Case Study: Autonomous Robotic Food Waste Pickup Pilot

DOI: 10.61152/OGSX1745

Series: CASE STUDIES

Author: Hannah Ranieri

Publisher: Orange Sparkle Ball

Original Publication Date: September 5th, 2024

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Orange Sparkle Ball was awarded a Real World Deployment Grant from the Michigan Economic Development Corporation's Office of Future Mobility and Electrification. This grant is enabling us to put our Autonomous Robotic Pickup Platform into action through a series of pilots in the Transportation Innovation Zone of Detroit, established by the Office of Mobility Innovation at The City of Detroit.

Orange Sparkle Ball

Ottonomy

Brother Nature Produce

Scrap Soils

NCNA

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Abstract

Case Study: Autonomous Food WastePickup Pilot

In June 2024, Orange Sparkle Ball launched a pilot testing the autonomous robotic pickup of food waste for composting in partnership with Ottonomy, a leading ground robotics company, and Scrap Soils, a local Detroit composting non-profit, with support from Brother Nature Produce and the North Corktown Neighborhood Association. Utilizing cutting-edge technology, autonomous ground robots navigated designated routes in the North Corktown neighborhood of Detroit to pick up food waste for composting.

This pilot was successfully completed in July 2024. Orange Sparkle Ball generated a case study detailing the goals of the pilot, pickup process, KPIs, qualitative outcomes, survey results, issues and resolutions, and key learnings.

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Case study

We generated this document detailing the goals of the pilot, pickup process, KPIs, qualitative outcomes, survey results, issues and resolutions, and key learnings.

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PILOT VIDEO

We documented the pilot operations to share the pickup process, reception of the technology by the community, and outcomes of the pilot, including key learnings and metrics.

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PILOT AT A GLANCE

We generated this document with a high-level overview of the goals of the pilot, key metrics, community engagement, key takeaways, and pilot outputs.

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Contact Hannah Ranieri to receive the full case study PDF.

Exploring Social Care Network Structures

https://doi.org/10.61152/HDNZ4028

Jasmine Fernandez1, Michaela Bonnett1, Teri Garstka2 and Meaghan Kennedy1

1Orange Sparkle Ball, 2Social Innovation Labs, The University of Kansas

Series: Sunbelt 2024

Original Publication Date: June 25th, 2024

Publisher: Orange Sparkle Ball

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Abstract

Exploring Social Care Network Structures

This research is grounded in the theory that scale-free networks form between many organizations in a community when coordinating social care services and influential hubs in the network emerge (Barabási & Réka, 1999).We explore the variability in the structures of social care networks, focusing on how the diverse needs of community members and the array of providers influence these structures. We posit that the architecture of these networks may hold the key to discerning patterns in community health and social outcomes.

Our study examines the resilience of social care networks, defining them as systems designed to enhance interactions among all nodes to meet diverse community needs. We discuss community as a network and community resilience as a process, introducing three key properties—scale-free, small world, and hubness/information spreading scores, for understanding network resilience.

We analyzed 20 social care networks, which have been active over an 18-month period using the referral technology tool to send and receive service referrals, providing raw interaction data among organizational nodes. We focused on two primary objectives: 1) Social care networks are more likely to exhibit scale-free properties and contain influential hubs; and 2) There is significant variability among social care networks in terms of scale-free properties and centrality measures.

Using the three properties—small world, scale-free, and hubness/information spreading scores—we classified the 20 social care networks into different structural profiles. We analyzed node,edge radius, diameter, to understand the network structure characteristics. Our findings highlighted four distinct network structures, which we ranked from most to least resilient. We discussed the implications of these structures on community-level outcomes, including the potential centralized vulnerability when hubs and information spreaders overlap, creating efficiency during normal operations but also increasing vulnerability to disruptions.

Our findings offer insights into the emergent properties of complex systems, particularly in networks intentionally designed to enhance resilience and meet diverse community needs. We conclude by discussing the variability in centrality and structural metrics within the identified groups and propose future research directions to explore the long-term impact of these network structures.

