2c:null 2d:["$","title",null,{"children":"Well-Maintained Mini Parks: San Francisco’s Hidden Gems Over the Years(2015-2023) | Northeastern University John A. Guerra Gomez classes | Observable"}] 32:T94b,# Well-Maintained Mini Parks:
San Francisco’s Hidden Gems Over the Years(2015-2023) This is a data visuzalization project made by [Shufan Chai](https://observablehq.com/user/@shufan-chai) from Northeastern University for the course CS7295: Information Visualization Fall 2024 with Professor John Alexis Guerra Gomez. Link to the course: https://johnguerra.co/classes/data_visualization_fall_2024/ ## Description In this project, I explored the park maintenance conditions in San Francisco from 2015 to 2023 using the [San Francisco Annual Park Evaluation Scores data.](https://data.sfgov.org/Culture-and-Recreation/Annual-Park-Evaluation-Scores/r33y-seqv/about_data) I analyzed five types of parks, including civic plazas and squares, mini parks, neighborhood parks and playgrounds, parkways, and regional parks. By examining their maintenance scores and visualizing the data through line charts and bar graphs, the project offers insights into how well-maintained these parks have been over time. This project could be valuable for [Urban Planners and Policymakers, Park and Recreation Department Managers, as well as Citizens and Community Groups.](https://observablehq.com/d/6db60d3c44666fc1#cell-218) ## Table of Contents * [Part 1: (Real) Insights](https://observablehq.com/d/6db60d3c44666fc1#cell-59) * [Part 2: Data Abstraction](https://observablehq.com/d/6db60d3c44666fc1#cell-25) * [2.1 Data source](https://observablehq.com/d/6db60d3c44666fc1#cell-27) * [2.2 Dataset discription](https://observablehq.com/d/6db60d3c44666fc1#cell-37) * [2.3 Attributes used](https://observablehq.com/d/6db60d3c44666fc1#cell-43) * [2.4 Derived attributes used](https://observablehq.com/d/6db60d3c44666fc1#cell-45) * [2.5 Data availability](https://observablehq.com/d/6db60d3c44666fc1#cell-47) * [Part 3: Analysis Process](https://observablehq.com/d/6db60d3c44666fc1#cell-8) * [3.1 Example made-up insights and considerations](https://observablehq.com/d/6db60d3c44666fc1#cell-279) * [3.2 Target audience](https://observablehq.com/d/6db60d3c44666fc1#cell-218) * [3.3 Task abstraction](https://observablehq.com/d/6db60d3c44666fc1#cell-192) * [3.4 How abstraction](https://observablehq.com/d/6db60d3c44666fc1#cell-194) * [3.5 Data processing](https://observablehq.com/d/6db60d3c44666fc1#cell-10) * [Refernece](https://observablehq.com/d/6db60d3c44666fc1#cell-12)33:T937,### 3.1 Example Made-up Insights and Considerations I selected both of the following two Made-up insights to analysis.
[The made-up Insight 1 has been confirmed. Please refer to the real insights 1 & 2 in part 1 for more details.](https://observablehq.com/d/6db60d3c44666fc1#cell-59)

[The made-up Insight 2 has been partially confirmed. Please refer to the real Insight 3 in part 1 for more details.](https://observablehq.com/d/6db60d3c44666fc1#cell-136)

Example Made-up Insight 1:
The maintenance scores of some types of parks likely fluctuated between 2015 and 2023, but for Mini Parks, the scores may have remained stable over the years.
Consideration:
For me to test this, I need to have the "year"(number-ordinal) , "park_type"(text-categorical), derived ["avg_park_maintenance_score"(number-quantitative)](https://observablehq.com/d/6db60d3c44666fc1#average_park_maintenance_score_byType) data from 2015 to 2023.
In terms of the idiom, to illustrate the average maintenance scores by type and year, and provide a way to compare each type against the overall average score as a benchmark, a line chart could be used. The chart would have ‘Year’ on the X-axis, ‘Average Maintenance Score’ on the Y-axis, and ‘park_type’ as color labels. A reference line showing [the overall average maintenance score for all park types](https://observablehq.com/d/6db60d3c44666fc1#average_park_maintenance_score_forAllTypes) would serve as a benchmark.

