Enhancing the Two-Step Floating Catchment Area Model for Community Food Access Mapping: Case of the Supplemental Nutrition Assistance Program

In this article, a Gaussian-based two-step floating catchment area (2SFCA) model is applied to evaluate the geographical accessibility to Supplemental Nutrition Assistance Program (SNAP)-authorized retailers. First, the proposed method considers the interaction between the food supply (in terms of categorized benefit redemptions) and demand (in terms of benefit-receiving households). Second, the model is used to visualize food access patterns at the level of refined administrative units (i.e., census block groups). The developed food access metric was compared to the U.S. Department of Agriculture (USDA) Food Access Research Atlas, justifying the validity of this new method for small area estimation. The following are important observations: (1) the choice of catchment size had a considerable impact on the accessibility measure in urban areas (or when small statistical units are used); (2) the 2SFCA measurement had a higher level of correspondence with that of the USDA Atlas at a smaller catchment size for identifying low food access units; and (3) there was no significant inequality regarding SNAP accessibility with respect to different socioeconomic deprivation variables. This new method can better assist the SNAP administration with store authorization on a refined geographic scale. Key Words: food access, inequality, scale, Supplemental Nutrition Assistance Program (SNAP), two-step floating catchment area (2SFCA).     

Comparing methods for mapping canopy chlorophyll content in a mixed mountain forest using Sentinel-2 data

The Sentinel-2 Multi-Spectral Imager (MSI) has three spectral bands centered at 705, 740, and 783 nm wavelengths that exploit the red-edge information useful for quantifying plant biochemical traits. This sensor configuration is expected to improve the prediction accuracy of vegetation chlorophyll content. In this work, we assessed the performance of several statistical and physical-based methods in retrieving canopy chlorophyll content (CCC) from Sentinel-2 in a heterogeneous mixed mountain forest. Amongst the algorithms presented in the literature, 13 different vegetation indices (VIs), a non-parametric statistical approach, and two radiative transfer models (RTM) were used to assess the CCC prediction accuracy. A field campaign was conducted in July 2017 to collect in situ measurements of CCC in Bavarian forest national park, and the cloud-free Sentinel-2 image was acquired on 13 July 2017. The leave-one-out cross-validation technique was used to compare the VIs and the non-parametric approach. Whereas physical-based methods were calibrated using simulated data and validated using the in situ reference dataset. The statistical-based approaches, such as the modified simple ratio (mSR) vegetation index and the partial least square regression (PLSR) outperformed all other techniques. As such the modified simple ratio (mSR3) (665, 865) gave the lowest cross-validated RMSE of 0.21 g/m² (R² = 0.75). The PLSR resulted in the highest R² of 0.78, and slightly higher RMSE =0.22 g/m² than mSR3. The physical-based approach-INFORM inversion using look-up table resulted in an RMSE =0.31 g/m², and R² = 0.67. Although mapping CCC using these methods revealed similar spatial distribution patterns, over and underestimation of low and high CCC values were observed mainly in the statistical approaches. Further validation using in situ data from different terrestrial ecosystems is imperative for both the statistical and physical-based approaches' effectiveness to quantify CCC before selecting the best operational algorithm to map CCC from Sentinel-2 for long-term terrestrial ecosystems monitoring across the globe.     

用于雷达天文研究的高速基带数据记录系统

为了开展雷达天文科学研究, 将射电望远镜接收的雷达回波信号进行采集和记录, 研究基于SNAP (Smart Network ADC (Analog to Digital Converter) Processor)硬件实验板和快速存储服务器设计并开发了雷达天文基带数据采集与记录系统. 该系统采用CASPER (Collaboration for Astronomy Signal Processing and Electronics Research)提供的图形化FPGA (Field Programmable Gate Array)开发工具流, 设计了双通道、256MHz带宽信号采集和VDIF (VLBI (Very Long Baseline Interferometry) Data Interchange Format)基带数据输出固件程序; 基于HASHPIPE (High Availability SHared PIPeline Engine)多线程管理引擎开发了双万兆以太网口实时基带数据存储程序, 存储带宽达到1GB/s; 最后编写了VDIF格式到雷达天文格式的转换程序. 经过脉冲星信号观测实验检测, 该系统准确、可靠.     

Variation in low food access areas due to data source inaccuracies

Several spatial measures of community food access identifying so called “food deserts” have been developed based on geospatial information and commercially-available, secondary data listings of food retail outlets. It is not known how data inaccuracies influence the designation of Census tracts as areas of low access. This study replicated the U.S. Department of Agriculture Economic Research Service (USDA ERS) food desert measure and the Centers for Disease Control and Prevention (CDC) non-healthier food retail tract measure in two secondary data sources (InfoUSA and Dun & Bradstreet) and reference data from an eight-county field census covering 169 Census tracts in South Carolina. For the USDA ERS food deserts measure accuracy statistics for secondary data sources were 94% concordance, 50–65% sensitivity, and 60–64% positive predictive value (PPV). Based on the CDC non-healthier food retail tracts both secondary data demonstrated 88–91% concordance, 80–86% sensitivity and 78–82% PPV. While inaccuracies in secondary data sources used to identify low food access areas may be acceptable for large-scale surveillance, verification with field work is advisable for local community efforts aimed at identifying and improving food access.