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Citation List

Csárdi, G., Nepusz, T., Müller, K., Horvát, S., Traag, V., Zanini, F., & Noom, D. (2024). igraph for R: R interface of the igraph library for graph theory and network analysis (v2.0.2) [Computer software]. [object Object]. https://doi.org/10.5281/ZENODO.7682609

Doyle, J. C., Alderson, D. L., Li, L., Low, S., Roughan, M., Shalunov, S., Tanaka, R., & Willinger, W. (2005). The “robust yet fragile” nature of the Internet. Proceedings of the National Academy of Sciences, 102(41), 14497–14502. https://doi.org/10.1073/pnas.0501426102

Ercal, G., & Matta, J. (2013). Resilience Notions for Scale-free Networks. Procedia Computer Science, 20, 510–515. https://doi.org/10.1016/j.procs.2013.09.311

Fitzpatrick, T. (2016). 3—Community Disaster Resilience. In B. W. Clements & J. A. P. Casani (Eds.), Disasters and Public Health (Second Edition) (Second Edition, pp. 57–85). Butterworth-Heinemann. https://doi.org/10.1016/B978-0-12-801980-1.00003-9

Norris, F. H., Stevens, S. P., Pfefferbaum, B., Wyche, K. F., & Pfefferbaum, R. L. (2008). Community Resilience as a Metaphor, Theory, Set of Capacities, and Strategy for Disaster Readiness. American Journal of Community Psychology, 41(1–2), 127–150. https://doi.org/10.1007/s10464-007-9156-6

O’Kelly, M. (2014). Network Hub Structure and Resilience. Networks and Spatial Economics, 15. https://doi.org/10.1007/s11067-014-9267-1

Salavaty, A., Ramialison, M., & Currie, P. D. (2020). Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nod

A Model for Technology-Enabled Community Resilience

https://doi.org/10.61152/PLCR9111

Meaghan Kennedy1, Michaela Bonnett1, and Teri Garstka2

1Orange Sparkle Ball, 2Social Innovation Labs, The University of Kansas

Series: Sunbelt 2024

Original Publication Date: June 25th, 2024

Publisher: Orange Sparkle Ball

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Abstract

A Model for Technology-Enabled Community Resilience

Introduction
Tech-Enabled Community Resilience is an innovative model designed to enhance resilience and optimize impact in complex systems such as communities and ecosystems. The model leverages social network analysis and technology to visualize network dynamics, measure interactions, and implement targeted interventions.
Model Structure
The approach consists of two key stages: a Startup Phase focused on assembling champions and co-creating a shared vision, and a Steady-state Phase involving iterative measurement and intervention. By utilizing technology platforms for data collection and visualization, the model provides near real-time understanding of network functioning.
Advantages Over Traditional Approaches
Traditional resource mapping approaches provide a limited understanding of the network based on a static understanding of resources and a lack of complexity about network function. The Tech-Enabled Community Resilience model provides for a more dynamic, systems-thinking perspective. The model allows for precision interventions based on network structure, potentially influencing community-level outcomes.
Case Studies and Research Findings
Case studies from social care networks and economic development initiatives demonstrate the model's applicability across various contexts. Research findings linking network cohesion to improved community outcomes during crises, and network structure to increased innovation in ecosystems, underscore the model's potential impact.
Future Directions
Further model refinement includes the development of a portfolio of network-based interventions, integration of real-time data sources, and strategies for adaptive governance structures. This model represents a significant advancement in how to understand and harness complex systems for community resilience and impact optimization.

