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This page provides some brief descriptions of ongoing (and recently completed) research projects, along with some links to related software, papers, and data.

Michigan School Program Information (MiSPI) Project –
The MiSPI project aims to understand how public school administrators acquire and use information to make decisions about which instructional, health, and social skills programs to adopt in their schools. It is funded by the National Institutes of Health and William T. Grant Foundation, and involves interview, survey, and archival data collected from school administrators, intermediary sources of information about school programs, and school program researchers. A key component of the project is a quasi-small world experiment: we ask school administrators where they go for information about school programs, and in subsequent waves continue to trace these information-chains backward. The long-term goal of this project is to develop communication strategies for both school administrators and researchers that makes it easier for these two groups to share useful information with each other. To learn more about the project, visit the project website, watch this short 90-second video, or email the project team at Some of the project's preliminary findings are reported in the following papers:
Neal JW, Neal Z, Mills K, Lawlor J. (2015) Brokering the research-practice gap: A typology. American Journal of Community Psychology 56, 422 – 435. [PUBLISHER]  [SCI-HUB]
Neal Z, Neal JW, Lawlor JA, Mills K. (2015). Small worlds or worlds apart? Using network theory to understand the research-practice gap. Psychosocial Interventions 24, 177 – 184. [PUBLISHER]

Bipartite Projections –
Social network data can be difficult, time consuming, costly, or impractical to collect. Inferring social or economic ties between agents based on co-behaviors can be a useful alternative. For example, we might be able to infer that a social tie exists between people who attent the same events, that a political tie exists between legislators that sponsor the same bills, and that an economic tie exists between cities that host offices of the same firms. These are all examples of projections of two-mode or bipartite networks. I have been working to develop methods to facilitate these inferences by telling us, for example, how many events two people need to co-attend before we should infer that they have a social tie. Several of these methods are available using the Stata -onemode- command, and are described in detail in the following papers:
Neal Z. (In press). Well connected compared to what? Rethinking frames of reference in world city network research. Environment and Planning A. [PUBLISHER]  [SCI-HUB]
Neal Z. (2014). The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance and other co-behaviors. Social Networks 39, 84 – 97. [PUBLISHER]  [SCI-HUB]
Neal Z. (2013). Identifying statistically significant edges in one-mode projections. Social Network Analysis and Mining 3, 915 – 924. [PUBLISHER]  [SCI-HUB]

Urban Networks –
Cities are geographic places, but to really understand how they work, we have to think about them as networks. I view urban networks as existing at multiple scales. At the smallest scale, micro-urban networks refer to the social networks among people living in cities. Looking at these networks can help us understand how neighborhood social networks form and how they are impacted by the surrounding environment. At the middle scale, meso-urban networks refer to the networks that help coordinate urban services and make city life possible. They can include communication links between organizations that help provide social services, but can also include infrastructural ties like city roads and public transit links that move people and goods around. At the largest scale, macro-urban networks refer to the economic networks that exist between entire cities at the national, international, and global levels. Studying these networks can help us understand how processes like economic globalization and global migration unfold and differ from place to place. Most of my urban research is guided by this multi-level framework, which is described in detail in my book The Connected City: How Networks are Shaping the Modern Metropolis.

Networks and Agent-Based Models –
Much of my research has relied in the theories and methods of network science. To better understand how real-world networks form and evolve, I have turned to agent-based simulation models. Although these types of models do not always involve networks, they offer an ideal tool for exploring network dynamics. In a nutshell, agent-based models simulate the simultaneous interaction of agents (they could be people, businesses, animals, etc) with each other and with their surrounding environment. In my models, these interactions include the creation and dissolution of network ties between the agents. I have been particularly interested in combing network science and simulation models to understand how networks form and evolve in urban settings, and how features of those settings like diversity and segregation impact the process. Some recent research that adopts this approach is reported in these papers:
Neal Z. (2015) Making big communities small: Using network science to understand the ecological and behavioral requirements for community social capital. American Journal of Community Psychology 55, 369 – 380. [PUBLISHER]  [SCI-HUB]
Neal Z, Lawlor J. (2015). Agent-based models. Pp. 197 – 206 in Handbook of Methodological Approaches to Community-Based Research: Qualitative, Quantitative, and Mixed Methods, edited by Jason LA & Glenwick DS. New York: Oxford University Press.  [PUBLISHER]  [PDF]  [INTERACTIVE MODELS]
Neal, Z & Neal JW. (2014). The (in)compatibility of diversity and sense of community. American Journal of Community Psychology 53, 1 – 12. [PUBLISHER]  [SCI-HUB]

Air Traffic Network Data –
I have found air traffic one useful way to measure the level and type of interaction between cities. In seeking to make these data more informative, I have developed a method for distinguishing business from leisure passengers in publicly available air transportation statistics for US cities. This method is available using the Stata -airnet- command, but datasets generated using this command can also be downloaded directly: and are comma-delimited files containing route, origin-destination, business, and leisure passenger counts from 1993-2011, aggregated at the level of airports or metropolitan areas. For more information about using these data or about the -airnet- command, please refer to the following papers:
Neal Z. (2014). The devil is in the details: Differences in air traffic networks by scale, species, and season. Social Networks 38, 63 – 73. [PUBLISHER]  [SCI-HUB]
Neal Z. (2014). AIRNET: A program for generating intercity networks. Urban Studies 51, 136 – 152. [PUBLISHER]  [SCI-HUB]
Neal Z. (2013). The AIRNET2000 data and airnet program. Connections, 33, 43 – 45. [PUBLISHER]
Neal Z. (2010). Refining the air traffic approach: An analysis of the US city network. Urban Studies 47: 2195 – 2215. [PUBLISHER]  [SCI-HUB]