Programs and Communities

Hybrid system models - those devised from two or more disparate sub-system models - provide a number of benefits in terms of conceptualization, development, and assessment of dynamical systems. The decomposition approach helps to formulate complex interactions that are otherwise difficult or impractical to express. However, hybrid model development and usage can introduce complexity that emerges from the composition itself.

To improve assurance of model correctness, sub-systems using disparate modeling formalisms must be integrated above and beyond just the data and control level; their composition must have model specification and simulation execution aspects as well. Poly-formalism composition is one approach to composing models in this manner.

This dissertation describes a poly-formalism composition between a Discrete EVent System specification (DEVS) model and a Cellular Automata (CA) model types. These model specifications have been chosen for their broad applicability in important and emerging domains. An agent-environment domain exemplifies the composition approach. The inherent spatial relations within a CA make it well-suited for environmental representations. Similarly, the component-based nature of agents fits well within the hierarchical component structure of DEVS.

This composition employs the use of a third model, called an interaction model, that includes methods for integrating the two model types at a formalism level, at a systems architecture level, and at a model execution level. A prototype framework using DEVS for the agent model and GRASS for the environment has been developed and is described. Furthermore, this dissertation explains how the concepts of this composition approach are being applied to a real-world research project.

This dissertation expands the tool set modelers in computer science and other disciplines have in order to build hybrid system models, and provides an interaction model for an on-going research project. The concepts and models presented in this dissertation demonstrate the feasibility of composition between discrete-event agents and discrete-time cellular automata. Furthermore, it provides concepts and models that may be applied directly, or used by a modeler to devise compositions for other research efforts.

The New Jersey Childhood Obesity Study, funded by the Robert Wood Johnson Foundation, aims to provide vital information for planning, implementing and evaluating interventions aimed at preventing childhood obesity in five ew Jersey municipalities: Camden, Newark, New Brunswick, Trenton, and Vineland. These five communities are being supported by RWJF's New Jersey Partnership for Healthy Kids program to plan and implement policy and environmental change strategies to prevent childhood obesity.

Effective interventions for addressing childhood obesity require community specific information on who is most at risk and on contributing factors that can be addressed through tailored interventions that meet the needs of the community.

Using a comprehensive research study, the Center for State Health Policy at Rutgers University is working collaboratively with the State Program Office for New Jersey Partnership for Healthy Kids and the five communities to address these information needs. The main components of the study include:

• A household survey of 1700 families with 3 -18 year old children

• De-identified heights and weights data from public school districts

• Assessment of the food and physical activity environments using objective data

Data books and maps based on the results of the study are being shared with the community coalitions in the five communities to help them plan their interventions.

The maps in this chartbook describe the food environment in Vineland in terms of access to supermarkets, smaller grocery stores, convenience stores, and limited service restaurants. Research shows that when residents have access to healthy food outlets, they tend to eat healthy.

• Food environment maps were created using geo-coded commercially available data of food outlets (Info USA, 2008 and Trade Dimensions, 2008) in Vineland and in a 1 mile buffer area around Vineland.

•Using the commercial data and additional investigation, food outlets were classified into different categories based on their likelihood of carrying healthy choices: supermarkets carry most healthy choices; smaller grocery stores carry fewer healthy choices; convenience stores and limited service restaurants are likely to carry mostly unhealthy choices.

• Access to different types of food outlets was computed at the census block group level based on concentration of stores / restaurants per unit area and is reported as food outlet densities.

• Food outlet density maps are compared with Census 2000 data to visualize accessibility of healthy foods in neighborhoods with different characteristics.

Data Sources: Info USA food outlet 2008 data

Trade Dimensions food outlet 2008 data

Census 2000 data

New Jersey Department of Education 2008-2009 data

The maps in this chartbook describe the food environment in ewark in terms of access to supermarkets, smaller grocery stores, convenience stores, and limited service restaurants. Research shows that when residents have access to healthy food outlets, they tend to eat healthy.

• Food environment maps were created using geo-coded commercially available data of food outlets (InfoUSA, 2008 and Trade Dimensions, 2008) in Newark and in a 1 mile buffer area around Newark.

•Using the commercial data and additional investigation, food outlets were classified into different categories based on their likelihood of carrying healthy choices: supermarkets carry most healthy choices; smaller grocery stores carry fewer healthy choices; convenience stores and limited service restaurants are likely to carry mostly unhealthy choices.

• Access to different types of food outlets was computed at the census block group level based on concentration of stores / restaurants per unit area and is reported as food outlet densities.

• Food outlet density maps are compared with Census 2000 data to visualize accessibility of healthy foods in neighborhoods with different characteristics.

Data Sources: Info USA food outlet 2008 data

Trade Dimensions food outlet 2008 data

Census 2000 data

New Jersey Department of Education 2008-2009 data