This document discusses developing an open spatial framework for place-based decision making. It notes the need to integrate spatial effects into decision making processes more effectively. Existing infrastructures have limitations for analyzing complex spatial data and processes. The framework aims to integrate data, analytics, and visualization to allow dynamic exploration and simulation of spatially varying phenomena to inform policy decisions. It will utilize open source tools and be flexible enough to incorporate different data types and scales of analysis over time.

1. An Open Spatial Systems
Framework for Place-Based
Decision-Making
Marynia Kolak, PhD Candidate
PH-GIS | April 14, 2016

2. Background
● A push towards place-based policy
● Need for distilling causal links
● Complex spatial organization & processes
● New types Big Data
● Limitations of existing infrastructures
Chicago Boundaries (Bill Rankin 2009) 2
Streaming
Big
Dynamic
Static
Small
Rigid

3. Absence of an integrated systems
framework means that spatial effects
are not considered or accounted for
effectively or consistently
in a decision-making environment.
3

5. Issues from Ignoring Spatial Effects
Theoretical: may confuse process being studied, miss
important signals, & violate core assumptions.
Methodological: may skew and/or bias results that
impact decision-making.
Technological: analysis and/or dynamic decision-
making may not be feasible, especially in a Big Data
context.
In a policy or decision-
making context, integrating
all three components is
essential for on-the-fly
analysis of spatially
dynamic phenomena.
5

6. Space serves as the
Place for Integration
A spatial framework for distilling
causal links for policy &
intervention assessment
considers a more comprehensive
understanding of how spatial
effects impact the data
generating process.
theoretical
methodological technological
infrastructure
design
research
design
usability
design
SPATIAL
DATA
SCIENCE
for
DECISION-MAKING
6

7. Towards an (Open) Spatial Technological Framework
How can we better integrate data and spatial analysis in a flexible framework?
● Challenges with data sourcing, processing, integration, and ownership
● Need for sharing results in an easy, updateable cross-platform way
● Gap between web development and spatial analytic communities
● Adding not just visualization, but analytics to an open web environment
Desktop Environments: ArcGIS, QGIS, GeoDa, PostGIS
Web Environments: ArcGIS Explorer, Google Products, Policy Map, (GeoDa),
Open, Customizable Web Environments: HTML/CSS, Javascript, (Python), & much more...

8. Open Source Environments
Pros:
Software is free
Code is shared, and can be improved over time (ie.
when bugs emerge, community fixes them)
Project can be customized and improved
Cons:
Steep learning curve: learning or updating
programming skills to make and fix code
If a project doesn’t have a large community, bugs
won’t be fixed (unless you try to)
Open Source projects thrive where there is:
● Community, collaboration, and team environments
● Space, time, and availability to work on improvement
● Phased development and innovation are encouraged
● Flexible, working connection of multiple components

9. Project Overview
+

10. Spatial (Data Science) Decision Support Application
● Built-in Spatial Framework
● Allows for spatially dynamic processes
● Dynamic development & evaluation of place-based policies
Customized Application Goals:
● Asset Mapping - Supports Needs Assessment
● Identify Areas for Treatment - Based on Risk Assessment
● Evaluate Policies & Interventions - Causal Analytic Framework
10
increasing
complexity

11. Healthy Access, Healthy Regions Project
Collaborators: Chicago Department of Public Health (Epidemiology, Policy Analysis, and
Innovation Groups); GeoDa Center, Vader Lab, & ChainBuilder Project at Arizona State
University; Goldstein Research at the Harris School of Public Policy at U of C, and more...
Builds on previous work:
● Spatial Data Warehouse Infrastructure (John Hopkins University 2013)
● West Humboldt Park Resource Mapping (WHPD, OLA, Northwestern, 2013)
● Work with CDPH & multiple community organizations since 2012
● Chicago Food Access Inequity from 2007-2014 (Northwestern-led study, 2016)
● Engineers Without Borders principles & lessons learned (volunteer since 2010)
11

12. Application Goals
CDPH Goal: Evaluate issues of health accessibility across Chicago
Dissertation Goal: Consider spatially dynamic causal links of health access inequity
Phased Development:
● Community Resource Map and Data Exploration (public-facing)
● Health Risk Indicator Map and Data Exploration (public-facing)
● Service Area Network Analysis and Simulation (analyst-facing)
● Data-Driven Regionalization Analysis (analyst-facing)
● Health Accessibility Model Comparison (analyst-facing)

13. Application Objectives
● Visualizes health providers and human services locations by category;
● Calculates service areas for locations along transportation routes;
● Provides descriptive summaries of communities in service areas of key health providers;
● Visualizes distributions of vulnerable and at-risk populations according to demographic and socioeconomic
characteristics;
● Identifies and summarizes areas of need where services are lacking based on demographic and
socioeconomic need, indexed by service category;
● Calculates new targeted zones of risk or vulnerability according to community need and service coverage
attributes; and
● Identifies relationships between the built environment and disease, by category

14. Data
+

15. ● Different types of information stored
● Different ways of storing information
● Sharing data across organizations
● Managing data updates
● Visualizing data on maps
● Shoestring budget for technology
● Limited staff resources
● Domain knowledge with organization
Resource Mapping Challenges
What is the ultimate goal, as
defined by the community?
What technology is available to
(minimally) achieve that goal?
… That meets the needs of the
community?
… Five years from now?