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Citation List

Bonnett, M., Ladetto, A., Kennedy, M., Fernandez, J., & Garstka, T. (2024, June 25). Network Analysis of a Mobility Ecosystem in Detroit, MI [Conference Talk]. Sunbelt, Edinburgh, Scotland. https://www.insna.org/events/sunbelt-2024---edinburgh##

Cantner, U., & Graf, H. (2006). The network of innovators in Jena: An application of social network analysis. Research Policy, 35(4), 463–480. https://doi.org/10.1016/j.respol.2006.01.002

Garstka, T. (2024, June 25). The Relationship Between Community Networks and Population-Level Outcomes [Conference Talk]. Sunbelt, Edinburgh, Scotland. https://www.insna.org/events/sunbelt-2024---edinburgh##

Garstka, T., & Kennedy, M. (2023, May). Tech-Enabled Community Resilience [Conference Presentation]. Good Tech Fest, Washington, DC. https://www.goodtechfest.com/

Gartska, T., Kennedy, M., & Bonnett, M. (2024, September). Tech-Enabled Community Resilience: Research on the Transformative Role of Inclusive Social Care Ecosystems. International Social Innovation Research Conference (ISIRC), Bern, Switzerland. https://www.nunify.com/events/isir1

Isada, F. (2021). The Partnership Network Structure of Automakers under Radical Technological Change. Business Systems Research Journal, 12(2), 95–113. https://doi.org/10.2478/bsrj-2021-0021

Norris, F. H., & Stevens, S. P. (2007). Community Resilience and the Principles of Mass Trauma Intervention. Psychiatry, 70(4), 320–328. https://doi.org/10.1521/psyc.2007.70.4.320

Norris, F. H., Stevens, S. P., Pfefferbaum, B., Wyche, K. F., & Pfefferbaum, R. L. (2008). Community Resilience as a Metaphor, Theory, Set of Capacities, and Strategy for Disaster Readiness. American Journal of Community Psychology, 41(1–2), 127–150. https://doi.org/10.1007/s10464-007-9156-6

Network Analysis of a Mobility Ecosystem in Detroit, MI

https://doi.org/10.61152/HEJW8941

Michaela Bonnett1, Meaghan Kennedy1, Angela Ladetto2, Jasmine Fernandez1 and Teri Garstka3

1Orange Sparkle Ball, 2Detroit Regional Partnership, 3Social Innovation Labs, The University of Kansas

Series: Sunbelt 2024

Original Publication Date: June 25th, 2024

Publisher: Orange Sparkle Ball

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Abstract

Network Analysis of a Mobility Ecosystem in Detroit, MI

Background
As part of a new initiative from the Global Epicenter of Mobility (GEM), organizations across many sectors in Detroit, MI, and surrounding counties are collaboratively investing in transforming the local legacy mobility industry into an inclusive advanced mobility cluster over the next 3 years. At the start of this initiative, in partnership with the research team at the Detroit Regional Partnership, a social network analysis was conducted to map the relationship between the foundational 24 organizations, the greater coalition, and their extended network to date. The organizations within this initiative were divided into 4 sectors that highlighted key differences in engagement This baseline map and relationship data, as well as key network analysis metrics, will be compared to future data collections over the coming years to track the initiative’s progress.

Methods
The original coalition (161 organizations) was identified by the local partner organization and data collection proceeded from September-December 2023 through survey completion. One or more representatives of coalition organizations were asked to identify their relationship to other members of the coalition using a 1-5 scale (Frey et al., 2006). Data were analyzed in R, and organization-level metrics, as well as centralized network-wide metrics, were produced for weighted betweenness, degree, and weighted degree centrality, as well as averages of connection strength. Maps were produced using KUMU software.

Findings
The mobility coalition consisted of 159 nodes and 7412 connections. Of those connections, 3763 (50.77%) had at least a level 1 connection strength, while 2319 (31.29%) had a connection strength of ≧ 3 (an active working relationship). The average connection strength for the network was 2.13. The coalition network was highly interconnected, with a clustering coefficient of 0.70 and a density of 0.59. Nonprofit and foundation organizations made up 47.5-50% of the top quartile by all centrality metrics while only making up 32.1% of the network. Corporate and private organizations made up 42.8% of the network and made up 68.42-82.50% of the bottom quartile across all metrics. The distribution of centrality scores of the corporate and private organizations was significantly lower than those of all other sectors within the network.

