28:null 29:["$","title",null,{"children":"Final Project: On Data and LA County Public Services | Catherine Deal's Workspace | Observable"}] 2f:T48c,# Introduction Los Angeles is one of the most data-rich cities in the United States, with public datasets available on everything from parks and libraries to emergency response times and transit access. At first glance, these datasets appear objective—dots on a map showing where services exist. But when we dig deeper, we find these representations often mask disparities. This project asks students to examine how mapping public services without proper context can mislead, and how the same datasets can be restructured to tell a very different—and often more truthful—story about equity and access in Los Angeles. Using public feature layers from the LA GeoHub and demographic data from the U.S. Census, students will explore different visualizations of the same data. The first visualizations show how absolute counts might be interpreted by observers, where the final visualization relies on coordinates to reveal areas underserved by public infrastructure. Through interactive mapping, photo references, and guided questions, students will learn to recognize the biases in seemingly neutral data and develop a deeper understanding of spatial justice.30:T5ed,# If someone were to tell you that there are 795 parks and 187 libraries in LA County, how would you immediately visualize a map of the distribution of those resources? According to LA city and LA county data, there are around 795 parks and 187 libraries spread across the Los Angeles County Area. Reading this as a flat number, it is easy to accept this statistic as an indication of the bountiful presence of these vital public spaces. In fact, when cities and counties boast about the presence of these parks, they generally provide you with a number such as this. But if you were asked to think a little bit more deeply about these numbers, you might realize that this subject is far more nebulous than it immediately appears to be. For someone living in LA County, what would these statistics actually mean? To better understand some of the automatic assumptions a person might make when only receiving this flat number, **the following visualizations attempt to capture how the proportional distribution of parks according to city would fluctuate depending on whether the city's overall area or total population is considered the primary determinant.** The size of the dot varies depending on how many parks or libraries are assumed to be in that city, as determined by proportions. The toggle function allows viewers to isolate or hide certain cities, and the size of each symbolic dot can be increased for easier viewing. ## Maybe you assume the parks and libraries are distributed evenly across cities...31:T419,### Making Connections To assess equity in the distribution of public resources like parks and libraries, it’s essential to examine the demographic landscape of Los Angeles County. The population density map (2019) highlights how residents are unevenly spread, with dense clusters in Central and South LA, while northern areas remain more sparsely populated. Overlaying this with the 2023 Median Household Income map reveals clear socio-economic divides—affluent neighborhoods such as Santa Monica and Beverly Hills contrast sharply with lower-income communities in South and East LA. Further deepening this analysis, the Population Diversity Index showcases LA County’s rich cultural makeup. Many of the most diverse areas—particularly in the southeast and central regions—coincide with communities that are lower-income and more densely populated. Taken together, these visuals underscore the need to evaluate whether park and library placements are aligned with the needs of LA’s most diverse, populous, and underserved communities.32:T904,# Conclusions The equity-adjusted visualization surfaced several striking patterns. Neighborhoods such as South Los Angeles, Boyle Heights, and parts of the San Fernando Valley, despite showing a respectable raw count of parks, libraries, or transit stops, revealed severe under-service once population and poverty were factored in. Conversely, lower-density, wealthier districts on the Westside