16. ● Visualize and Explore Data
● Dynamic and shareable
● Data could be owned, managed, and
updated by community-based
organizations and other partners
Resource Mapping Needs
Develop a shared community
resource for the community,
by the community.

17. VGI Resource Mapping
Case Example: West Chicago
● Community resources consolidated and
shared across two organizations (West
Humboldt Park Development Council and
Diabetes Link) for the West Side of Chicago
● Initiated while at Northwestern University
● Web map developed by customizing open
source code and published online
● Underrepresented talent recruited to support
development (data, front-end coding)
● makosak.github.io/HumboldtResources
Team Effort!
- West Humboldt Park
Development Council
- Our Lady of Angels
- Northwestern University
- NM Hospital
- GeoDa Center
- Volunteers from GIS
course, Hack Day 2015

21. VGI Resource Mapping
Chicago-wide VGI Framework
● New collaboration with the Chicago
Department of Public Health
● Health providers used by senior planning
analyst uploaded to dynamic Fusion table
● Web map application developed
● makosak.github.io/ChiHealthAccess
Next Steps:
● Determine best method of uploading data in
easy-to-use, effective environment
● Share with CBO network by CDPH
Existing Web Page:
Connects Health Providers
Future Web Page:
Connects Community
Resources from across
Chicago?

22. Show me nearby resources
22http://makosak.github.io/HumboldtResources
flexible
framework
dynamic
spatial data

23. Analysis
+

24. Types of Analysis
● Choropleth maps with multiple Data Classifications
○ Equal Interval, Standard Deviation, Fisher Jenks, CDPH Categories
○ Javascript breaks and/or PySal assignment
● Service Area Network Algorithm
○ Distance- or time-based
○ Open Street Maps roads (PBF file) and Fusion Table points (as JSON)
○ Feng Wang at the Vader Lab
● Hot Spot/ Cold Spot and Outlier detection
○ LISA Statistics and cluster maps
● Max-P Regions and Cluster Analysis
○ Goldstein Research Group open project: “Blobs”

25. Show me 1-mile service areas for hospitals
25http://makosak.github.io/chihealthaccess/
dynamic
exploration
analytic API
integration

29. Simulation Experiment
In Development:
● Run analysis with a model to
develop a quantifiable outcome.
● Add, remove, or change a point.
● Re-run analysis; compare results.
● Repeat as much as you’d like.
Technology:
● Automated Workflows
● Data and Analysis both act as
services (ie. called by URL)
● Chainbuilder, Open ASU Project

30. Which unit should I use for analysis?
30http://makosak.github.io/chihealthaccess/
check
assumptions
spatial
framework for
research design

31. Max-P Algorithm (via “Blobs”)
Max-P is a type of regionalization algorithm:
● Regions are a group of “areas,” and there are
generally multiple regions in a particular dataset.
● Regions are binned according to the similarities in
(multivariate) data
● Can define number of areas that must make up a
region, as well as min number of persons
● In epidemiology: may need similar baseline regions to
not violate core assumptions of inference

33. Systems
Integration
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34. Methods and Tools of Integration
Methods of Analysis:
ESDA (choropleth maps, relative risk maps, Moran’s I, LISA cluster maps, histograms)
Spatial Econometrics, Causal Methodology Toolbox
Tools of Integration (Data, Analytics, Visualization):
PostGresSQL, POSTGIS, python, javascript, html/CSS, Flask, D3, Leaflet, Chainbuilder,
Google Fusion Tables, Google Map API, Plenar.io API
Usability Testing: surveys, task-based interviews, focus groups, & stakeholder meetings
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35. Application Framework
Develop a dynamic and flexible
framework to integrate multiple data
types and formats, allow for scaling, allow
for updates.
Join on space and time in a spatially
enabled data system, rather than object
identifiers, when available.
Privilege services (APIs) and dynamic
updates rather than static datasets.
Allow for service-driven visualization and
analytics in final environment.
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36. Coming Soon...
Timeframe for final completion:
Summer 2017
New datasets, analytics, and resources!
Connecting larger datasets:
- Sensor Networks?
- Social Media?
In July 2016, GeoDa Center moves to the new
Center for Spatial Data Science

37. Acknowledgements
Chicago Department of Public Health
Vader Lab, GeoDa Center, and ChainBuilder at
Agency for Healthcare Research & Quality
GeoDa Fellowship, Arizona State University
Stan Lesny Scholarship, Kosciuszko Foundation
Questions? email mkolak@asu.edu