ABSTRACT:

The U.S. Customs and Border Protection (CBP) Encounter Data provides records of migrant encounters in the U.S. specifically towards its border and ports of entry. This data provides fiscal years from 2020 to the present (2024). In specifically looking at the legal authorities by Title 8 and Title 42 sections of the U.S. code that categorizes all permanent federal laws. Title 8 covers laws concerning immigration and nationality and Title 42 discusses laws related to welfare and public health.

The cbp_resp.csv contains information upon the field sectors, mainly the areas of responsibility, mostly for areas in the regional/national level. Additionally, the cbp_state.csv focuses on state level data for CBP encounters where it’s by U.S. state.

OVERVIEW:

DATA FROM NATIONWIDE ENCOUNTERS BY AREA OF RESPONSIBILITY

Description:

The description of “CBP Encounters Over Time by Land Border Regions” demonstrates a relationship between the past 4 years (2020 through 2024) and the number of encounters based on the three land border regions: Northern Land Border, Southwest Land Border, and Other.

This visualization is effective in showing that the Southwest Land Border has been the most prominent with CBP encounters over the past few years, as well, in connection to the Northern Land Border, the distinctions among the number of encounters are noticeable. The use of colors are neutral and colorblind friendly and are distinct enough to visualize each bar for each year. Additionally, the creation of a simple bar plot helps creates attention to the bars and keeping the y axis lines keeps a general form of the values for each region. Plotly was used in order to use the hover function to demonstrate the year and the total number of encounters, as well, in the legend if the user presses any of the variables, then it will not show up in the plot, which helps with users who are interested in evaluating further relationships with the data. Overall, it’s an effective and simple visualization for evaluating CBP encounters over time within Land Border Regions. This tells a story with the data because such narratives are displayed news outlets and politicians, which oftentimes are biased. From using a reliable source and being able to create a plot that doesn’t attempt to make any biased choices, it’s notable that Land Border Regions and the number of encounters are shown and even with the data, in 2024, the number of CBP encounters have decreased compared to 2023, which is the peak from the data.

STATE LEVEL ENCOUNTER DATA

Description:

The description of “Yearly CBP Encounters Segmented by Demographic Group” demonstrates a relationship among the quantity of demographic groups that the CBP encounters. These demographic groups are FMUA: Individuals in a Family Unit, Accompanied Minors, UC (unaccompanied children) / Single Minors and Single Adults.

As shown by the visualization, the largest demographic groups are single adults, and the smallest are accompanied minors. This visualization makes it easy to distinguish and compare trends across groups.

Even with the years displayed, it’s easy to observe any changes and patterns across the demographics. The interactive features are shown by the hover function and that shows the years for each plot and the total encounters for each group. Additionally, the interactive legend allows for a user to focus on graphing lines that are of best interest or allows for easier insights among the groups. Overall, the colors allow for accessibility and color-blind friendly, the design is simple, and the axises are simply labeled, and the visualizations allow for user interactivity through the legend and hover function. These functions are able to help with telling a story derived from this data, as the concerns of immigration policies are shown with family distinctions and such moralities of them, we notice groups among these demographics, which can be distinguished by age, admissibility, and relationship. There also seems to be an additional proof among the reduction of immigration from 2023 transitioning onto 2024. Additionally, this is with evaluting the relationships among demographics, where single adults have reduced significantly compared to the other demographics. Overall, this data is helpful in drawing forms of demographic relationships with the total number of encounters.