression analyses suggest that city robbery rates are not spatially independent. We find that spatial dependence is better accounted for by spatial error models than by spatial lag models. Further exploration of various spatial weights matrices indicates that robbery rates of cities within the same state are related to robbery rates of other cities within the same state, regardless of their proximity. Our analyses illustrate how systematic inquiry into spatial processes can alert researchers to important omitted variable biases and identify intriguing problems for future research.
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    Spatial Effects in Energy-Efficient Residential HVAC Technology Adoption
    (2013-05) Noonan, Douglas S.; Hsieh, Lin-Han Chiang; Matisoff, Daniel C.
    If your neighborhood adopts greener, energy-efficient residential heating, ventilating, and air conditioning (HVAC) systems, will your proenvironmental behavior become contagious, spilling over into adjacent neighborhoods’ HVAC adoptions? Objective data on more than 300,000 detailed single-family house sale records in the Greater Chicago area from 1992 to 2004 are aggregated to census block-group neighborhoods to answer that question. Spatial lag regression models show that spatial dependence or “contagion” exists for neighborhood adoption of energy-efficient HVACs. Specifically, if 625 of 726 homes in a demonstration neighborhood upgraded to green HVAC, data of this study predict that at least 98 upgrades would occur in adjacent neighborhoods, more than doubling their baseline adoption rates. This spatial multiplier substantially magnifies the effects of factors affecting adoption rates. These results have important policy implications, especially in the context of new standards for neighborhood development, such as Leadership in Energy and Environmental Design (LEED) or Low-Impact Development standards.