Two similar applications built to compare capabilities/limitations of open source and proprietary dynamic Web mapping APIs

Mining the data

Interactive Visualizations of Conflict Economies

Reza Amindarbari, Makiko Shukunobe, Laura Tateosian1

Center for Geospatial Analytics, NC State1lgtateos@ncsu.edu

SummaryMotivation Conflict economies, such as human trafficking (HT), are sustained by interactions amongst the actors in these markets. Directed graphs (di-graphs) are often used to visualize potential HT data [1][2][3]. Layout and summarization algorithms that relocate nodes, can improve readability of large graphs [4][5], but specialized interactive spatio-temporal visualization and data mining solutions are needed to preserve important geographic context for investigating potential HT data sources.

Approach This project creates 3 geovisualization applications that connect to an interdiction model and display the interpersonal interactions between actors within a potential HT market. First, proprietary and open source geovisualization platforms are compared. Then, coordinated views of spatio-temporal data are designed for exploring pertinent questions.

With a temporal focus, the thirdapplication, use OpenLayers & provides:• Temporal filtering (time-span and time of day), inflow (reviews received) filtering, city

size (population-based) filtering, city selection filtering.• Graphs of filtered data (dual bar chart, city sparklines, and interaction matrix). • Coordinated view map that updates based on filtering.• MongoDB backend for rapid query and on-the-fly aggregation.

Data sourceElasticSearch database of activity logs from a communication technology with potential HT-related activities.

Data mined Location (city) of reviewers and reviewees, and review.

Data processing outputA di-graph of review interactions with origin graph nodes representing reviewers and destinations representing reviewees

AggregationEach graph edge represents one or more interaction with the same origin-destination (orig-dest). Edge weight determined by aggregated interaction count.

Open Source vs. Proprietary

Node Table (georeferenced points)

Edge Table (origin & destination IDs with weights)

Shortest routesMotivation✓ Interdictions often at truck stops on major highways✓ Reduce visual clutter

AlgorithmGet Major US Highway Network dataset, US_hwyCreate empty orig-dest pair list, ODlistfor each orig-dest pair interaction, ab: Set weightab = number of interactions from orig a to dest b Calculate shortest path, SP, on US_hwy for each orig-dest path, pq, on SP that contains no intermediate cities:

if pq exists in ODlist: Set weightpq = weightpq + weightab else: Set weightpq = weightab Insert pq into ODlist

Interactions implemented in both applications:

• Upload edge/node data to display graph over map. Edge color or thickness varies with underlying data (e.g., flow).

• Map zoom/pan; Select edges/nodes to view data attributes; Toggle data layers.

• Modify node/edge attributes; Download modified data as CSV files.

• Send data to a max flow interdiction model; visualize model output as highlighted interdiction layer.

ArcGIS JS API OpenLayers

• Large selection of basemaps • Free and open source

• Off-the-shelf layer toggle & info pop-up • Only 3 basemap options

• Extensive examples & resources • Few documentation examples

• Not free for all

OBJECTID ORI DES Weight5 139 276 14

17 359 197 127 372 276 1

FID OBJECTID LONGITUDE LATITUDE Type Pr1 City State0 1 -123.057 48.9887 S 1 Point Roberts Washington1 2 -122.42 37.7771 S 1 San Francisco California2 3 -120.954 37.6417 S 1 Yosemite California

Spatio-temporal Mapping

JavaScript API for ArcGIS (proprietary)

OpenLayers (open source)

(1) Filtering the interactions

City filter

Inflow/population filter

Temporal filter

In/out flow chart

(2) Analytics (3) Coordinated map view

References[1] Ibanez, M., & Suthers, D. D. (2014). Detection of domestic human trafficking indicators and movement trends using content available on open internet sources. In System Sciences (HICSS), 2014 47th Hawaii International Conference on (pp. 1556-1565). IEEE.[2] Memex (2015). http://deepdive.stanford.edu/showcase/apps[3] Polaris (2016) http://polarisproject.org/sites/default/files/Cantinas-SexTrafficking-EN.pdf [4] Dwyer, T., Riche, N. H., Marriott, K., & Mears, C. (2013). Edge compression techniques for visualization of dense directed graphs. IEEE Transactions on visualization and computer graphics, 19(12), 2596-2605.[5] Herman, I., Melançon, G., & Marshall, M. S. (2000). Graph visualization and navigation in information visualization: A survey. IEEE Transactions on visualization and computer graphics, 6(1), 24-43.

original connections

shortest path re-routed

Interaction volume

Cities …

Modifying the time-of-day filter (below) reveals a different flow pattern late at night (left)vs. in the evening (right).

AcknowledgementsThis material is based upon work supported in whole or in part with funding from the Laboratory for Analytic Sciences (LAS). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the LAS and/or any agency or entity of the United States Government.

Data structure

Interaction heatmap

City sparklines