Proposal Topic & Site Functionality

Public Health and Urban Planning have a long history, and we hope to demonstrate the synergies between these two fields through innovative uses of information technology. The Centers of Disease Control and Prevention (CDC) provides some highlights on this intertwined history (source: http://www.cdc.gov/mmwr/preview/mmwrhtml/su5502a12.htm):

During the 19th and early 20th centuries, the synergies between urban planning and public health were evident in at least three areas: creation of green space to promote physical activity, social integration, and better mental health; prevention of infectious diseases through community infrastructure, such as drinking water and sewage systems; and protection of persons from hazardous industrial exposures and injury risks through land-use and zoning ordinances. During the middle of the 20th century, the disciplines drifted apart, to a certain extent because of their success in limiting health and safety risks caused by inappropriate mixing of land uses.

The disciplines recently have begun to reintegrate. During the last 20 years, shared concerns have included transportation planning to improve air quality, encourage physical activity, prevent injuries, and promote wellness. In addition, some original crossover ideas, such as the potential for parks and recreational facilities to contribute to physical activity and mental health, have reemerged. Relatively recently, urban planning has focused on the effects of community design on energy use and greenhouse gas emissions to affect the growing public health concern of climate change. Finally, emergency preparedness (e.g., community infrastructure assurance, evacuation planning) and access to health care (e.g., assurance of accessibility and adequacy of facilities) are topics important to both disciplines.

Our project intends to employ spatial statistical techniques to analyze influenza data provided by the CDC. We hope this information will be useful to urban planners, geographers, and public health professionals.

(2) Functionality

Users will be able to select CDC data (and possibly other data, time permitting), which will be hosted on Google Fusion Tables. After selecting the data, the user will choose one of a number of statistical techniques to be performed on the data. We will first attempt to provide measures of central tendency, and if successful, we will provide more advanced statistics. The statistical operations will be executed in the browser, and will render the resulting images on a map. In addition to the statistics, the CDC features offers several RSS feeds (e.g. podcasts, influenza updates), which will feature on the website and provide real time information.

Project Datasets and Sources

Project Datasets/Data Sources

The mission of the CDC Flu App Challenge is to promote healthy behavior for flu prevention by making it easier to communicate critical information about the flu and its impact.  In order to raise awareness about influenza and/or to educate consumers on ways to prevent and treat it, our website will utilize several data sources.  Specially, data will be obtained directly from the CDC, general health sources and general population sources.  Technological applications will include several mapping and visualization tools to help promote best practices for influenza health and risk communication.

Mapping/Visualization APIs

Google Maps API: http://code.google.com/apis/maps/documentation/javascript/basics.html

Google Fusion Tables API: http://code.google.com/apis/fusiontables/docs/sample_code.html#ftl

Google Visualization API: http://code.google.com/apis/visualization/documentation/gallery.htm

Google Charts API: http://code.google.com/apis/chart/docs/making_charts.html
CDC Flu Data

CDC contest datasets:

Influenza Vaccination Estimates
http://www.cdc.gov/flu/professionals/vaccination/reporti1011/resources/2010-11_Coverage.xls
XML source of the Weekly Flu Activity Report
http://www.cdc.gov/flu/weekly/flureport.xml
RSS Feed of Influenza pages through content syndication
http://t.cdc.gov/feed.aspx?tpc=26829&days=90
RSS Feed of Influenza updates
http://www2c.cdc.gov/podcasts/createrss.asp?t=r&c=20
RSS Feed of Influenza podcasts
http://www2c.cdc.gov/podcasts/searchandcreaterss.asp?topic=flu
RSS Feed of CDC Features pages through content syndication
http://t.cdc.gov/feed.aspx?tpc=26816&fromdate=1/1/2011
JSON Feed of Influenza pages through content syndication
http://t.cdc.gov/feed.aspx?tpc=26829&days=90&fmt=json
JSON Feed of CDC Features pages through content syndication
http://t.cdc.gov/feed.aspx?tpc=26816&fromdate=1/1/2011&fmt=json

CDC Seasonal Flu Activity: http://www.cdc.gov/flu/weekly/fluactivitysurv.htm
Health Data

Health Indicator Warehouse API: http://www.healthindicators.gov/Developers/Overview

Medline Plus API: http://www.nlm.nih.gov/medlineplus/webservices.html

Health Data Interactive: http://www.cdc.gov/nchs/hdi.htm

Distribute: http://isdsdistribute.org/

WHO FluNet: http://www.who.int/csr/disease/influenza/influenzanetwork/flunet/en/
Other Population Data (demographics, geographic boundaries, etc.)
U.S. Census TIGER/Line: http://www.census.gov/geo/www/tiger/
School information: http://www.greatschools.org/api/registration.page

Good and Bad Health Data Example Sites

http://www.google.org/flutrends/

http://healthmap.org/swineflu/ (here is link to with information about the site http://healthmap.org/about/)

http://www.healthindicators.gov/Indicators/Flu-vaccination-adults_119/California_6/Profile/Data

http://www.cdph.ca.gov/data/statistics/Pages/H1N1Data.aspx

http://www.cdc.gov/flu/weekly/

Updated Project Proposal

Revised Project Description
FNR Consulting is in the process of designing a website that integrates business locations surrounding LACMTA bus and rail stops with Metro’s Trip Planner. By combining data from existing sources—Metro’s Trip Planner API, Google Maps, and Walkscore—our website will aid transit riders in trip planning and help connect users to businesses near transit. It will also hopefully encourage people who don’t currently use transit to try it by making trip planning more accessible.