These results illuminate a network that is highly interconnected, but in which not all sectors are engaging equally. These results are being used to plan and implement strategic interventions to foster new relationships and growth within the network. In addition to the 159 coalition organizations, respondents to the survey identified an additional 244 organizations as active participants within the Detroit region mobility space. A select number of these organizations will be added to the coalition as it becomes established within the Detroit region. These provide directions for future growth of the GEM initiative and the mobility ecosystem network and are examples of turning research into action.

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Citation List

D’Aveni, R. A., Gunther, R. E., & Cole, J. (2001). Strategic Supremacy: How Industry Leaders Create Growth, Wealth, and Power Through Spheres of Influence. Simon and Schuster.

Frey, B. B., Lohmeier, J. H., Lee, S. W., & Tollefson, N. (2006). Measuring Collaboration Among Grant Partners. American Journal of Evaluation, 27(3), 383–392. https://doi.org/10.1177/1098214006290356

Garstka, T., & Kennedy, M. (2023a, September). Convening of Mapping Our Social Fabric: The Tech-Enabled Community Resilience Summit [Virtual Summit]. Mapping Our Social Fabric: The Tech-Enabled Community Resilience Summit, Online. https://www.tecresilience.com/

Garstka, T., & Kennedy, M. (2023b, October). Establishing a Responsive and Inclusive Governance Structure for Multi-Sector Technology and Data Collaborations [Conference Presentation]. US Census Bureau Data Innovation Summit, San Juan, Puerto Rico. https://datasymposium23.splashthat.com/

Greenwald, H. P., & Zukoski, A. P. (2018). Assessing Collaboration: Alternative Measures and Issues for Evaluation. American Journal of Evaluation, 39(3), 322–335. https://doi.org/10.1177/1098214017743813

Malatesta, D., & Smith, C. R. (2014). Lessons from Resource Dependence Theory for Contemporary Public and Nonprofit Management. Public Administration Review, 74(1), 14–25. https://doi.org/10.1111/puar.12181

Norris, F. H., & Stevens, S. P. (2007). Community Resilience and the Principles of Mass Trauma Intervention. Psychiatry, 70(4), 320–328. https://doi.org/10.1521/psyc.2007.70.4.320

Santos, F. M., & Eisenhardt, K. M. (2005). Organizational Boundaries and Theories of Organization. Organization Science, 16(5), 491–508. https://doi.org/10.1287/orsc.1050.0152

Vargo, S. L., & Lusch, R. F. (2016). Institutions and axioms: An extension and update of service-dominant logic. Journal of the Academy of Marketing Science, 44(1), 5–23. https://doi.org/10.1007/s11747-015-0456-3

Precision Public Health: Empowering Communities with Hyperlocal Data for Targeted Interventions and Improved Outcomes

