Handbook of applied spatial analysis: software tools,methods and applications,edited by M.M. Fischer and A. Getis

With the rising oil prices, climate change, and the ever increasing burden of nutrition-related disease, food security is of growing research interest in academic disciplines spanning agronomy to epidemiology to urban planning. Some governments have developed progressive policies encouraging individuals to consume locally produced foods in order to support local economies, improve agricultural sustainability and community access to food, and to plan and prepare for adverse environmental impacts on food security. However, fundamental methods are lacking for conducting research on food security across these various disciplines. In this article, we first present a method to measure agricultural self-sufficiency, which we refer to as our self-sufficiency index (SSI) for the province of British Columbia, Canada. We then present a Bayesian autoregressive framework utilizing readily available agricultural data to develop predictive smoothing models for the SSI. We find that regional capital investment in agriculture and cropland acreage is the strong predictor of SSI. To accommodate spatial variability, we compare linear regression models with spatially correlated errors to less traditional spatially varying coefficient models, and find that the former class results in better model fit. The smoothed maps suggest that relatively strong self-sufficiency exists only in subset clusters in the Okanagan, Peace River, and lower mainland regions. In spite of policy to promote local food, the existing local agricultural system is insufficient to support a large-scale shift to local diets. Our approach to estimating neighborhood-based self-sufficiency with a predictive model can be extended for use in other regions where limited data are available to directly assess local agriculture and benefit from explicit consideration of spatial structure in the local food system.     

Comment on 'Systematic determination of earthquake rupture directivity and fault planes from analysis of long-period P-wave spectra' by L. M. Warren and P. M. Shearer

Warren and Shearer describe a method of estimating the duration of P pulses radiated by earthquakes, on the assumption that the source is a unilateral fracture. The estimates are made in the frequency domain. The estimates obtained by Warren and Shearer for seven of the earthquakes are compared to durations estimated here in the time domain; the time-domain measurements being made on broad-band seismograms (∼0.1–4.0 Hz) derived by filtering from short-period recordings. Overall, the time-domain method indicates that the pulse duration of the earthquakes studied here range from 2.0 to 7.6 s, whereas the estimates from the results of Warren and Shearer range from 7.1 to 9.8 s. This suggests that the method of Warren and Shearer cannot resolve pulse lengths less than about 7 s. The suggestion is supported by the estimates of the duration of rupture—fault length/speed of rupture—implied by the results of Warren and Shearer. For, although the estimated fault-length ranges from 0.8 km to over 40 km most rupture durations are around 8 s.     

“城市空间转向”与新城市地理研究

在全球化与信息技术革命以及经济地理学的复式转向的推动下,“城市空间转向”已成为事实,城市地理研究不仅需要对物质空间进行研究,更要对人文空间及文化空间等进行研究,其研究的视点是城市空间的主观性与客观性的融合,顺应这种转变,开展新城市地理研究就成为必然。     

Interpreting What is Rural and Urban for Western U.S. Counties

The paper examines how rural and urban are interpreted and applied to counties in eleven states in the western United States. After reviewing various conceptual approaches, we turn to a three-part analysis of county commissioners' perceptions and census data to identify characteristics associated with urban-ness and rurality. The analysis involves comparing qualitative and quantitative survey and interview data, using a multiple regression analysis to correlate census variables with commissioners' perceptions of their home counties, and using cluster analysis techniques on census variables to identify patterns and unevenness in rurality and urbanness. Three characteristics mentioned by the widest range of commissioners and found to be statistically significant in the regression analysis were population concentration, total population, and the agricultural land base. Population concentration, in particular, was identified most frequently by interviewees as the single most important variable in characterizing an urban county and had the most meaningful contribution to predicting commissioners' perceptions of their home counties in the regression analysis. The cluster analysis identified five county types: largest urban centers, growing regional hubs, high growth rural, dispersed rural, and stable rural agriculture. These county types were widely distributed, reflecting the spatial unevenness of macroscale processes operating across eleven western states.     

Interpreting What is Rural and Urban for Western U.S. Counties

The paper examines how rural and urban are interpreted and applied to counties in eleven states in the western United States. After reviewing various conceptual approaches, we turn to a three‐part analysis of county commissioners' perceptions and census data to identify characteristics associated with urban‐ness and rurality. The analysis involves comparing qualitative and quantitative survey and interview data, using a multiple regression analysis to correlate census variables with commissioners' perceptions of their home counties, and using cluster analysis techniques on census variables to identify patterns and unevenness in rurality and urbanness. Three characteristics mentioned by the widest range of commissioners and found to be statistically significant in the regression analysis were population concentration, total population, and the agricultural land base. Population concentration, in particular, was identified most frequently by interviewees as the single most important variable in characterizing an urban county and had the most meaningful contribution to predicting commissioners' perceptions of their home counties in the regression analysis. The cluster analysis identified five county types: largest urban centers, growing regional hubs, high growth rural, dispersed rural, and stable rural agriculture. These county types were widely distributed, reflecting the spatial unevenness of macroscale processes operating across eleven western states.