Functionalities
FNR’s website will allow users to map the pedestrian shed surrounding LACMTA bus stops and rail stations, then search for and map specific types of businesses within the ped shed. It will draw its base layer from the Google Maps API, use the LACMTA’s trip planning and GIS Developer files to map transit routes, and use business location information from Walkscore. As shown in the accompanying storyboard, users will be able to search for transit lines based on origin and destination. Based on the line(s) chosen for a particular trip, our product will generate a pedestrian shed using information from ESRI ArcGIS Network Analyst, then display businesses located in or immediately around that pedestrian shed.

Datasets Used
Metro LA API
Walkscore API
Google Maps API

Milestones
Midterm
*Trip planning functionality
*Show air radius walking distance from stops

Final
*Integrate true walkable area using Network Analyst
*Display business & other attractions via Walkscore

Concerns
*Matching up the true walkable area with the simple radius used by Walkscore

Storyboard

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

GoM-Website Proposal

GoM is a website that targets at college students in Metro service area in LA County. Wigh GoM, people can search and communicate Dining, Housing and Recreational information in  Metro service area.

Compared with other website providing similar information, GoM particularly targets at college students. Therefore, GoM is well organized to better serve people who are not rich and more public-transit dependent. And we provide some functions like commuting time estimation from dorm to school and Metro recreational information to attract target users(for detailed information please see our wireframe). And they would find GoM a really helpful trip planner for those without a car.

GoM uses LA Metro API and Yelp API to realize trip planning and dining searching information. And for recreational information and housing searching function, GoM uses Google spreadsheet to realize it.

This wireframe shows detailed information about how functions are organized and what exactly GoM can provide for its users.

Here is some sketchy storyboard for GoM.

Storyboard: GoM_Storyboard

GoM-Wireframe

We suppose to provide 3 main functions for GoM customers. Firstly they can use GoM to search nearby restaurant and add comment to existing restaurants which could be seen by other GoM customers. With GoM they can conveniently find which Metro lines go there and when next bus comes. To realize this function we suppose to use Yelp API and Google spreadsheet (for final). However, because we don’t know how powerful is Yelp API. So we haven’t decided how to design the searching panel.

Secondly, we suppose to provide Housing information for our target customer, college students. Firstly, we find all college in Metro service area and then set up a Google spreadsheet for people to share information. Students can use GoM to post new housing information and searching housing near school. GoM provide users bus line information from every living site to colleges and estimate the commuting time helping students to find houses fitting their requirements.

Last but not least, we provide college students recreational information. By setting up a Google spreadsheet and updating it regularly, students can find latest recreational information on GoM. And they can use GoM to plan their trips to these sites. Also, students could update recreational information on GoM too. GoM users can communicate their feelings of each performance by adding comment on it.

That’s all we can think of now and all contents are subject to change.

Website Proposal

Proposal

We propose an exciting web mapping product.  Primarily using the Google Maps API, we will develop a platform that allows users to determine potential food destinations that they can access within a given time budget, such as a short lunch break.  More specifically, we will combine the functions of two classes of existing websites: (1) those that map the locations and provide customer reviews of restaurants (such as Yelp) or food trucks (Foodtruckr.com/, Foodtrucksmap.com), and (2) those that provide real-time travel directions (such as Google Maps) or potential destinations that can be reached by bike and transit within a given amount of time (Mapnificent).  We anticipate incorporating data feeds from a variety of sources:

The following schematic explains our concept visually:

The following storyboard walks you through the user experience:

Milestones

By Week 6 we expect to set up the basics.  We will build a code to bring together the multiple API feeds, and limit them to the appropriate geographic bounds.  We also expect to demonstrate how to parse LA Metro real-time bus arrival data for a specific bus route.  Finally, we would like to begin exploring the travel distance functions.

We will build incrementally, beginning with smaller sub-tasks such as:

  • Calculate a simple walking/biking radius
  • Calculate a more sophisticated walking/biking service area using available routes
  • Incorporate terrain impedance for walking/biking service area (potentially using ArcMap)
  • Display all relevant bus route near a given location
  • Use LA Metro API to determine real-time bus arrival times for these routes
  • Calculate wait and travel times given the time budget

We expect the more challenging task to be designing the user interface.  We have not yet learned the necessary skills to allows users to input unique data into the website.  The second major task will be to calculate travel times, and the areas one can access within a given travel budget, from the LA Metro real-time transit data, the USGS terrain data, and more traditional Google Maps directions (see concerns below).  These tasks will be completed by Week 10.

Looking Forward

Our main concern is the availability of data that we can use, and familiarizing ourselves with these different sources.  We know how to use Metro’s API, but have not yet worked with the others.  We plan to use Twitter feeds to map locations of food trucks, similar to the Sendai Twitter Map we were shown during Week 1, so we will need to learn how to parse the Twitter data for specific food trucks.  Also, we noticed in our research that the Twitter feeds often give addresses in slang, e.g., “Trojan turf”, so we need to figure out how to manage these data.

The other major concern involves the user interface.  Our website will require users to input travel time budgets, preferred mode, etc., and so we will need to learn how to gather that user input.  How do we create text input boxes?  How do users submit responses, and how do we process them in the code?