INTRODUCTION
The prospect of creating a geospatial map depicting relevant data with aesthetically pleasing hexagons, points or nodes, reveals that this generation is receptive to information visualization tools and techniques. Throughout the course, the ability to create tables, graphs and now maps have impacted the way data analysis is shown. Everyone knows this phrase, “a picture is worth a thousand words”. Knowing how to demonstrate and layout any visualization leads to the basic understanding when any type of graphic information is done correctly. The positive results speak for itself whereby people generate a buzz surrounding the map which is filled with pertinent information at their disposal.
The significance of maps portrays the positioning of data within a particular city, region or district. In this case scenario, two different datasets are used to inform the audience about the data analysis based in New York City. First and foremost, both datasets were obtained from the NYC OpenData website: https://nycopendata.socrata.com/data?browseSearch=public+pay&type=&agency=&cat=social+services&scope=. The datasets chosen for this project are as follows:
- NYC Wi-Fi Hotspot Locations
- Public Pay Telephone Locations
Carto, formerly known as CartoDB, is an open source (free) software used for Geographic Information System (GIS) which displays the data on a flat earth surface called a map. The information provided depends on what datasets are used. The map created for this project will answer the following questions:
- What is the purpose of a map?
- Why is it important to know about the Wi-Fi Hotspot locations?
- Why do public pay telephone locations still exist?
Generally speaking, maps answer questions about purpose, location, and the process of determining what elements to include among other tools. Cartographers typically use typography to ensure a detailed description of the city, local or district in which the map focuses on to determine the outcome of the data selected. Having a map that represents the Wi-Fi Hotspots is crucial for anyone seeking access to work on their computers whether it is for business, educational, or recreational purposes. By the same token, in this age of smartphones, public pay telephones are scarce throughout New York City and beyond. In any case scenario, the importance of knowing the whereabouts of pay phone locations is equally important.
VISUALIZATION EXAMPLES
Figure 1
When former Mayor Bloomberg introduced the bike initiative to New York City, his vision for New Yorkers stemmed from adding bicycle routes as a form of mass transit throughout New York City: http://www.wnyc.org/story/284632-nyc-bike-share-maps-re-live/. The bikes serve a purpose by solving the commuting problems and by having yet another form of transportation. This link informs the reader about where to locate a Citi Bike Station. If you hover over the points, the label displays the location with address.
Figure 2
New York City is known for dining, museums, and attractions to name a few. The map of New York City Cruise Boat Locations conveniently labels exactly where each pier is located by indicating the address. The map simplifies the process of locating the pier of the visitor’s choice.
Figure 3
In Figure 3, the choropleth map is described as statistical data for “Time lapse of Crime in NY” according to each New York City’s precincts. Although this map image is static, the map is actually animated and upon pressing the play button on the left-hand side of the map, it automatically updates the information reported for each precinct from the year 2000 through 2015. The choropleth map is designed to show the decline of reported crime for each precinct respectively. The polygon shape draws attention to the color scheme showing the two colors each time the dataset is updated and categorized by precinct — bar chart depicts the last 15 years of statistical data found for the crime reported. Figures 1 through 4 demonstrate the similarities between one another concentrating on map locations.
MATERIALS USED FOR MAPPING
During lab, carto.com was used as the preferred software of choice to create data visualization maps. I created an account to start the mapping adventure. The main features include the use of a basemap of New York City.
To my surprise, locating the datasets for this project was an easy find. I chose two different datasets from the NYC Open Data website. Under the Social Services category both files were at my disposal. The datasets were exported to a spreadsheet in comma-separated value, otherwise known as CSV file. The primary function of a CSV file is to house tabular data in Microsoft Excel or other programs. In addition, the datasets were complete containing the necessary elements, and error free. Finally, the process of downloading the datasets into Carto is the next step.
VISUALIZATION METHODS
Geovisualization techniques are achieved through Carto using interactive techniques. After downloading the datasets from the map dashboard, I selected the basemap of New York City to create a map. Under the style category, I chose the hexagon style to differentiate between the Wi-Fi Hotspots and Public Pay phones dataset. Comparatively speaking, the color scheme must be visible through the viewer’s eye. The green hexagons show the Wi-Fi Hotspots whereas the Public Pay Telephones are shown in red. Hence, using the outliers and cluster feature prevents confusion showing the data points in red to differentiate between the two. In addition, the outlier shows a greater or lesser value than the other data in the set.
Figure 4
Furthermore, the label on the top left-hand corner shows both locations. If you click on the Public Pay Telephone, you will be able to see the general location of Wi-Fi Hotspots in NYC which are the green hexagons. Equally, if you select the geo export, you will find the Public Pay Telephone data points in red. Hence, the Public Pay Telephone location data points are in red. The Wi-Fi Hotspots location are the green hexagons.
NYC Wi-Fi Hotspot locations are naked to the eye. It is primarily located in Downtown Brooklyn, Manhattan, and including Wi-Fi services in NYC Subway stations. Although it is not available in all underground stations, in the near future most of these stations will be equipped with this service. Here is a link for more information about the availability of Wi-Fi in underground stations: http://transitwirelesswifi.com.
With respect to Public Pay Telephone, the data provides free public Wi-Fi locations. As I previously mentioned, due to the minimal amount of public telephones in NYC, it is significant to know the status of location and zoning information. Please refer to this link for more information: http://www.nyc.gov/html/dcp/home.html.
VISUALIZATION RESULTS
Choosing the right data to administer the results of Wi-Fi location is vital since so many people rely on these services. New York City is committed to provide Americans and visitors with the tools necessary to obtain free Wi-Fi services for anyone seeking this information. The future of Wi-Fi is unknown. However, all the hype about Wi-Fi may exceed everyone’s expectations: http://www.forbes.com/sites/parmyolson/2013/02/05/what-happens-if-america-gets-free-nationwide-wifi-google-wins-carriers-lose/#6fcc3543a862. For map enthusiasts, updating results through graphic visualizations such as the map shown above will continue to have a place for interested parties.
FUTURE DIRECTION
Due to time constraints, I would have included an interactive map showing the address location when hovering over each data point. This type of map enables the user to choose the aggregation options with customizing styles. Focusing on this would have made a world of difference; although the map created is simple it is to the point showing the end results with labeling capabilities.
Recently, Carto introduced a wealth of information by designing Carto builder for the layman, making it easier to create a map for non-coders. The Carto platform is user-friendly and meets the needs of anyone interested in creating a map with minimal geospatial skills. In this technological age, I am looking forward to learning more about Carto since I used only a fraction of what Carto can do with location analytics.