https://doi.org/10.61152/SKTQ6431

Michaela Bonnett1, Meaghan Kennedy1, and Odiraa Okala1, Teri Garstka2

1Orange Sparkle Ball, 2Social Innovation Labs, The University of Kansas

Series: Georgia public health association 2024

Article #2

Original Publication Date: May 3rd, 2024

Publisher: Orange Sparkle Ball

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Abstract

Background
Precision public health is an effective strategy for reaching the last mile in service delivery, but is frequently hampered by its dependence on unattainable data standards and the non-transferability of the solutions designed. This paper proposes a five-part system involving 1) dynamic data governance, 2) hyperlocal community data, 3) data synthesis and analysis, 4) the design and implementation of precision interventions, and 5) correlation between community data and traditional outcome data. Recent studies of community network data have found the connectedness of communities to be positively correlated with community social and environmental outcomes. Taking advantage of hyperlocal community data is therefore a promising approach to improve community outcomes by characterizing and optimizing for greater connectivity.
Methods
Collection and governance of hyper-local data that is community-owned can be accomplished through such transferable systems as IRIS, a community-led referral network originally designed for multi-sector social and healthcare organizations. Using this data, communities can identify precise areas of intervention through descriptive and network analysis techniques, and design a responsive, community-led intervention. Immersive Innovation Labs, an applied learning approach, is an effective methodology for the adaptive design of innovative precision interventions. This combination of approaches can empower communities and public health professionals.
Conclusion
The COVID-19 pandemic revealed the impact of chronic understaffing and skills gaps, particularly at the local level. This paper aims to broaden the definition of precision public health as a response, beyond the traditional application that is dependent on big, non-contextual data sources. Reframing precision public health to a methodology dependent on community-owned, ongoing data collection allows the design of hyper-local solutions while shifting the burden of scalability to data collection technology. While challenges in implementation remain, precision is necessary to make public health and communities more responsive and effective in delivering equitable health outcomes and reaching the last mile.

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Citation List

Canfell, O. J., Davidson, K., Woods, L., Sullivan, C., Cocoros, N. M., Klompas, M., Zambarano, B., Eakin, E., Littlewood, R., & Burton-Jones, A. (2022). Precision Public Health for Non-communicable Diseases: An Emerging Strategic Roadmap and Multinational Use Cases. Frontiers in Public Health, 10, 854525. https://doi.org/10.3389/fpubh.2022.854525

Garstka, T., & Kennedy, M. (2023, October). Establishing a Responsive and Inclusive Governance Structure for Multi-Sector Technology and Data Collaborations [Conference Presentation]. US Census Bureau Data Innovation Summit, San Juan, Puerto Rico. https://datasymposium23.splashthat.com/

Rasmussen, S. A., Khoury, M. J., & del Rio, C. (2020). Precision Public Health as a Key Tool in the COVID-19 Response. JAMA, 324(10), 933–934. https://doi.org/10.1001/jama.2020.14992

University of Kansas. (2024). Adapting to Meet Emerging Needs | Connect with IRIS. https://connectwithiris.org/knowledgebase/adapting-meet-emerging-needs

van Staa, T.-P., Goldacre, B., Buchan, I., & Smeeth, L. (2016). Big health data: The need to earn public trust. BMJ: British Medical Journal, 354. https://www.jstor.org/stable/26946408

A Cross-Sectoral Approach to Innovation in Public Health

https://doi.org/10.61152/HBTW2644

Michaela Bonnett1, Meaghan Kennedy1, Sophie Becker1, and Odiraa Okala1

1Orange Sparkle Ball

Series: Georgia public health association 2024

Article #1

Original Publication Date: May 2nd, 2024

Publisher: Orange Sparkle Ball

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Abstract

Brief Description
Innovative methods need to be integrated into public health thinking to accelerate change, a belief that led to the creation of our public health innovation cohorts. 11 participants were taught new ways of approaching problems and how to test with pilots. Thematic and network analysis of interviews, along with a 2-year follow-up, revealed an enduring shift in approach to problem-solving within their work.
Background
Public health tends to be siloed and reactive in solving new and existing problems. Innovation and the development of novel approaches requires cross-sectoral collaboration and thinking. The intentional development of new ideas and actionable programs is key to responding to emerging challenges. Immersive Innovation Labs is a guided approach to innovation focused on multidisciplinary collaboration and the creation of actionable solutions.
Methods
11 public health professionals and 12 student coaches participated in a 10-day Public Health Innovation Summit. Collaborative learning sessions, research, and guided innovation sessions were accompanied by interviews to gauge participant’s knowledge about and attitudes toward developing and implementing innovative solutions within the public health field. Interviews were analyzed for common themes using AI and influential concepts were identified using network analysis software. Follow-up interviews were conducted with participants and coaches 2 years later to determine the impact that the innovation training had on their careers and approach to problem-solving.
Results
Participants of the summit developed 11 pilot programs. 28 interviews and a post-summit feedback session revealed a shift in topical focus by betweenness centrality. Prior to the summit, participants focused on public health programming and actions (top 43% most influential nodes). After completing the innovation summit, participants shifted focus away from public health (top 6% most influential nodes) to the impact these techniques would have in their workplace, the creative approaches to problem-solving, and the new connections made during the summit.
Conclusion
Immersive Innovative Labs is an effective methodology for reframing the approach public health professionals have to solve new and existing problems. Guided coaching and cross-sectoral collaboration lead to innovation, producing new approaches and ideas. This technique has been effective in shaping mindsets beyond the session and has meaningfully impacted the careers of participants.

