Search

Demographic and socio-economic information provided by the American Community Survey (ACS) have been increasingly relied upon in many planning and decision making contexts due to its timely and current estimates. However, ACS estimates are well known to be subject to larger sampling errors with a much smaller sample size compared with the decennial census data. To support the assessment of the reliability of ACS estimates, the US Census Bureau publishes a margin of error at the 90% confidence level alongside each estimate. While data error or uncertainty in ACS estimates has been widely acknowledged, little has been done to devise methods accounting for such error or uncertainty. This article focuses on addressing ACS data uncertainty issues in choropleth mapping, one of the most widely used methods to visually explore spatial distributions of demographic and socio-economic data. A new classification method is developed to explicitly integrate errors of estimation in the assessment of within-class variation and the associated groupings. The proposed method is applied to mapping the 2009–2013 ACS estimates of median household income at various scales. Results are compared with those generated using existing classification methods to demonstrate the effectiveness of the new classification scheme. [ABSTRACT FROM AUTHOR]

Choropleth maps visualize areal geographical data by grouping data into a few map classes and assigning different colors, shades, or patterns. Recent studies show that data uncertainty, commonly observed in real-life applications, should also be accounted for when determining the best classification scheme. Due to data uncertainty, a few studies note that map units might be placed in a wrong class, and the concept of map robustness has been introduced to minimize such misplacement. Recently, an algorithm has been developed to integrate robustness into the design of the optimal map classification scheme. However, the existing algorithm has two limitations: first, it is only suitable for certain robustness metrics. Second, when identifying the optimal class breaks, the existing algorithm requires predefined candidate class break values, which might lead to sub-optimal solutions. This paper resolves these issues by proposing a new model, namely, the Continuous Robust Map Classification Problem (CRMCP), and the associated solution approach. The CRMCP allows mapmakers to customize robustness metrics according to their data and applications. In addition, a particle swarm optimization algorithm is developed to solve the CRMCP. The model and algorithm are tested using American Community Survey data. Test results suggest that the new approach can find better solutions than the existing algorithm. The study improves the usability of choropleth maps when uncertain geographical attributes are involved and allows spatial analysts and decision-makers to incorporate robustness into the mapmaking process more flexibly. • Robustness is a new perspective in determining the class break values for choropleth maps. • CRMCP allows flexible robustness definition and continuous class break values for mapping uncertain geographical attributes. • A particle swarm optimization algorithm is developed to solve the CRMCP model. • The proposed algorithm outperform the existing ones in terms of solution quality, efficiency, and versatility. [ABSTRACT FROM AUTHOR]

Geoforensics leverages various spatial analytical, biological, ecological, and geological techniques to improve criminal investigations, including efforts to thwart terrorism, evaluate humanitarian crises or identify the origin of fraudulent goods. In addition to investigating what took place, geoforensic efforts often focus on determining where and when the event occurred. Forensic palynology is a promising subfield of geoforensics that uses pollen to link persons or objects to particular places and times. This promise is driven by pollen's strong utility as a biomarker. It is ubiquitous, durable and demonstrates a relatively predictable distribution in time and space. As a result, pollen often provides important clues for determining the provenance of hard-to-trace items, such as computers, fraudulent goods, digging tools, clothing, and undetonated explosives during geoforensic investigations. One major limitation that has reduced the implementation of forensic palynology is the lack of computational tools that relate pollen species to geographic locations. The purpose of this paper is to introduce a robust geocomputational framework that uses pollen sample composition to identify the relative likelihood of potential origin locations for improving geoforensic efforts. Using USDA CropScape data for the state of Texas, our results suggest that this framework allowed for multiple potential origin sites to be identified simultaneously, with solution properties that were better than a random process and offer a possible alternative to the single site joint probability approaches commonly used in the field of forensic palynology. • Pollen is small, durable and ubiquitous – making it an excellent biomarker for geoforensics • Pollen can be linked to specific places at specific times • We present geocomputational framework that uses pollen to identify potential origin locations for objects of interest • GOFIND offers an alternative to the single site, joint probability approaches commonly used in forensic palynology. [ABSTRACT FROM AUTHOR]