Explore the vast range of titles available.

Photographs and essays from the artists's collaborative art-as-research projects.

ZIP Codes¹ are commonly used for mapping, spatial analyses, creating tables, or other reporting products. Used for these tasks, the results from using these geographies often are distorted because of adverse statistical properties inherent with ZIP Codes. Summarizing ZIP Code data to other large geographies (for example, county, Core Based Statistical Area, state) associates them with these other geographies to create aggregate counts so that metropolitan or county rankings can be reported. This process requires ZIP Codes to be properly allocated to these other geographies to accurately associate a record with that area. Although some companies or government organizations already provide a crosswalk to these geographies, the allocation method used is unclear, leaving it indiscernible as to the accuracy of the assignment of ZIP Codes. In this article, we demonstrate how to use the U.S. Department of Housing and Urban Development (HUD) United States Postal Service ZIP Code Crosswalk Files to more directly control the ZIP Code allocation process of records to alternative geographies. In meeting this objective, we also provide results of an analysis using the HUD Crosswalk File in associating a ZIP Code with U.S. counties.

[...]we compare the estimates from the crosswalk process with the actual data to gauge the level of accuracy as an indicator of reliability. General Approach to Crosswalking the ZIP Code Files Linking ZIP Code data to any of the ZIP Code crosswalk data sets is not a one-to-one assignment with any of the available geographies HUD offers.5 Rather, a many-to-many approach must be used to ensure each record's proportion in a crosswalk file is associated with the corresponding geography each ZIP Code is contained within or with which it overlaps. [...]when a ZIP Code is assigned to one tract, all the adjacent tracts associated with that same ZIP Code are not assigned any of the address ratios used for analysis. Once joined, a new field is calculated that is the product of the ZIP Code frequency and the tot_ratio field to produce an estimate for 311 rat calls for service. Because ZIP Codes cross-cut multiple counties, duplicate ZIP Code records appear in the crosswalk file, indicating multiple overlaps.

\"A thousand years from now, Vance Astro, Yondu, Martinex and Charlie-27 will rise to free the enslaved planet Earth -- as the Guardians of the Galaxy! Soon, Captain America, Doctor Strange, the Thing, the Hulk and more join the time-spanning heroes in the war to reclaim the future! Threats arise from other worlds -- as well as new allies Nikki and the uncanny Starhawk! But when Guardians and Avengers join forces in the present day, will even the combined might of two millennia be enough to stop the deranged demigod Michael Korvac? Plus: the Silver Surfer, Ms. Marvel, Spider-Man and Adam Warlock!\"-- Amazon.com description.

Objective To determine the safety and efficacy of altering the ratio of carbohydrate and protein in low-energy diets in conjunction with a popular exercise program in obese women. Design Matched, prospective clinical intervention study to assess efficacy of varying ratios of carbohydrate and protein intake in conjunction with a regular exercise program. Participants One-hundred sixty one sedentary, obese, pre-menopausal women (38.5 ± 8.5 yrs, 164.2 ± 6.7 cm, 94.2 ± 18.8 kg, 34.9 ± 6.4 kg·m -2 , 43.8 ± 4.2%) participated in this study. Participants were weight stable and not participating in additional weight loss programs. Methods Participants were assigned to either a no exercise + no diet control (CON), a no diet + exercise group (ND), or one of four diet + exercise groups (presented as kcals; % carbohydrate: protein: fat): 1) a high energy, high carbohydrate, low protein diet (HED) [2,600; 55:15:30%], 2) a very low carbohydrate, high protein diet (VLCHP) [1,200 kcals; 63:7:30%], 3) a low carbohydrate, moderate protein diet (LCMP) [1,200 kcals; 50:20:30%] and 4) a high carbohydrate, low protein diet (HCLP) [1,200 kcals; 55:15:30%]. Participants in exercise groups (all but CON) performed a pneumatic resistance-based, circuit training program under supervision three times per week. Measurements Anthropometric, body composition, resting energy expenditure (REE), fasting blood samples and muscular fitness assessments were examined at baseline and weeks 2, 10 and 14. Results All groups except CON experienced significant reductions ( P < 0.05 – 0.001) in waist circumference over 14 weeks. VLCHP, LCHP and LPHC participants experienced similar but significant ( P < 0.05 – 0.001) reductions in body mass when compared to other groups. Delta responses indicated that fat loss after 14 weeks was significantly greatest in VLCHP (95% CI: -5.2, -3.2 kg), LCMP (-4.0, -1.9 kg) and HCLP (-3.8, -2.1 kg) when compared to other groups. Subsequent reductions in % body fat were significantly greater in VLCHP, LCMP and HCLP participants. Initial dieting decreased ( P < 0.05) relative REE similarly in all groups. All exercise groups significantly ( P < 0.05) improved in muscular fitness, but these improvements were not different among groups. Favorable but non-significant mean changes occurred in lipid panels, glucose and HOMA-IR. Leptin levels decreased ( P < 0.05) in all groups, except for CON, after two weeks of dieting and remained lower throughout the 14 week program. Exercise participation resulted in significant improvements in quality of life and body image. Conclusion Exercise alone (ND) appears to have minimal impact on measured outcomes with positive outcomes apparent when exercise is combined with a hypoenergetic diet. Greater improvements in waist circumference and body composition occurred when carbohydrate is replaced in the diet with protein. Weight loss in all diet groups (VLCHP, LCMP and HCLP) was primarily fat and stimulated improvements in markers of cardiovascular disease risk, body composition, energy expenditure and psychosocial parameters.

