Space-Time Diffusion Visualization using Bayesian Inference

Retail marketing geography has traditionally employed static gravity models for location analytics based on probabilistic locational consumer demand. However, such retail trade area models provide little insight into the dynamic space-time hierarchical diffusionary processes that aggregate to an eventual market structure equilibrium (Mason et. al., 1994), which gravity models attempt to predict for retail trade areas. In addition, most attempts to display the aggregating dynamic space-time hierarchical diffusionary processes of space, time and attributes of interest, in a geographical information system (GIS), produce visualizations that are overly complex and typically displayed utilizing unfamiliar paradigms. Further, these attempts fail to take into account the extensive body of literature in psychology and brain science that stress the importance of perceptual elements and design in achieving optimum visualization comprehension. In other words, simplicity (three-way factor analysis) and visual familiarity (cognitive fit theory (Vessey, 2006), mere-exposure effect in psychology (Dajonc, 1968). This will provide faster perception and better visuospatial and temporal understanding of objects and trends. In this study we incorporate these elements in our visualization object that we refer to as “Avatar”. A Huff inspired, Bayesian framework of inference for spatial allocation and hypothesis testing allows the Avatar object to display the spatial allocation of the Bass model’s innovators and imitators for sales forecasts of new product diffusion (e.g. a mathematical version of Everett Roger’s adoption concept), thus enabling and supporting faster and improved visuospatial understanding of very large data repositories of unbounded and/or “countably infinite” sized geo-big-data (referred to throughout the rest of this paper as GBD). We then introduce the three steps necessary to create an Avatar object (i.e. a 3-D semaphoric, space-time diffusion visualization object). The Avatar object is designed specifically to visualize determinant attributes (e.g. demographics) for the Bass, Bayes, Berry and Huff integrated ensemble model forming part of an ancillary paper to this study. In this way we display the timed hierarchical diffusion of new innovative products throughout store trade areas and across the ensuing and evolving store networks. In addition, by calculating Bayesian conjugate priors and posterior spatial allocation probabilities for the “smallest units of human settlement” (Christaller, 1966) or in our case statistical demographic units (i.e. Census Blocks), we establish customer (innovator and imitator) spatial distributions for the Bass temporal-only model for the case of the aggregating store level trade area (SLTA) scenario. Our approach is empirically supported by five years of new product diffusion geocoded panel data from the Southern California market. We conclude that our cognitive fit theory validated Avatar space-time diffusion visualization strengthens “location analytics” and “location intelligence” and provides a simple and familiar tool for displaying GBD across a growing domain of varying applications and end-user knowledge and needs

Non-selective herbicide applicators for weed control in no-tillage snap beans

A hooded sprayer and a rope-wick applicator were designed and constructed for application of glyphosate or paraquat in the row middles of no-tillage snap beans. The objectives of the study were to design and construct the applicators and to evaluate their effectiveness for interrow weed control. Rows of snap beans received chemical treatment in two parts. Treatments were either preemergence banded or preemergence broadcast with a dinoseb and pendimethalin tank mix immediately after planting. Approximately five weeks later, the row middles received an application of either paraquat or glyphosate from one of the interrow applicators. Weed data were collected from the drill (number and species per 6 linear feet) and in the row middles (number and species per 4 square feet). Treatment means and specific treatment contrasts were determined from the data. The results showed that a one-time preemergence application of a dinoseb + pendimethalin tank mix did not provide adequate weed control throughout the entire growing season. Both interrow applicators were effective for controlling weeds in the row middles of plots treated with the tank mix. However, the hooded applicator provided better control of interrow weeds than the rope-wick applicator. Findings also indicated no difference in the effectiveness of glyphosate and paraquat, applied by the hooded sprayer, for weed control in the row middle

Achieving Green and Healthy Homes and Communities in America

In the Fall of 2010, the National Coalition to End Childhood Lead Poisioning contracted with the National Academy to develop and execute an online dialogue that would examine ways to increase the health, safety, and energy efficiency of low- to moderate-income homes. Since 1999, the National Coalition had worked to improve low- to moderate-income housing through the support and execution of home interventions that addressed multiple issues within a home at one time; an approach that often did not align with other traditional, single-issue housing assistance programs. By 2010, the National Coalition had taken on the leadership of the Green and Healthy Homes Initiative, a public-private partnership focused on integrating funding streams to improve low- to middle-income homes across the country.With plans to expand the GHHI's operations, the National Coalition partnered with the National Academy to conduct the National Dialogue on Green and Healthy Homes, a collaborative online dailogue in which participants were asked to identify challenges to, and innovative practices for, improving the health, safety and energy-efficiency of low- to moderate- income homes. The Dialogue was live from November 4-November 22, 2010, and collected 100 hundred ideas and 362 comments from 320 registered users. Over the course of its two and a half week duration, the Dialogue received more than 2,500 visits from over 1,100 people in 48 states and territories. Key FindingsBy reviewing the feedback received in the Dialogue, the Panel was able to make a number of recommendations on how the green and healthy homes community of practice could increase the health, safety and energy efficiency of homes across the country. These recommendations included: Conduct an evaluation of current housing standards to determine if they meet the Nation's health, safety, and energy efficiency needs; Develop a tiered performance standard for healthy, safe and energy efficient homes; Group government funding streams to better align programs with the comprehensive intervention approach; Develop a long-term funding strategy to support efforts after Recovery Act funding ends; and Educate government decisionmakers and the public on the importance of developing green and healthy homes and communities, and the work that supports that development

Green IS, DSS & CyberGIS: Sustainable Growth and CO2 Reduction?

A nonintuitive, decision support modeling ensemble approach is developed and tested for hypothetical, business-to-consumer (B2C,), innovative product data. The approach demonstrates the potential for gaining competitive advantage by integrating a unique permutation of innovation diffusion theory, sustainability, IS and location. The basic assumption is that early adopters of an innovation “pay more and buy less” i.e., which supports a firm’s sustainable profitable growth while demanding fewer units due to their small proportion among all adopters which translates to reduced CO2 for their purchases. The behavioral rationale for early adopters is derived from Innovation Diffusion Theory. Locating all potential early adopters using the modeling ensemble (i.e., the Bass Bayes Spatial Extension) in a SOM (serviceable and obtainable market) temporally (i.e., quickly) and spatially (i.e., within qualified census blocks) is critical to optimum early adopter maximum target market penetration. The decision support modeling ensemble approach generates expected results subject to limits and delimits, ceteris paribus