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Citation List

Bevc, C. A., H. Retrum, J., & M. Varda, D. (2015). New Perspectives on the “Silo Effect”: Initial Comparisons of Network Structures Across Public Health Collaboratives. American Journal of Public Health, 105(S2), S230–S235. https://doi.org/10.2105/AJPH.2014.302256

Consoli, D. (2007). Services and systemic innovation: A cross-sectoral analysis. Journal of Institutional Economics, 3(1), 71–89. https://doi.org/10.1017/S1744137406000567

Isada, F. (2021). The Partnership Network Structure of Automakers under Radical Technological Change. Business Systems Research Journal, 12(2), 95–113. https://doi.org/10.2478/bsrj-2021-0021

Immersive Innovation

https://doi.org/10.61152/RJPF2928

Orange Sparkle Ball

Immersive Innovation

Version 2

Original Publication Date: March 29th, 2024

Publisher: Orange Sparkle Ball

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Abstract

At Orange Sparkle Ball, we believe that innovation should be accessible to all and can be used across disciplines to effect change. From our multi-sectoral work, we noticed varying frameworks that are all doing similar things, but wanted to create a hybrid framework highlighting the best of each and with more general language. From that came the Immersive Innovation framework. It’s a 6-step process that takes a large challenge down to a use case and testable pilot that can be built out into a larger rolled-out and sustained solution. Offered in both English and Spanish, the Immersive Innovation book is a comprehensive look into the process.

Driving Innovation: New Technologies in Private and Public Sector Mobility

https://doi.org/10.61152/LNJB7379

Odiraa Okala1

1Orange Sparkle Ball

SERIES: THE MOBILITY SERIES

ARTICLE #4

Original Publication Date: August 10th, 2023

Publisher: Orange Sparkle Ball

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Abstract

In the penultimate post of the series, Orange Sparkle Ball talks about the last mile challenge in the public and private sectors. We also talk about the dangers of over reliance on technology to solve those issues.

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Citation List

Bhattacharjee, Dilip, et al. “Navigating the Labor Mismatch in US Logistics and Supply Chains.” McKinsey & Company, 10 Dec. 2021, www.mckinsey.com/capabilities/operations/our-insights/navigating-the-labor-mismatch-in-us-logistics-and-supply-chains.

“Biden-Harris Administration Provides $759 Million to Bring High-Speed Internet Access to Communities Across Rural America.” USDA, 27 Oct. 2022, https://www.usda.gov/media/press-releases/2022/10/27/biden-harris-administration-provides-759-million-bring-high-speed. Accessed 9 Aug. 2023.

Dalton , Andrew, and The Associated Press . “Writers Strike: Why A.I. Is Such a Hot-Button Issue in Hollywood’s Labor Battle with SAG-AFTRA .” Fortune , 24 July 2023, https://fortune.com/2023/07/24/sag-aftra-writers-strike-explained-artificial-intelligence/. Accessed 9 Aug. 2023.

Ferguson, Stephanie. “Understanding America’s Labor Shortage: The Most Impacted Industries.” U.S. Chamber of Commerce, 12 July 2023, www.uschamber.com/workforce/understanding-americas-labor-shortage-the-most-impacted-industries.

“The Global Supply Chain Crisis - What’s behind the Shortages?” Liberty Mutual Business Insurance, 31 Aug. 2022, business.libertymutual.com/insights/the-global-supply-chain-crisis-whats-behind-the-shortages/#domino-effect-of-rising-costs.