The coronavirus COVID-19 has infected millions of Americans. Datasets like the national county-level aggregation of COVID-19 case counts that Johns Hopkins University & Medicine assembled have been widely used, but few analyses have been performed at the local level due to the low supply of data. Like many things American, the distribution of COVID-19 data varies due to differing state, county, and local government reporting policies. The result is a patchwork of COVID-19 data at the local level, mostly aggregated to ZIP Codes due to ease of data processing rather than census tracts which are a better geographical unit for analysis. Local level COVID-19 data are rare and often only available for small areas. In this article, we demonstrate how the U.S. Department of Housing and Urban Development (HUD) Crosswalk Files can be used to assemble a census tract-level dataset of COVID-19 case rates in the Washington, D.C. Metropolitan Statistical Area across multiple states.

As the novel coronavirus disease (COVID-19) continues to infect, harm, and kill thousands of Americans, many jurisdictions and institutions are publishing data at the ZIP Code-level, including counts of tests performed, people infected, hospitalizations, and deaths. These data are leading to quickly produced publications with strong conjectures about the forming of geographic patterns. We present an alternative to ZIP Codes when working with local COVID-19 data.

The Nearest Neighbor Index (NNI) is a spatial statistic that detects geographical patterns of clustered or dispersed event locations. Unless the locations are randomly distributed, the distances of either clustered or dispersed nearest neighbors form a skewed distribution that biases the average nearest neighbor distance used in calculating the NNI. If the clustering or dispersion of locations is moderate to extreme, the NNI can be inaccurate if the skew is substantial. Using Housing Choice Voucher program residential locations, we demonstrate in this article the method to derive an NNI based on a median and two quartiles that more accurately represents the midpoint of a set of nearest neighbor distances. We also demonstrate how to use these alternative point estimates to gauge multiple scales of clustering from different positions across the nearest neighbor distance distribution. Finally, we discuss how to use the average and standard deviation distances from the calculation of each NNI to more comprehensively gauge the scale of the geographic patterns. We also include a Python program that creates a randomized set of locations to calculate statistical significance for the median and quartile NNIs.

The rapidly degrading housing market of the mid-2000s caused local governments to be concerned about the multitude of problems foreclosures could wreak on their jurisdictions (Wilson and Paulsen, 2008). One concern was the escalation of crime and disorder in neighborhoods with concentrated foreclosures. The author illustrates in this article how changing geographic units can produce converse results with an example of foreclosure and crime modeling drawn from Wilson and Behlendorf (2013). He also conducts a spatial analysis to identify which geographic unit is best for modeling foreclosures and crime in the Wilson and Behlendorf (2013) example, using several spatial analysis techniques. Urban policy often targets places, and as such, the spatial extent of those policies should match the geographic coverage area of the problem to be effective in mitigation. Using the wrong geographic unit could lead to policies that do not fully address the problem.