Rural-urban classification schemes are frequently used in ecological studies of population health. However, the algorithms used to produce these classifications as well as their underlying assumptions may not match their intended use in health research. Here, we focus on the spatial distribution of features of the physical environment that are related to health - such as healthcare - to examine the extent to which eight classification schemes capture the heterogeneous context of rural places. We further explore how well rural-urban classifications distinguish between different types of rural places by comparing rural Tribal reservations with other rural areas in the American southwest. Because health services and infrastructure are often distributed through state and federal programs to underserved populations in rural areas, this approach speaks to the broader political implications in how rural communities are defined and represented. Results indicate that rural-urban classifications do not adequately reflect heterogeneous contexts within and across rural places. We advocate for more appropriate population health models that explain contextual differences in the relationship between health and place.

The least-cost surface (LCS) calculation is a compute-intensive problem conventionally solved by the queue-based Dijkstra’s algorithm. Alternative raster-based scanning algorithms have also been proposed which use a moving window to scan the whole study area iteratively. Here we propose improvements to the raster-based algorithms. The main improvement is to implement multiple scanning orders (MSO) to replace the conventional single scanning order (SSO, typically from upper-left corner to lower-right corner, row by row). We compared the performance of different algorithms over different cost surfaces and with different numbers of source points. The comparison shows that a raster-based algorithm adopting MSO has a substantially better performance than a conventional raster-based algorithm using SSO. An MSO raster-based algorithm is generally comparable to the queue-based Dijkstra’s algorithm, and surpasses the latter over a relatively simple cost surface (e.g. in which the cost is resampled) and/or when the number of source points is relatively large. Our empirical experiments suggest that MSO reduces the time complexity from to Θ(N2) to Θ(NlogN). Additionally, we found that the MSO raster-based algorithm can be easily parallelized using shared-memory parallel programming.

The spatial scan statistic method has been widely used for detecting disease clusters. Its results may be affected by scales, including the aggregation level of the input data and the population threshold used in the detection. Previous studies offered inconsistent findings, and few had considered both types of scales at the same time. Using 24 simulated datasets and two real disease datasets, we investigated the method’s sensitivity to the two types of scales. We aggregated the individual-level data into areal units of three levels, including county, town, and a 900 m grid. We detected clusters with three population thresholds, including 10%, 25%, and 50%. We used two measurements, distance between cluster centres and the Jaccard index, to quantify the consistency of clusters detected with different scale settings. We find: (1) the method is not greatly sensitive to the data aggregation level when the cluster is strong and in a place with high population density; (2) the method’s sensitivity to the population threshold is determined by the actual size of the true cluster; and (3) a regular grid with fine resolution is advantageous over the subjectively defined areal units. The process and findings may have broader meanings to similar spatial analyses.

Conventionally, a raster operation that needs to scan the entire image employs only one scanning order (i.e., single scanning order (SSO)), and the scan usually runs from upper left to lower right and row by row. We explore the idea of alternately applying multiple scanning orders (MSO) to raster operations that are based on the local direction, using the flow accumulation (FA) calculation as an example. We constructed several FA methods based on MSO, and compared them with those widely used methods. Our comparison includes experiments over digital elevation models (DEMs) of different landforms and DEMs of different resolutions. For each DEM, we calculated both single-direction FA (SD-FA) and multi-direction FA (MD-FA). In the theoretical aspect, we deducted the time complexity of an MSO sequential algorithm (MSOsq) for FA based on empirical equations in hydrology. Findings from the experiments include the following: (1) an MSO-based method is generally superior to its counterpart SSO-based method. (2) The advantage of MSO is more significant in the SD-FA calculation than in the MD-FA calculation. (3) For SD-FA, the best method among the compared methods is the one that combines the MSOsq and the depth-first algorithm. This method surpasses the commonly recommended dependency graph algorithm, in both speed and memory use. (4) The differences between the compared methods are not sensitive to specific landforms. (5) For SD-FA, the advantage of MSO-based methods is more obvious in a higher DEM resolution, but this does not apply to MD-FA.