Example Made-up Insight 2:
In 2023, the categories of the top 10 parks are likely concentrated in The Civic Plaza or Square, Mini Park, and Neighborhood Park or Playground.
Consideration:
For me to test this, I need to filter the data by year(2023), and I need to sort the data by park_score(number - quant) to get the [2023 top 10 parks by maintenance score](https://observablehq.com/d/6db60d3c44666fc1#cell-427).
In terms of the idiom, to display the park names and maintenance scores for the top 10 parks by maintenance score, and to show the park type using color, a bar chart could be helpful. The chart would use ‘Park Maintenance Score’ on the X-axis, ‘Park’ on the Y-axis, and ‘park_type’ as color labels.

34:T815,### 3.4 How Abstraction [Chart 1: Trends in Average Park Scores by Park Type (2015-2023) with the Benchmark](https://observablehq.com/d/6db60d3c44666fc1#cell-81)
Marks:

point
line

Channels:
1. Magnitude Channels - Ordered Attributes

x: position on common scale(the year that the evaluation happened)
y: position on common scale(the avarage maintenance score for each park type)
length of the line(the length of the lines between two points conveys the magnitude of maintenance score changes for each park type between two years)

2. Identity Channels - Categorical Attributes

Color hue: park type

Expressiveness and Effectiveness(chart 1): Use point to clearly represent each score for each year beacuse in this case, each park category has a score for each year, and use different line in differernt colors to distinguish the different park types. When looking at the chart, we can effectively distingusih the park type and their correspondingly score change(length of the line) over the years(X axis).

[Chart 2: Top 10 Parks by Maintenance Score in San Francisco (2023)](https://observablehq.com/d/6db60d3c44666fc1#cell-163)
Marks:

line

Channels:
1. Magnitude Channels - Ordered Attributes

x: position on common scale(the maintenance score for each park)
y: position on unaligned scale(the park name)
length of the line(the length of the lines conveys the park maintenance score)

2.Identity Channels - Categorical Attributes

Color hue: park type
Spatial region: The y axis shows the names of the parks, each of which corresponds to a unique spatial location (a row) to help identify and differentiate them.

Expressiveness and Effectiveness(chart 2): For the specifice park, the length of the line represent the their scores, the different colors represent the corresponding park type, and each bar is for one specific park. 35:T4a0,// Step 1: Group data by year and park_type, and aggregate park_score to calculate the average average_park_maintenance_score_byType = { const result = {}; Data.forEach(entry => { const year = entry.fy.replace(/"/g, ''); const parkType = entry.park_type.replace(/"/g, ''); const parkScore = parseFloat(entry.park_score.replace(/"/g, '')); if (!result[year]) { result[year] = {}; } if (!result[year][parkType]) { result[year][parkType] = { totalScore: 0, count: 0 }; } // Accumulate total score and count result[year][parkType].totalScore += parkScore; result[year][parkType].count += 1; }); // Step 2: Calculate the average scores for each year and park_type const averages = []; for (const year in result) { for (const parkType in result[year]) { const totalScore = result[year][parkType].totalScore; const count = result[year][parkType].count; const averageScore = totalScore / count; averages.push({ year: year, park_type: parkType, avg_park_score: averageScore.toFixed(3) // Keep 3 decimal places for average }); } } return averages; } 36:[] 30:{"id":"6db60d3c44666fc1","slug":"san_francisco_park_evaluation","title":"Well-Maintained Mini Parks: San Francisco’s Hidden Gems Over the Years(2015-2023)","thumbnail":"c82a91952162cfe405acf40e1db65427f97039c4807ba0bd0d415a71c1c3d305","access_level":"public","create_time":"$D2024-09-24T21:59:30.510Z","update_time":"$D2024-11-16T04:15:57.684Z","view_time":null,"first_public_time":"$D2024-09-26T18:30:53.392Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"afbaa6d30585fd36","login":"nujguerra","name":"Northeastern University John A. Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","bio":""},"version":558,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"a66d9c6f7c527559","login":"shufan-chai","name":"Shufan Chai","avatar_url":"https://avatars-next.observableusercontent.com/c9b39c358d361b518562d961d655caa1a16cee2580758c0c44efaf089197ceda","tier":null,"bio":"Thou art more lovely and more temperate."}],"members":[{"type":"individual","id":"a66d9c6f7c527559","login":"shufan-chai","name":"Shufan Chai","avatar_url":"https://avatars-next.observableusercontent.com/c9b39c358d361b518562d961d655caa1a16cee2580758c0c44efaf089197ceda","tier":null,"role":"editor"},{"type":"team","id":"afbaa6d30585fd36","login":"nujguerra","name":"Northeastern University John A. Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","role":"viewer"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Well-Maintained Mini Parks: San Francisco’s Hidden Gems Over the Years(2015-2023)","cells":[{"id":0,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"insights","database":"$undefined"},{"id":59,"value":"## Part 1: Real Insights\n
[Chart 1: Trends in Average Park Scores by Park Type (2015-2023) with the Benchmark](https://observablehq.com/d/6db60d3c44666fc1#cell-81)\n