appeared “dot-rich,” indicating an oversupply relative to local need. These findings illustrate that resource allocation in Los Angeles is not simply the result of geography or market forces—it is the cumulative outcome of policy choices, historical redlining, and uneven civic investment. Maps that ignore these contexts risk naturalizing inequality by making disparity look normal—or invisible. For society, these observations underscore why data transparency alone is insufficient. Open datasets can empower citizens only when they are paired with analytical frameworks that center equity; otherwise, they can be appropriated to justify the status quo. Urban planners, journalists, and advocacy groups must therefore treat demographic normalization and gap analysis as essential steps, not optional add-ons, in any public-facing data work. By exposing mismatches between service location and community need, visualizations can become tools for accountability—providing concrete evidence to support budget reallocations, zoning reforms, and targeted infrastructure investments. **Why This Matters** For students, learning to decode and critique these visualizations is foundational to twenty-first-century citizenship. Whether they pursue careers in the humanities, data science, public policy, or design, they will inevitably encounter datasets that appear authoritative. The ability to ask who is counted, who is invisible, and how presentation shapes perception equips them to challenge oversimplified narratives and to design more responsible, inclusive tools themselves. Ultimately, this project demonstrates that data literacy is not just about technical skill; it is about ethical discernment—recognizing that every chart, map, or dashboard carries social consequences and that informed analysts have a responsibility to surface, rather than suppress, the truths hidden beneath the dots.33:[{"id":"10cb65f025863096385165affb099dbcbd80be8c725cdc1a78afbb594e4a82bdaa2a04f43289089fa18967895a45ce7e61a5f30c5100d710e4d6e416ce687a4d","href":"https://static.observableusercontent.com/files/10cb65f025863096385165affb099dbcbd80be8c725cdc1a78afbb594e4a82bdaa2a04f43289089fa18967895a45ce7e61a5f30c5100d710e4d6e416ce687a4d","name":"AD_4nXck2QNXucnN-ZOzjysvt8fdb4lCvjj2YcKs8rRCxUppcfHN5sh57CjC3c6LmXiI7CRU12Ci1t1l5wr8i_I611Ib-22Cc97GFjiskOv7yGhxMn8X9EMXfzqzSK_Sxa1KCTw4vmtWKQ.png","type":"image/png","lastModified":1748626005338.849,"size":350859},{"id":"b60fa71d4b1a90044713b36f276b782669e84edbf40524140e142c8da6d6f56d7ff9ae59f8b846dc88bc9aff7e1b4601d51294c3fc4c0bb53cf89585d22ecf26","href":"https://static.observableusercontent.com/files/b60fa71d4b1a90044713b36f276b782669e84edbf40524140e142c8da6d6f56d7ff9ae59f8b846dc88bc9aff7e1b4601d51294c3fc4c0bb53cf89585d22ecf26","name":"Image.png","type":"image/png","lastModified":1748626005366.671,"size":431699},{"id":"1432d52dd515071c1281e05044d19b8d2290e5584d30f8ce798f0f0f6e9a7d827d0f9de2fc5495e90a68e79c142d19263bd55479185990cee70623404da29047","href":"https://static.observableusercontent.com/files/1432d52dd515071c1281e05044d19b8d2290e5584d30f8ce798f0f0f6e9a7d827d0f9de2fc5495e90a68e79c142d19263bd55479185990cee70623404da29047","name":"AD_4nXcKS8F0A6otUQSW4gcubXpit0ka_HHwIzkW_VBEAUwKLhzJK6W4qlICwBBCDtBKLsUW80cDWT1-Z2jApD5ydaGZY0W5oplSu4Hwt_ZCXb39dXYDmOfcebQ8VJtt5_qZHhWR-v6B.png","type":"image/png","lastModified":1748626005387.344,"size":499233}] 2c:{"id":"17671a26fd47cf59","slug":null,"title":"Final Project: On Data and LA County Public Services","thumbnail":"623e6892beb0d5765a383fa0c13c46e1db2bdad874a52c5266c7d8924c0dd563","access_level":"public","create_time":"$D2025-05-26T23:14:39.422Z","update_time":"$D2025-06-07T01:59:34.851Z","view_time":null,"first_public_time":"$D2025-05-26T23:14:39.422Z","fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"f78043483f179305","login":"catherine-deal-ws","name":"Catherine Deal's Workspace","avatar_url":"https://avatars-next.observableusercontent.com/4ec4a3f2b854476d7515e919a7797cce5de900ff54ad61af93e94e1d4bea24c3","tier":"starter_2024"},"version":226,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"f2ee2f1935931d21","login":"catherine-deal","name":"Catherine