Hernandez , Daisy, and Manasee Wagh . “These 19 Items Are in Short Supply Due to COVID-Related Supply Chain Issues.” Popular Mechanics , 23 Sept. 2022, https://www.popularmechanics.com/culture/g38674719/covid-shortages/. Accessed 9 Aug. 2023.

Martin, Bradley. “Supply Chain Disruptions: The Risks and Consequences.” RAND Corporation, 15 Nov. 2021, www.rand.org/blog/2021/11/supply-chain-disruptions-the-risks-and-consequences.html.

McDonald, Sean Martin. “Technology Theatre.” Centre for International Governance Innovation, 13 July 2020, www.cigionline.org/articles/technology-theatre/.

Rouse, Margaret. “Last Mile Technology.” Techopedia, 16 Nov. 2021, www.techopedia.com/definition/26195/last-mile-technology.

Tully , Catarina. “Public Sector Innovation Has a ‘First Mile’ Problem.” Apolitical, 18 Dec. 2022, apolitical.co/solution-articles/en/public-sector-innovation-has-a-first-mile-problem.

West, Darrell M. “Six Ways to Improve Global Supply Chains.” Brookings, 12 July 2022, www.brookings.edu/articles/six-ways-to-improve-global-supply-chains/.

Closing the Gap: Reaching the Last Mile

https://doi.org/10.61152/QHQY2184

Odiraa Okala1, Meaghan Kennedy1

1Orange Sparkle Ball

SERIES: THE MOBILITY SERIES

ARTICLE #3

Original Publication Date: August 4th, 2023

Publisher: Orange Sparkle Ball

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Abstract

In the third installment of our mobility series, we take a look at the last mile issue and how Orange Sparkle Ball is working to address it.

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Citation List

Franke , Lena Sophie. “The Famous ‘Last Mile’ Problem Explained.” MoreThanDigital, 26 Jan. 2021, morethandigital.info/en/the-famous-last-mile-problem-explained/.

Hayes, Adam. “Last Mile: What It Means in Reaching Customers.” Investopedia, 7 Dec. 2022, www.investopedia.com/terms/l/lastmile.asp.

Nesterak, Evan. “How Do We Solve the Last Mile?” Behavioral Scientist, 11 Dec. 2022, behavioralscientist.org/how-do-we-solve-the-last-mile/.

The Power of Connectivity: Exploring the Role of Mobility Infrastructure

https://doi.org/10.61152/DTUI5808

Odiraa Okala1

1Orange Sparkle Ball

SERIES: The mobility series

ARTICLE #2

Original Publication Date: July 18th, 2023

Publisher: Orange Sparkle Ball

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Abstract

In the second post of the series, we talk about mobility infrastructure, network theory, and how developing and understanding mobility infrastructure can create change.

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Citation List

Evans, T. S., & Chen, B. (2022). Linking the network centrality measures closeness and degree. Communications Physics, 5, 1–11. https://doi.org/10.1038/s42005-022-00949-5

Max Planck Institute for Social Anthropology . (n.d.). Mobility Infrastructure. Max Planck Institut für ethnologische Forschung. https://www.eth.mpg.de/molab-inventory/linked-and-distributed-mobilities

Unlocking the Nexus of Mobility: Introducing the Vast Ecosystem of Mobility

https://doi.org/10.61152/PSVN8525

Odiraa Okala1

1Orange Sparkle Ball

SERIES: The mobility series

ARTICLE #1

Original Publication Date: June 27th, 2023

Publisher: Orange Sparkle Ball

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Abstract

In the first blog post of the series, we examine mobility across various dimensions, discuss our expansive view of the term, and consider how network theory can help inform the way we approach our efforts to improve physical, social, and economic mobility.