Insight 1: Mini Parks in San Francisco have consistently maintained high standards of care, with their maintenance scores consistently outperforming the benchmark over the years (2015-2023).
Insight 2: Parkway Shows Dramatic Fluctuations. Unlike other parks, Parkway experienced a noticeable dip in 2020, with its score dropping significantly. However, it bounced back strongly in subsequent years, suggesting that the drop might have been due to temporary challenges like renovations or funding issues.

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":81,"value":"vl.layer(\n \n vl.markLine() \n .encode(\n vl.x().fieldT(\"year\").axis({ title: \"Year\" }),\n vl.y().fieldQ(\"avg_park_score\").scale({ zero: false, nice: true }).axis({ title: \"Average Maintenance Score\" }),\n vl.color().fieldN(\"park_type\"),\n vl.tooltip([\"park_type\", \"avg_park_score\"])\n )\n .data(average_park_maintenance_score_byType), \n \n vl.markPoint() \n .encode(\n vl.x().fieldT(\"year\"),\n vl.y().fieldQ(\"avg_park_score\"),\n vl.color().fieldN(\"park_type\"),\n vl.tooltip([\"park_type\", \"avg_park_score\"])\n )\n .data(average_park_maintenance_score_byType), \n\n \n vl.markLine({ strokeDash: [5, 5], color: 'gray' }) \n .encode(\n vl.x().fieldT(\"year\"),\n vl.y().fieldQ(\"avg_park_score\"),\n vl.tooltip([\"year\", \"avg_park_score\"]) \n )\n .data(average_park_maintenance_score_forAllTypes) \n)\n .title(\"Chart 1: Average Park Maintenance Score Trends by Park Type (2015-2023) with the Benchmark\") \n .render();","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":136,"value":"[Chart 2: Top 10 Parks by Maintenance Score in San Francisco (2023)](https://observablehq.com/d/6db60d3c44666fc1#cell-163)\n

Insight 3: Only Two Park Types Dominate the Top 10 in 2023. Even though there are five different types of parks in San Francisco, the top 10 highest-maintained parks in 2023 only include Mini Park and Neighborhood Park or Playground. This suggests that these two types of parks have received the highest levels of attention and care, while other park types like Civic Plaza or Square, Parkways, and Regional Parks did not make it into the top 10, potentially indicating areas that could benefit from further maintenance efforts.

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":163,"value":"vl\n .markBar()\n .encode(\n vl.y().fieldN(\"park\").sort(\"-x\").axis({ title: \"Park\" }),\n vl.x().fieldQ(\"park_score\").axis({ title: \"Park Maintenance Score\" }),\n vl.color().fieldN(\"park_type\"),\n vl.tooltip([\"park_score\", \"supervisor_district\"])\n \n )\n.data(top10_maintenance_score_park_2023)\n .title(\"Chart 2: Top 10 Parks by Maintenance Score in San Francisco (2023)\")\n \n.render();","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"## Part 2: Data Abstraction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":27,"value":"### 2.1 Data Source\nThe data are collected and maintained by The City Services Auditor division of the Controller’s Office, San Francisco. You can view the data source on [DataSF](https://data.sfgov.org/Culture-and-Recreation/Annual-Park-Evaluation-Scores/r33y-seqv/about_data). The dataset includes evaluations of all San Francisco civic plazas and squares, mini parks, neighborhood parks and playgrounds, parkways, and regional parks, covering data from 2015 to 2023.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":37,"value":"### 2.2 Dataset Discription\nThis dataset is a table which has 1324 rows and 6 columns in total.\n
Let's take a look at the first ten rows of the dataset.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":34,"value":"printTable(Data.slice(0, 10))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":390,"value":"Below is the description of fields in the dataset.\n