Optimizing land use allocation is a challenging task, as it involves multiple stakeholders with conflicting objectives. In addition, the solution space of the optimization grows exponentially as the size of the region and the resolution increase. This article presents a new ant colony optimization algorithm by incorporating multiple types of ants for solving complex multiple land use allocation problems. A spatial exchange mechanism is used to deal with competition between different types of land use allocation. This multi-type ant colony optimization optimal multiple land allocation (MACO-MLA) model was successfully applied to a case study in Panyu, Guangdong, China, a large region with an area of 1,454,285 cells. The proposed model took only about 25 minutes to find near-optimal solution in terms of overall suitability, compactness, and cost. Comparison indicates that MACO-MLA can yield better performances than the simulated annealing (SA) and the genetic algorithm (GA) methods. It is found that MACO-MLA has an improvement of the total utility value over SA and GA methods by 4.5% and 1.3%, respectively. The computation time of this proposed model amounts to only 2.6% and 12.3%, respectively, of that of the SA and GA methods. The experiments have demonstrated that the proposed model was an efficient and effective optimization technique for generating optimal land use patterns.

This paper considers the problem of downsampling H.264 compressed video, where a full-resolution compressed video sequence conforming to H.264 is transcoded into another compressed video sequence conforming to H.264 at a lower target resolution. A transcoding framework that makes efficient use of full-resolution information is proposed. In this framework, residuals and motion vectors at the target resolution are first predicted from their full-resolution counterparts. These predicted residuals and motion vectors are then applied to optimize the actual rate-distortion (RD) performance. Experimental results show that, compared against the benchmark system, which cascades an H.264 decoder, a spatial downsampler, and an H.264 encoder, and is generally regarded having the best possible RD performance, the proposed framework, surprisingly, provides consistently superior RD performance with up to 0.7 dB gain. Furthermore, the framework has the desired feature of being configurable to strike the right balance between rate-distortion performance and computational complexity according to application requirements.

Cellular automata (CA) have been increasingly used in simulating urban expansion and land-use dynamics. However, most urban CA models rely on empirical data for deriving transition rules, assuming that the historical trend will continue into the future. Such inertia CA models do not take into account possible external interventions, particularly planning policies, and thus have rarely been used in urban and land-use planning. This paper proposes to use artificial immune systems (AIS) as a technique for incorporating external interventions and generating alternatives in urban simulation. Inspired by biological immune systems, the primary process of AIS is the evolution of a set of ‘antibodies’ that are capable of learning through interactions with a set of sample ‘antigens’. These ‘antibodies’ finally get ‘matured’ and can be used to identify/classify other ‘antigens’. An AIS-based CA model incorporates planning policies by altering the evolution mechanism of the ‘antibodies’. Such a model is capable of generating different scenarios of urban development under different land-use policies, with which the planners will be able to answer ‘what if’ questions and to evaluate different options. We applied an AIS-based CA model to the simulation of urban agglomeration development in the Pearl River Delta in southern China. Our experiments demonstrate that the proposed model can be very useful in exploring various planning scenarios of urban development.

This article identifies and compares four different methods for dealing with inhomogeneous backgrounds in kernel density estimation. The four methods result from combinations of two bandwidth types (fixed vs. adaptive) and two adjustment sides (site side vs. case side). The fixed and adaptive bandwidths employ different uniform bases in density calculation (spatial extent vs. population support). The adaptive bandwidth's strength lies in identifying spatial extents of density variation. It also produces values that are more comparable between locations and more stable statistically. When making adjustments to address the background, the site-side method makes the adjustment at each site for which the density value is to be estimated, and the case-side method makes the adjustment at each case location. Within a disease-mapping context, the former measures population at risk around each site and the latter measures around each disease case. The case-side adjustment is more justifiable in an application like disease mapping. It is also less sensitive to spatial details of the background (a favorable feature) and considerably more computationally efficient. Lung cancer data from Merrimack County, New Hampshire, USA, are used to demonstrate and compare the results from the four methods, leading to the conclusion that the case-side-adaptive-bandwidth method is most advantageous.