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Citation List

Britannica, T. Editors of Encyclopaedia (2022, February 7). social mobility. Encyclopedia Britannica. https://www.britannica.com/topic/social-mobility

Chetty, R., Jackson, M., Kuchler, T., Stroebel, J., Hendren, N., Fluegge, R., Gong, S., González, F., Grondin, A., Jacob, M., Johnston, D., Koenen, M., Laguna-Muggenburg, E., Mudekereza, F., Rutter, T., Thor, N., Townsend, W., Zhang, R., Bailey, M., … Wernerfelt, N. (2022). Social

Capital I: Measurement and associations with economic mobility. Nature , 608, 108–121. https://doi.org/10.3386/w30313

Gugushvili, A. (2022). Information about inequality of opportunity increases downward mobility perceptions: A population-wide randomized survey experiment. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.868303

Math insight. Edge definition - Math Insight. (n.d.). https://mathinsight.org/definition/network_edge

Oxford University Press. (2005). mobility . The New Oxford American Dictionary.

Owen-Smith , J. (2016). Network Theory: The Basics . http://www-personal.umich.edu/~jdos/papers/2016/Network_theory.pdf

The Pew Charitable Trusts. (n.d.). Economic mobility. The Pew Charitable Trusts. https://www.pewtrusts.org/en/topics/economic-mobility

Richard V. Reeves, B. D., Sarah Nzau, C. B., Sawhill, I. V., Andre M. Perry, J. R., Beyond Deng, T.

W., & Morley Winograd, M. H. (2022, August 3). Seven key takeaways from Chetty’s new research on Friendship and Economic Mobility. Brookings. https://www.brookings.edu/articles/7-key-takeaways-from-chettys-new-research-on-friendship-and-economic-mobility/

Using Social Network Analysis to Link Community Health and Network Strength

https://doi.org/10.61152/SCSF6662

Michaela Bonnett1, Chimdi Ezeigwe1, Meaghan Kennedy1, and Teri Garstka2

1Orange Sparkle Ball, 2Center for Public Partnerships and Research, The University of Kansas

Series: Tech-Enabled Community Resilience

Article #2

Original Publication Date: July 14th, 2023

Publisher: Orange Sparkle Ball

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Abstract

Social network analysis (SNA) is a technique used to analyze social networks, whether it be composed of people, organizations, physical locations, or objects. It is being increasingly applied across a variety of sectors to gain insight into patterns of behavior and connectivity, the flow of information and behaviors, and to track and predict the effectiveness of interventions or programs. A key area associated with network strength using SNA is the health and wellness of individuals and communities. Both network strength and health and wellness are measured in many ways, which can obfuscate the association, so more consistency and further research is required. Despite this, the existing research using SNA to link characteristics of social networks to health and wellness find that stronger, more connected networks tend to be associated with better health outcomes. These results also present opportunities and insights for effective program implementation in response to disasters, to increase resilience, and to improve outcomes for individuals and communities.

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Citation List

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Linking Community Resilience to Health and Wellness

https://doi.org/10.61152/PVTK9816

Natalie Vaziri1, Michaela Bonnett1, Meaghan Kennedy1, and Teri Garstka2

1Orange Sparkle Ball, 2Center for Public Partnerships and Research, The University of Kansas

Series: Tech-Enabled Community Resilience

Article #1

Original Publication Date: July 14th, 2023

Publisher: Orange Sparkle Ball

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Abstract

Community Resilience (CR) is a topic on many people’s minds these days, and represents a community’s and an individual’s ability to weather adversity, as well as to adapt and recover. It also represents a community’s strength and readiness to respond to changes and capitalize on opportunities. Adaptation and recovery are intrinsically linked to the health and wellness of a community or individual, and measuring the link between CR and a community’s health is a point of key importance. Community resilience is complex, so scholars and stakeholders have developed a variety of models and metrics to measure and identify it. Many of these are linked to health and wellness outcomes within the community, providing a foundation for the link between the resilience of a community and the health of the people. Further research is required as the nature of CR is better defined, but current results provide support for using the measurement of CR to identify key points of intervention to improve the health and wellbeing of communities.

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