Idaho Highway Wildlife Mortality A. James Frankman Abstract—Idaho wildlife mortalities on highways and roads is tracked by the Idaho Fish and Game and the data is made available to the general public through an API called IFWIS Core. While the data supplied does offer species information and geographic coordinates, it can be difficult to organize and understand. This paper will attempt to organize and present this data in visual form using Google Maps and Visualizations APIs to show facets of wildlife mortality in Idaho by density of occurance, time of year, and species variety Index Terms—Information Visualization, Idaho Fish and Game, IFWIS Core, road kill, wildlife mortality, Google Visualization API. 1 INTRODUCTION Amongst the rural communities throughout the United States, the only shows 250 of the latest observations, the density of markers on attrition of wildlife by highway collision is a common occurrence. the map make it difficult to distinguish individual incidents. In an effort to better track and understand wildlife collisions occurrences, the Idaho Fish and Game tracks highway collisions that have occurred since 2001. This data can be useful and relevant to several areas of study. First, understanding how and where collisions occur can help prevent traffic accidents. According to the National Highway Traffic Administration 4% of all traffic accidents in the United States are collisions with wildlife[1]. The collisions with wildlife on U.S. roads and highways represent a significant safety concern to motorists. Besides the risks posed to motorists, the affect on wildlife populations is also significant. America’s wildlife is a natural resource, and highway collisions have a negative impact on wildlife populations. In addition, the time and place of wildlife collisions can reveal trends such as common migration routes and wintering areas as well as the diversity of wildlife itself. For instance, one can infer the health or population of from areas with a high occurrence of deer collisions. It can also reveal intrusions of species into areas previously uninhabited, for, if a Gray Wolf is killed on a freeway in an area that previously had been devoid of Gray Wolves, one can easily conclude that Gray Wolves have entered the area. The Idaho Fish and Game offers the public access to wildlife deaths on roads and freeways in two primary ways. First it makes the data available via API for software developers and web authors. Second, it offers a Dashboard application to view the latest 250 incidents. However, this data has several limitations that could be overcome with certain information visualizations. There is no way for a viewer to easily zero in on road kills for a One problem with the data is its overall breadth. There are particular species or time of year. The data markers are not currently over 11000 records of wildlife collisions and when all of distinguished in any way to show the type of incident according to these are displayed on a map the information overwhelms the viewer. species or time. All makers appear identical and the viewer must For instance even from the Idaho Fish and Game dashboard, which click each one to find out the nature of the incident. In addition, the markers on the map are so large it only takes 8 or so to saturate the map canvas. Thus, an area containing about 8 incidents will appear the same as one with dozens more. A final problem is the accessibility of the data to the public at large. While the data is available via API, it takes persons knowledgeable with web programming to write their own web pages and construct their own queries to filter the reported incidents to obtain the view they are interested in. One viewer may be interested in high occurrence areas while others may want to satisfy curiosity and view uncommon species or rare occurrences. 2 SOLUTION AND APPROACH The raw data maintained by the Idaho Fish and Game and made accessible by the Idaho Fish and Game’s IFWIS Core API and website can be effectively organized for in-depth analysis using the common key. For example, data found in an existing fusion table Google Visualization API in combination with Google Fusion Tables containing the names and geometric shapes of US counties could be services and API’s. combined with the wildlife collision data according to County Name. Google Fusion Tables was a primary means of overcoming the 2.1 Google Fusion Tables current limitations with IFWIS Core data as it allowed the data to be Google Fusion Tables is an online service where users can upload, accessed and organized more easily than what was possible through share, and merge data. Data is organized into tabular form with API service calls. Some of the techniques used to accomplish this tables and views similar to how data is organized in a traditional are explained in section 3 of this document, Data. database. However, it can be done so without in-depth technical knowledge by the user. 2.2 Google Visualization API Google Fusion Tables require a Google Account and a basic The Google Visualization API is a tool whereby data can be understanding of structured data. Upon logging on with their Google rendered visually using charts, graphs, maps, timelines, trees, and account, users can upload their own data to create tables or can other visual components. For the purposes of the research covered browse and find publicly available tables shared by other Google by this paper, the API’s map components were very important [3]. Fusion Tables users. Data can be uploaded in various forms At its core, the API does require Javascript and web including comma delimited files (.csv), xml, kml, Microsoft Excel programming. However, many of the API’s features were seamlessly spreadsheets, and Open Document Standard spreadsheets. integrated with Google Fusion Tables. This removed the need for Once data is uploaded, Google Fusion Tables will create a table manual programming to produce many of the visualizations found in based upon the data uploaded and will organize the data into rows this paper. Instead of manual Javascript programming, the API’s and columns. Each column within a table can be classified by type features could be accessed from the Google Fusion Table’s including text, number, Location, etc, as well as format, such as a Visualization menu. From this menu one is able to produce various link or image. Given the data for Idaho Wildlife collisions was geographic, chart, motion, and timeline visualizations. Once a particular visualization is chosen, the user is given even more access to the Visualization API’s features through menus and other web controls. Because of this, non-programmers are able to take advantage of the Google Visualization API without programming experience by using the built in features within the Google Fusion Tables API. 3 DATA As mentioned previously, Idaho wildlife collision data was obtained from the IFWSI Core API service. The data can be downloaded in various forms and types. Since the data needed to be uploaded to Google Fusion tables, data in comma delimited form was the best choice since the csv format takes the least amount of space. The data was downloaded using the IFWIS Core API using a typical http get operation. In the URL the desired format of the data could be specified along with query parameters to direct which records should be returned. Below is an example of the URL used to retrieve records for 2001 wildlife collisions: http://fishandgame.idaho.gov/ifwis/core/view/roadkills/2001.c sv?species=0&start=&end=&highway=0&mpFrom=&mpTo= location based, the included column type of location was particularly &nstart=01/01/2001&nend=12/31/2001&pageSize=2000 useful since it allowed the data in the table to be rendered on a map almost effortlessly. 3.1 Problems within IFWIS data The Google Fusion Tables offers several ways to organize data such as filtering data by certain query parameters and aggregating There were several problems with the IFWIS data in terms of data by column. Probably one of the most useful content as well as accessibility. A short description of each of features of Google Fusion Tables is its merge capability. With this the problems is found below. feature data contained into two separate tables can be combined by a 3.1.1 Accessing Bulk Amounts Odocoileus hemionus rather than the commonly understood The first obstacle came from obtaining IFWIS data came from name of Mule Deer. accessing the data itself. Since the data was exposed via API In order to address this problem, features in Google Fusion it is likely that the intended purpose of querying and accessing Tables were used to include the species common names in the the data was for small interactive sessions rather than bulk fusion tables. To do this, the table was aggregated by species data exports. When specifying a query to download a large name, this operation returned a result that only showed a dataset, timeouts and other errors occur. As a result, the data single record for every distinct species found in the table. needed to be downloaded in smaller chunks. To do this, 11 Next, a new table was created based upon these aggregated queries were run, one for each year from 2001 to 2011. These results having the columns of scientific name and common queries collectively downloaded into 11 separate comma name. The common name for each of the entries in the table delimited files, one for each year. Once all 11 files were where then entered. Once this table was complete, it downloaded, they were combined into a single comma represented a map between scientific names and species delimited file using simple text editing tools (Notepad++).