fy(Number): The fiscal year during which the park was evaluated.
park(Text): Name of the park evaluated.
park_type(Text): Type of park i.e. neighborhood park or playground, regional park, mini park, parkway, civic plaza or square.
park_score(Number): Each park's maintenance score calculated as an annualized percentage from 0 to 1.
psa(Text): The Park Service Area designated by RPD.
supervisor_district(Text): The supervisor district the park falls within.

fy (Ordinal attribute), the fiscal year during which the park was evaluated
park (Categorical attribute), name of the park evaluated
park_type (Categorical attribute), type of park i.e. neighborhood park or playground, regional park, mini park, parkway, civic plaza or square
Supervisor District (Categorical attribute), the supervisor district the park falls within.

average_park_maintenance_score_byType (Quantitative derived, average), the average annual park maintenance scores of different types of parks from 2015 to 2023.
[Steps to get the derived attributes: average_park_maintenance_score_byType](https://observablehq.com/d/6db60d3c44666fc1#cell-95)

average_park_maintenance_score_forAllTypes (Quantitative derived, average), the average annual park maintenance scores of all types of parks from 2015 to 2023.

Urban Planners and Policymakers: Focusing on trends in park maintenance scores and the performance of different park types to better allocate budgets and resources.
Park and Recreation Department Managers: Monitoring the fluctuation in park maintenance scores helps them determine which park facilities and services need improvement.
Citizens and Community Group: Using this data to understand which parks' maintenance is best and choosing which parks to visit for activities.

Task 1: Compare trends in park average maintenance scores for different types of parks between 2015 and 2023 and discover if there are some large fluctuations in scoring.(Discover/lookup/compare, trends/outliers)
Task 2: Locate and identify the 10 highest maintenance score parks in 2023 and determine their distribution within park types.(Discover/locate/identify, distribution)

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":194,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":10,"value":"### 3.5 Data Processing","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":14,"value":"DataSF’s API has a default limit of providing 1,000 rows at a time, so I made two requests and concatenated them together to get the full 1,324 rows.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":16,"value":"data_part1 = fetch(\"https://data.sfgov.org/resource/r33y-seqv.json\").then(res=>res.json())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"data_part2 = fetch(\"https://data.sfgov.org/resource/r33y-seqv.json?$limit=324&$offset=1000\").then(res=>res.json())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"Data = data_part1.concat(data_part2)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":95,"value":"### Compute average park maintenance score by park type from 2015 to 2023","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":98,"value":"$35","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":447,"value":"### Compute average park maintenance score for all park types from 2015 to 2023","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":456,"value":"// Step 1: Group data by year, and aggregate park_score to calculate the average\naverage_park_maintenance_score_forAllTypes = {\n const result = {};\n\n Data.forEach(entry => {\n const year = entry.fy.replace(/\"/g, ''); \n const parkScore = parseFloat(entry.park_score.replace(/\"/g, ''));\n\n if (!result[year]) {\n result[year] = { totalScore: 0, count: 0 };\n }\n\n // Accumulate total score and count\n result[year].totalScore += parkScore;\n result[year].count += 1;\n });\n\n // Step 2: Calculate the average scores for each year\n const averages = [];\n\n for (const year in result) {\n const totalScore = result[year].totalScore;\n const count = result[year].count;\n const averageScore = totalScore / count;\n averages.push({\n year: year,\n avg_park_score: averageScore.toFixed(3) // Keep 3 decimal places for average\n });\n }\n\n return averages;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":427,"value":"### Filter 2023 top 10 maintenance score park","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":145,"value":"top10_maintenance_score_park_2023 = Data.filter(entry => entry.fy === \"2023\").sort((a, b) => parseFloat(b.park_score) - parseFloat(a.park_score)).slice(0, 10)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"## Part 4: The D3 examples and API used in this notebook","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":30,"value":"import {printTable} from \"@uwdata/data-utilities\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"import { vl } from \"@vega/vega-lite-api-v5\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W36","stdlib":"1"}} 31:"$30:body"