Re-investing in the Built Environment: A Spatial Network Approach

Before launching ambitious and expensive development programmes to induce new regional technology corridors and clusters, it is critical to appreciate existing spatial economic patterns in a region. Initial economic conditions drive location decisions of firms and a labour force such that any changes must intercede onto an existing landscape built for current economic conditions. This work adopts a simple regional economic model to integrate and review traditional and modern urban location theories in order to illustrate the power of initial conditions to determine a final result. A simple spatial dynamic simulation model captures many of the pertinent effects of real estate pricing patterns to frame both opportunities and constraints to re-shape an urban landscape. Attention to 'ground up' spatially correlated location patterns revealed in price data that suggests close attention to strategic zoning can have profound impacts on the success or failure of economic development. Relatively modest policy interventions that carefully utilize existing preferences for urban amenities and concurrent real property investments involve fewer policy risks with potentially more powerful stimulative economic consequences than promised in more ambitious programmes.     

Spatial‐temporal patterns of snow cover in western Canada

Snow cover is often measured as snow‐water equivalent (SWE), which refers to the amount of water stored in a snow pack that would be available upon melting. Snow cover and SWE represent a source of local snow‐melt release, and are sensitive to regional and global atmospheric circulation, and changes in climate. Monitoring SWE using satellite‐based passive microwave radiometry has provided nearly three decades of continuous data for North America. The availability of spatially and temporally extensive SWE data enables a better understanding of the nature of space‐time trends in snow cover, changes in these trends and linking these trends to underlying landscape and terrain characteristics. To address these interests, we quantify the spatial pattern of SWE by applying a local measure of spatial autocorrelation to 25 years of mean February SWE derived from passive microwave retrievals. Using a method for characterizing the temporal trends in the spatial pattern of SWE, temporal trends and variability in spatial autocorrelation are quantified. Results indicate that within the Canadian Prairies, extreme values of SWE are becoming more spatially coherent, with potential impacts on water availability, and hazards such as flooding. These results also highlight the need for Canadian ecological management units that consider winter conditions.