Deal","avatar_url":"https://avatars-next.observableusercontent.com/bf2916f2279ef122e5fe796d142137e4d94f582b39b634b55f4526e7964b4374","tier":null}],"members":[{"type":"individual","id":"f2ee2f1935931d21","login":"catherine-deal","name":"Catherine Deal","avatar_url":"https://avatars-next.observableusercontent.com/bf2916f2279ef122e5fe796d142137e4d94f582b39b634b55f4526e7964b4374","tier":null,"role":"editor"},{"type":"team","id":"f78043483f179305","login":"catherine-deal-ws","name":"Catherine Deal's Workspace","avatar_url":"https://avatars-next.observableusercontent.com/4ec4a3f2b854476d7515e919a7797cce5de900ff54ad61af93e94e1d4bea24c3","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":4,"collection":null,"type":"notebook","body":{"title":"Final Project: On Data and LA County Public Services","cells":[{"id":0,"value":"# Final Project: On Data and LA County Public Services\n#### Digital Humanities 187: Capstone Seminar","pinned":false,"hidden":false,"mode":"md"},{"id":42,"value":"### Major Project Links\n\n- [LA County - Parks](https://parks.lacounty.gov/)\n- [LA City - Parks](https://www.laparks.org/parks)\n- [LA City - Geohub](https://geohub.lacity.org/)\n","pinned":false,"hidden":false,"mode":"md"},{"id":5,"value":"$2f","pinned":false,"hidden":false,"mode":"md"},{"id":45,"value":"map = {\n const container = yield htl.html`

`; // Add width here\n const map = L.map(container).setView([34, -118.1123], 9);\n L.tileLayer(\"https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png\", {\n attribution: \"© OpenStreetMap contributors\"\n }).addTo(map);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":7,"value":"## Context\n\nLos Angeles is a sprawling, diverse city where access to public resources like parks, libraries, schools, and emergency services varies significantly from one neighborhood to another. While city-published datasets can make it seem like services are widely available, these representations often overlook key contextual factors like population density, income level, or historical patterns of disinvestment.\n\nThis project explores how “neutral” data visualizations can obscure inequality and reinforce false assumptions about who has access to what. Use the toggles in the panel to examine the public resources—namely libraries and parks—available in each city, each dot representing a real facility or service. Experimenting with this toggle will allow you to reveal hidden disparities and notice that while some neighborhoods are resource-rich, others appear flat—likely not due to lack of need, but due to systemic gaps in service distribution.","pinned":false,"hidden":false,"mode":"md"},{"id":9,"value":"$30","pinned":false,"hidden":false,"mode":"md"},{"id":108,"value":"","pinned":false,"hidden":false,"mode":"html"},{"id":29,"value":"### But...\n\nThis map assumes that each city in Los Angeles County receives an equal share of the 795 parks and 187 libraries. It’s a simple distribution that ignores factors like population or geographic area, instead treating each city as equally deserving of the same number of resources. While this visualization may seem fair at first glance, it oversimplifies the reality of public needs and usage patterns. Cities with vastly different populations and land areas end up looking equal, potentially masking under- or over-allocation of resources.","pinned":false,"hidden":false,"mode":"md"},{"id":118,"value":"## What if they were instead distributed according to the geographic area of each city? ","pinned":false,"hidden":false,"mode":"md"},{"id":133,"value":"","pinned":false,"hidden":false,"mode":"html"},{"id":125,"value":"### But...\n\nThis map reallocates the parks and libraries in proportion to each city's physical land area. Larger cities in terms of square mileage—such as Lancaster or Santa Clarita—receive more resources regardless of their population size. While this approach considers spatial access and the potential need for more dispersed public infrastructure, it doesn’t address how many people actually use these spaces, possibly overlooking population density and demand.","pinned":false,"hidden":false,"mode":"md"},{"id":130,"value":"## So what if the distribution of these parks and libraries was instead determined by each city's overall population?","pinned":false,"hidden":false,"mode":"md"},{"id":120,"value":"","pinned":false,"hidden":false,"mode":"html"},{"id":211,"value":"### But...