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This study integrates novel data on 100-year flood hazard extents, exposure of residential properties, and place-based social vulnerability to comprehensively assess and compare flood risk between Indigenous communities living on 985 reserve lands and other Canadian communities across 3701 census subdivisions. National-scale exposure of residential properties to fluvial, pluvial, and coastal flooding was estimated at the 100-year return period. A social vulnerability index (SVI) was developed and included 49 variables from the national census that represent demographic, social, economic, cultural, and infrastructure/community indicators of vulnerability. Geographic information system-based bivariate choropleth mapping of the composite SVI scores and of flood exposure of residential properties and population was completed to assess the spatial variation of flood risk. We found that about 81% of the 985 Indigenous land reserves had some flood exposure that impacted either population or residential properties. Our analysis indicates that residential property-level flood exposure is similar between non-Indigenous and Indigenous communities, but socioeconomic vulnerability is higher on reserve lands, which confirms that the overall risk of Indigenous communities is higher. Findings suggest the need for more local verification of flood risk in Indigenous communities to address uncertainty in national scale analysis.

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Abstract

Modern databases allow mobile clients, that subscribe to replicated data, to process the replica forgoing continuous connectivity, and to receive the updates while connected to the server. Based on the overlap in client interest pattern, the server can do update processing for manageable number of data-groups instead of perclient basis, and hence decouple the update processing cost from the client population. In this paper, we propose an efficient update propagation method that can be applied to a relational database system irrespective of its inherent data organization. We present computationally efficient algorithms for group design and maintenance based on a heuristic function. We provide experimental results that demonstrate that our approach achieves a significant increase in overall scalability over the client-centric approach.

This study presents the first nationwide spatial assessment of flood risk to identify social vulnerability and flood exposure hotspots that support policies aimed at protecting high-risk populations and geographical regions of Canada. The study used a nationalscale flood hazard dataset (pluvial, fluvial, and coastal) to estimate a 1-in-100-year flood exposure of all residential properties across 5721 census tracts. Residential flood exposure data were spatially integrated with a census-based multidimensional social vulnerability index (SoVI) that included demographic, racial/ethnic, and socioeconomic indicators influencing vulnerability. Using Bivariate Local Indicators of Spatial Association (BiLISA) cluster maps, the study identified geographic concentration of flood risk hotspots where high vulnerability coincided with high flood exposure. The results revealed considerable spatial variations in tract-level social vulnerability and flood exposure. Flood risk hotspots belonged to 410 census tracts, 21 census metropolitan areas, and eight provinces comprising about 1.7 million of the total population and 51% of half-a-million residential properties in Canada. Results identify populations and the geographic regions near the core and dense urban areas predominantly occupying those hotspots. Recognizing priority locations is critically important for government interventions and risk mitigation initiatives considering socio-physical aspects of vulnerability to flooding. Findings reinforce a better understanding of geographic flood-disadvantaged neighborhoods across Canada, where interventions are required to target preparedness, response, and recovery resources that foster socially just flood management strategies. K E Y W O R D S100-year flood hazard, environmental justice, flood risk management, residential flood exposure, social vulnerability

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Modern databases allow mobile clients that subscribe to replicated data, to process the replica without requiring continuous connectivity, and to receive the updates while connected to the server. In such an environment-usually known as the Intermittently Connected Database (ICDB) system-the server should maintain the updates to the database in the log file(s). These update log files should be pruned to reduce update retrieval time. In this paper we propose two pruning algorithms, based on the periodic connectivity of the clients, that consider two scenarios: uniform client connectivity patterns and widely varying client connectivity patterns. In the former case, the complete pruning algorithm is effective in keeping the log file size within a bound, hence reducing both disk I/O during update propagation, and disk storage space; whereas, in case of the latter, the partial pruning algorithm achieves significant further reduction in disk I/O while retrieving the updates. Any reduction in CPU or I/O time in turn reduces wireless connection time for each client resulting in significant savings in time and costs. Experimental results demonstrate the effectiveness of these algorithms.