Healthy Regions + Policies Lab

Healthy Regions & Policies Lab

Summer Fellowship Program

 

The Healthy Regions & Policies Lab at the Center for Spatial Data Science is hiring multiple full- and/or part-time Summer Fellows for University of Chicago students (undergraduate or graduate) over the summer of 2021. Fellows will meet with project teams for weekly check-ins, and/or as needed. In addition, fellows will meet every other week for a HeRoP Summer Program Seminar where they will discuss work with other summer interns/RA/staff, and take turns in presenting ongoing work. Fellows will work remotely from June 21st – August 13th. Rates are $15-19/hr depending on experience, in accordance with university protocol. Please review the following position descriptions, and submit your application with your FT/PT preference, CV/resume, and work sample here. Deadline is June 1, 2021 or until the position is filled.

 

Data Engineer Fellow: Spatial Infrastructures 

The US COVID Atlas project seeks assistance in engineering and optimizing a data solution for transferring and utilizing time-series geospatial data. The project diverges from traditional web maps that use backend processing and data serving in favor of analytics in the browser. Analysis of data includes spatial autocorrelation and binning techniques that require all data members be present, so tiled or chunked data is less attractive, and highly performant time-series exploration requires fast access across columns. Current data have roughly 3,200 counties observed and up 450 days (columns) of data for multiple datasets (CDC, New York Times, etc.) and multiple variables (cases, deaths, testing, vaccination, etc.). Data loading/transfer is increasingly a challenge, as CSV based transfer and parsing slow with greater data volumes, and in-memory analytics are becoming an additional concern. CSDS / Healthy Regions and Policies Lab seeks a research assistant with a background in data management and engineering, particularly with an interest in compression, binary data types, frontend data and state management and in-memory analytics. Work may extend and be part of an effort to create accessible geospatial dashboard templates with low-cost server requirements, aimed at increasing the potential for web mapping and geospatial analysis without reliance on established services.

Required Skills: 

  • Proficiency with SQL and Python or Node.js
  • Familiarity REST APIs and HTTP requests
  • Knowledge of data transfer file formats (CSV, JSON)
  • Understanding of Lambda functions and serverless infrastructure

Preferred Skills: 

  • Background with geospatial data and geoprocessing/computation
  • Familiarity with Http/2 and/or Websockets
  • Understanding of binary data formats for transfer (eg. ProtoBuffer File), storage (eg. Apache Parquet), and in-memory analytics (eg. Apache Arrow)

 

Data Scientist Fellow: Social-Spatial Networks 

The Research Assistant will work on a project that examines how social networks and social geography affect HIV and HCV risks among young persons who inject drugs from suburban and rural areas. The project will be funded through a NIH project that collaborates with researchers from the University of Illinois at Chicago. The Research Assistant will provide technical assistance with cleaning, reshaping, summarizing, and analyzing social network and spatial datasets (such as kernel density estimate analysis), as well as generating analytical insights and developing manuscripts with other project staff and contributors as a coauthor. Coursework in quantitative methods and familiarity with basic concepts in spatial and social network analysis is required for this position. Familiarity with R (or STATA) and experience with spatial analytic and/or social network analysis is strongly preferred. 

Required Skills: 

  • Knowledge of quantitative research methods;
  • Familiarity with concepts in spatial and social network analysis;
  • Data analysis experience in R (preferably tidyverse) or STATA;
  • Excellent organizational skills and attention to details;
  • Ability to establish priorities and work independently;
  • Strong interest in public health applications; 

Preferred Skills: 

  • Spatial analytic and geocomputation experience in R (preferably sf, tmap, and adehabitat);  
  • Experience with social network analysis in R;
  • Knowledge of Github.

 

Data Scientist Fellow: Built Environments 

How does the built environment (BE) intersect the opioid epidemic in varying, nuanced ways? In this project funded by the NIH, the HeRoP team works with collaborators to investigate intersections of opioid use disorder and the neighborhood environment in New Jersey. The team has compiled an expansive data warehouse of dozens of characteristics corresponding to various social, economic, policy, and environmental domains, and is developing both concept- and data-driven indices to approximate built environment realities for further validation and analysis. This summer, we seek a research assistant to extend, refine, and evaluate BE index/indices; identify associations with overdose and fentanyl-specific hotspots over time; decompose how the dimensions of access are associated with the BE and overdose outcomes; and distill how the BE, access, and health outcomes change across different regions (urban/suburban/rural). A familiarity with (spatially explicit) multivariate clustering and dimension-reducing techniques, regionalization algorithms, and exploratory spatial data analysis is expected. The research assistant will support model implementation, analyses, literature review, and manuscript writing as a co-author. Interest or experience in working with risk environment frameworks (as a conceptual model for HIV or persons who inject drugs-PWID), application of the Thomas & Penchansky model of access, and integrating an intersectional public health framework, is preferred.

Required Skills: 

  • Knowledge of quantitative research methods;
  • R/tidyverse and basic mapping in R (sf, tmap);
  • Demonstrated experience with GeoDa;
  • Excellent writing skills;
  • Knowledge of multivariate clustering and dimension-reduction techniques; spatial clustering analyses; regionalization algorithms;
  • Interest and demonstrated experience in integrating conceptually driven thinking with quantitative analyses.

Preferred Skills: 

  • Familiarity with Github;
  • Familiarity with Zotero;
  • Excellent editing skills;
  • Interest and/or experience in the following conceptual frameworks: risk environment (Rhodes); accessibility (Thomas & Penchansky); intersectionality (Crenshaw).

 

Data Scientist Fellow: Measuring Accessibility 

Access to medications for opioid use disorder providers (MOUD) is a critical component of reducing opioid overdose deaths, though the various dimensions and complexities of access remain understudied. This research assistant will work with HeRoP lab scientists to refine spatial access measures to MOUD resources across the United States; integrate other dimensions of access including structural social, economic, and built environment factors; experiment with the development of access indices; identify associations of access across various regions with dimensions of social vulnerability and health outcomes; decompose results with a focus on populations disportionately impacted by criminal justice settings; and support the implementation of a spatially extended sensitivity analysis to estimate and identify the “best” metric(s) of access for policy-makers and researchers. The research assistant may conduct statistical analyses in R, exploratory spatial data analysis in GeoDa, assist in developing tables and figures for publication, support the development of a research brief to communicate results to a more general audience, conduct a literature review of MOUD accessibility, and contribute to the manuscript development as a study co-author. This position is funded through an award by the National Institutes of Justice (NIH) via the Methodology and Advanced Analytics Resource Center (MAARC), to support NIH’s Justice Community Opioid Innovation Network (JCOIN), which seeks to improve opioid addiction treatment in criminal justice settings. 

Required Skills: 

  • R/tidyverse preferred, (Py/pandas ok);
  • Demonstrated experience with GeoDa;
  • Knowledge of quantitative research methods;
  • Excellent writing skills;
  • Interest in policy applications.

Preferred Skills: 

  • Familiarity with Github;
  • Familiarity with Zotero;
  • Spatial packages in R (sf, tmap) (or Py/pysal);
  • Graphic design sensibility;
  • Excellent editing skills;
  • Interest in quasi-experimental causal inference methods (in a counterfactual framework) welcome for research design planning of the next stage of research.

 

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