Tag Archives: smart phones

Geospatial Student Spotlight: Matthew Ward

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Academic Institution:

Hunter College                                                          New York City
Department of Geography and Environmental Science
M.S. Geoinformatics (June 2021)

Virginia Tech                                                             Blacksburg, VA
School of Public and International Affairs
B.S. Environmental Policy and Planning   (May 2010)

Research Focus:

Ward’s graduate research at Hunter College focuses on the expanding use of Augmented Reality (AR) in the geospatial disciplines  with a  particular  interest  in  supporting civic  and public engagment.   AR is an interactive experience of the real-world environment where the objects that reside in the real world are enhanced by computer-generated content and perceptual information.   His research inspired by spatial AR research and development work at companies such as Google and  ESRI.

Ward’s work was recently highlighted and included  in the 2022 NYC OpenData  Week which included  augmenting street level imagery with the NYC PLUTO database which offers over 70 data variables at the tax parcel level.  Built on the WebXR standard, the web app leverages the THREE.js graphics library with files and front-end hosted on Github and the data on a Heroku  Postgres cloud platform.  The code repository and web app is available on Github. A short demo of the application can be seen here.

Cubes represent PLUTO centroids along the street face.   The magenta cube is the one currently selected by the user and provides current PLUTO information of the selected parcel in the upper left hand corner of the screen.  Cyan cubes represent other available PLUTO centroids for query. 

Ward’s smart phone app is now available (iOS and Android) here for entire NYC footprint.    Additional documentation and instructions are available on his GitHub repository.  

Selected Additional Geospatial Projects

Gerrymandering Web Map

Ward performed a series of analyses on the proposed plans by the New York State Independent Redistricting Commission (NYRIC) based on data related to the 2020 census.  Because the Commission couldn’t agree on a single draft plan, the bipartisan members drafted two plans.  Two of the analyses are shown below (References to the “Names” (Republican)”vs “Addresses” (Democrat) Plans are from the New York State Senate District plans submitted by the NYIRC.

The image above (from a web map) is a test of “roundness” (aka the Roeck test [1961]).   In general, rounder districts will have a higher Roeck score and oddly shaped or districts that are wider or longer (darker shaped districts) will receive lower scores. Although not a perfect assessment of gerrymandering, the Roeck test offers good comparison between district shapes. 

Another test done by Ward was comparing the average voter party tilt of a district to its neighboring districts (darker purple districts are more unlike their neighbors).  Metro NYC shows what could be considered large areas of voter homogeneity.

An map of an additional analysis is included with Ward’s paper (available here) summarizing this research work and findings.  The third review looked at identifying those proposed senate districts which had at least 30% minority group population (African-American, Asian, and Hispanic/Latino populations were used). Such districts are almost entirely located in New York City, with some inclusion of parts of Nassau county.  The research paper includes a comprehensive list of all data used in the gerrymandering analysis. A “live”  interactive version of the web mapping app can be accessed here.  The three different analysis for each plan (Names and Letters) can be viewed by clicking the layers button in the upper right hand corner of the viewing window.  (Designed for desktop viewers not smartphones.)

 Crime Modeling Across NYC

Ward’s graduate coursework also included developing a crime view modeling viewer (below) based on NYC OpenData reported crimes for 2019. Selected violent crime types were analyzed.  He developed an even square grid to cover the citywide footprint so as to render the spatio-temporal nature of the data.   Crimes that occur are tracked in space and time within those squares.  Data is associated with corresponding police precinct building addresses (how the data is made available through OpenData – not necessarily the exact crime location).

The data is for all of 2019 but each day of the model (365 days total) is rendering the three weeks of crime leading up to that day to predict ‘likelihood’ of crime going forward. Darker purple is an application “indicator” of where more violent crimes may occur based on the historical data.  Yellow grids meaning less likely than purple.  No color means no spatio-temporal connections in crimes or no crimes.  Click here to run the video.

Summary:

The Hunter College Masters in Geoinformatics (MSGEOi) focuses on the growing demand for emerging professionals  trained in the collection, organization, analysis, and dissemination of geospatial data.  Matthew Ward’s graduate work is illustrative of this as it intersects developing analytical methods and visualizations of large geospatial datasets. 

After school, Ward is interested in applying his work and research in the public or government space.  “I see the great power of combining open source technologies and open data portals for empowering local users with AR technology, he notes.  “For example, there are so many types of uses in helping the public visualize utilities and the public infrastructure or in environmental applications.”  

Augmented reality is no doubt a rapidly expanding and growing technology in the geospatial arena.

Contact:

Matthew Ward
Graduate Student
Hunter College, City University of New York (CUNY)
Department of Geography and Environmental Science
MATTHEW.WARD14@myhunter.cuny.edu

Dr. Sean Ahearn
Professor & Director, Center for Advanced Research of Spatial Information (CARSI)
Department of Geography and Environmental Science
sahearn@hunter.cuny.edu

Using Smart Phone Metrics for Carrier Network Design

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I really did start a conversation with Brian Webster at Wireless Mapping regarding an article for eSpatiallyNewYork.  It was my intent to begin a discussion on the use of geospatial technology in the  telecommunications and wireless engineering space.  Turns out the discussion ended up aspiring Brian to author a detailed article for his own blog which provides much more detail clarity than I could ever produce.   Which makes it easy for me – I’m going to send you to his post.

An initial focus of his company was in mapping of broadband internet availability. Using GIS technology, the company provides clients with maps showing broadband internet availability down to the census block level on a local, statewide or national basis.  These analysis are further quantified by the type of services available, number of occupied households in each census block, and market penetration rate. For those businesses shifting  to an internet based marketing program such as social networks, knowledge of where the broadband customers are located is a key factor to future successful marketing campaigns.

His more recent work focuses on a paradigm shift of designing carrier networks based on mobile metrics generated from smart phones vs. traditional fixed metrics such as demographics and/or traffic counts tied to specific roadways, among others.  Today’s smart phone applications gather significant location based information which can be compiled and used in such a way to be able to show high and low “user” traffic areas. Taking this information and converting it to easily understandable formats such as heat maps, it is easier to visualize society’s mobility, where the heavy traffic areas are, where people work, and where they spend their free time. Smart phones use show the more heavily used portions of existing mobile carrier networks and moving forward such data can be used to aid in planning new carrier networks – particularly in the emerging 5G arena.


This series of maps, generated by Wireless Mapping, show cell phone data for the greater New York metro area (nearly 10 years of data through December 2017) changing in granularity when looking at more focused regions such as the five boroughs and then just Manhattan.  The smallest level of detail show where network capacity needs to be addressed and will be the areas where carriers will be targeting first for 5G deployments.

For a more detailed discussion on the use of smart phone data use and GIS and their potential use in designing the 5G carrier networks, visit Brian’s July 12th post.  You can also follow his other work in areas of radio frequency (RF) system design, business analytics, mapping, data mining, telecommunications and wireless engineering  by using links available as part of the blog post.