\n\nThis visualization adjusts the distribution of parks and libraries according to the total population of each city. Larger cities with more residents (e.g., Los Angeles, Long Beach) are assigned proportionally more resources, represented by larger circles. This approach prioritizes equity based on human need and usage, aiming to ensure that more densely populated areas have greater access to public amenities. It challenges the notion of fairness based solely on geography or administrative boundaries.","pinned":false,"hidden":false,"mode":"md"},{"id":11,"value":"# So, what is the truth?\n\nNow that we have considered some of the possible assumptions that might be made when given flat numbers such as 767 and 158, we can examine the precise distribution of these parks and libraries. \n\nThis visualization plots precisely the location of public parks and public libraries within Los Angeles. Using the toggle function, which allows you to isolate or hide certain cities and their respective parks and libraries, we can explore how our original assumptions differ from reality. The dot size and color of the parks and libraries can also be altered for viewing ease. \n\n*It is worth noting that the original assumptions explored above often occur immediately upon receiving a piece of information (such as a flat statistic) and might only be contradicted through more in-depth exploration of the data, its origins, and important contexts.*","pinned":false,"hidden":false,"mode":"md"},{"id":145,"value":"","pinned":false,"hidden":false,"mode":"html"},{"id":27,"value":"### Observations\n\nThis map presents the actual geographic distribution of parks and libraries throughout Los Angeles County, marked by red (parks) and light blue (libraries) dots. Each dot represents one facility, and the size can be adjusted to enhance visual clarity. The map reveals clear clusters of parks and libraries, especially around dense urban areas like Downtown LA, Santa Monica, and Long Beach. Sparse coverage in areas like Lancaster and Pomona highlights disparities in access to public resources. This visualization serves as a foundational layer for comparison with alternative distribution models based on population or geography.","pinned":false,"hidden":false,"mode":"md"},{"id":36,"value":"# Demographics\n\nNow let's add some more context to this data by looking at certain key demographics within LA County...","pinned":false,"hidden":false,"mode":"md"},{"id":167,"value":"### LA County Total Population by Census Tract (2019)\n\nThis map shows how LA County’s population is densely concentrated in central and southern regions, with sparser populations in northern and coastal areas.","pinned":false,"hidden":false,"mode":"md"},{"id":161,"value":"ad_4nxck2qnxucnnZozjysvt8fdb4lcvjj2ycks8rrcxuppcf = FileAttachment(\"AD_4nXck2QNXucnN-ZOzjysvt8fdb4lCvjj2YcKs8rRCxUppcfHN5sh57CjC3c6LmXiI7CRU12Ci1t1l5wr8i_I611Ib-22Cc97GFjiskOv7yGhxMn8X9EMXfzqzSK_Sxa1KCTw4vmtWKQ.png\").image({width: 600})","pinned":false,"hidden":false,"mode":"ojs"},{"id":163,"value":"### Median Household Income (2023)\n\nThis visualization highlights significant income disparities across LA County, with wealthier neighborhoods in the west and lower-income communities in the south and east.","pinned":false,"hidden":false,"mode":"md"},{"id":160,"value":"image = FileAttachment(\"Image.png\").image({width: 600})","pinned":false,"hidden":false,"mode":"ojs"},{"id":178,"value":"### Los Angeles County Population Diversity Index (2019)\n\nThe diversity index map reveals that the most culturally diverse neighborhoods are located in central and southeastern LA, reflecting the county’s broad ethnic and racial mix.","pinned":false,"hidden":false,"mode":"md"},{"id":162,"value":"ad_4nxcks8f0a6otuqsw4gcubxpit0ka_hhwizkw_vbeauwklh = FileAttachment(\"AD_4nXcKS8F0A6otUQSW4gcubXpit0ka_HHwIzkW_VBEAUwKLhzJK6W4qlICwBBCDtBKLsUW80cDWT1-Z2jApD5ydaGZY0W5oplSu4Hwt_ZCXb39dXYDmOfcebQ8VJtt5_qZHhWR-v6B.png\").image({width: 600})","pinned":false,"hidden":false,"mode":"ojs"},{"id":40,"value":"$31","pinned":false,"hidden":false,"mode":"md"},{"id":22,"value":"$32","pinned":false,"hidden":false,"mode":"md"}],"files":"$W33","stdlib":"1","titling":"auto"}} 2d:"$2c:body"