30:T52d,md` -

Why are you interested in this field/domain?

We are passionate travelers, so we are trying to visualize some important aspects of travel planning. So for this project, we are interested in presenting a Seattle Guide for travelers! -

What other examples of data driven project have you found related to this domain (share at least 3)? 1. https://www.aerisweather.com/blog/2016/05/16/making-weather-data-visualizations/ 2. https://www.ventusky.com/?p=38.5;-92.6;3&l=temperature-2m&t=20200502/0100 3. https://carto.com/blog/eighty-data-visualizations-examples-using-location-data-maps/ -

What data-driven questions do you hope to answer about this domain (share at least 3)?

- Which part of Seattle has the cheapest AirBnb/hotel? - When is the best timing to visit Seattle? - What outfit should I prepare for Seattle travels? - Which part of Seattle is the safest part? - What is the average price of Airbnbs in Seattle? - Which area has the most Airbnbs in Seattle? - Which area has the cheapest average price of Airbnbs in Seattle? -

What concepts in your domain would be important to visually explain (more than 1)?

- Weather Pattern - Locations for Airbnb / Hotel and Crime incident `31:T4a2,md`

Source #1: (Prices)

Where did you download the data (e.g., a web URL)? - Living expense in travel: https://www.kaggle.com/shanelev/seattle-airbnb-listings -

How was the data collected or generated? Make sure to explain who collected the data (not necessarily the - - same people that host the data), and who or what the data is about? - The data is parsed by a user from Kaggle and the original file is posted on Github that is scripted off the Airbnb website. The data is about the Airbnb listing in Seattle area scrapped on December 19th, 2018 -

How many observations (rows) are in your data? - There are **${SeattleLiving.length}** observations in the data -

How many features (columns) are in the data? - There are **${Object.keys(SeattleLiving[0]).length}** features in the data -

What questions (from above) can be answered using the data in this dataset? - What is the average price of Airbnbs in Seattle? - Which area has the most Airbnbs in Seattle? - Which area has the cheapest average price of Airbnbs in Seattle? `32:T654,md`

Source #2: (Weather)

Where did you download the data (e.g., a web URL)? - Seattle Weather from 2012 - 2015: https://github.com/vega/vega/blob/master/docs/data/seattle-weather.csv - Seattle Weather from 2016 - 2019: https://www.kaggle.com/sobhanmoosavi/us-weather-events -

How was the data collected or generated? Make sure to explain who collected the data (not necessarily the - - same people that host the data), and who or what the data is about? - The first link is a dataset posted by a user on Github. The user has not specified the original source of the data. The data is a csv file including weather information of seattle from the begining of 2012 to the end of 2015. - The second link is a dataset of US weather information from January 2016 to December 2019. The source originated from the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, 2019. -

How many observations (rows) are in your data? - There are **${weather2012to2015.length}** rows in the data - There are **${weather2016to2019.length}** rows in the data -

How many features (columns) are in the data? - There are **${ Object.keys(weather2012to2015[0]).length }** columns in the data - There are **${ Object.keys(weather2016to2019[0]).length }** columns in the data -

What questions (from above) can be answered using the data in this dataset? - When is the best time to visit Seattle - What outfits should I prepare for Seattle travel `33:T47c,md`

Source #3: (Crime)

Where did you download the data (e.g., a web URL)? - Safety data: https://www.kaggle.com/sohier/seattle-police-department-911-incident-response -

How was the data collected or generated? Make sure to explain who collected the data (not necessarily the - - same people that host the data), and who or what the data is about? - The dataset was kindly made available by the City of Seattle. They also update the data daily as well. Dataset is about police responses to 911 calls that were made in Seattle, Washington. -

How many observations (rows) are in your data? - There are a total of **1,433,853** rows in the official dataset. But we were only able to import to observable **10,000** rows without it crashing. -

How many features (columns) are in the data? - There are a total of **${ Object.keys(SeattleSafety[0]).length }** columns in the data. -

What questions (from above) can be answered using the data in this dataset? - Which part of Seattle is the safest? `34:[{"id":"09e44c63e33096d2af261a1636224ae37cfb83e306e1babba9c31b0e0563f91552b266c7038f743dbce121244d75fa88e52a36e3fb3776e16764082063b8a2db","href":"https://static.observableusercontent.com/files/09e44c63e33096d2af261a1636224ae37cfb83e306e1babba9c31b0e0563f91552b266c7038f743dbce121244d75fa88e52a36e3fb3776e16764082063b8a2db","name":"SeattleCalls.csv","type":"text/csv","lastModified":1588382942509.295,"size":2289204},{"id":"7f899a0755bbd18d4f716b0b108111422df5b0a5241ae164b42f9814a18e0f5acda3a07a4d96edb52700158030037753aa0a085897f3330a96fa5d0f285e902f","href":"https://static.observableusercontent.com/files/7f899a0755bbd18d4f716b0b108111422df5b0a5241ae164b42f9814a18e0f5acda3a07a4d96edb52700158030037753aa0a085897f3330a96fa5d0f285e902f","name":"seattle_01.csv","type":"text/csv","lastModified":1588381765530.59,"size":1835799},{"id":"2e2cadadbd6f4d43e73b56ca2345722b0b91953694ead2060207b733d8ddcb8c4f9a296979296f5fe55f3d4dca80df8effcf919cc558c6d1ecf471e3ac1a1d33","href":"https://static.observableusercontent.com/files/2e2cadadbd6f4d43e73b56ca2345722b0b91953694ead2060207b733d8ddcb8c4f9a296979296f5fe55f3d4dca80df8effcf919cc558c6d1ecf471e3ac1a1d33","name":"Seattle_Weather.csv","type":"text/csv","lastModified":1588383299136.445,"size":589365},{"id":"666b3f949c51743bd4689a2f5e2e4b84dec3a66de7790b91b297ae9ea5e3fa3530ad69eb0b028b13fb02cb75de4dab71b6aba8b38ce2e80ac8cfee189b38d314","href":"https://static.observableusercontent.com/files/666b3f949c51743bd4689a2f5e2e4b84dec3a66de7790b91b297ae9ea5e3fa3530ad69eb0b028b13fb02cb75de4dab71b6aba8b38ce2e80ac8cfee189b38d314","name":"weather.csv","type":"text/csv","lastModified":1588381405853.262,"size":47838},{"id":"2fb5f524c597fd3793fba1d7e61c513aca8b39d60b677629d470037f0db323d08dc52497ec4e3af4ed95120a27ef789edfdcd1eb364b2034ff2fb768af9ece0a","href":"https://static.observableusercontent.com/files/2fb5f524c597fd3793fba1d7e61c513aca8b39d60b677629d470037f0db323d08dc52497ec4e3af4ed95120a27ef789edfdcd1eb364b2034ff2fb768af9ece0a","name":"seattle_01@1.csv","type":"text/csv","lastModified":1588476024244.012,"size":1432106},{"id":"2fb5f524c597fd3793fba1d7e61c513aca8b39d60b677629d470037f0db323d08dc52497ec4e3af4ed95120a27ef789edfdcd1eb364b2034ff2fb768af9ece0a","href":"https://static.observableusercontent.com/files/2fb5f524c597fd3793fba1d7e61c513aca8b39d60b677629d470037f0db323d08dc52497ec4e3af4ed95120a27ef789edfdcd1eb364b2034ff2fb768af9ece0a","name":"Airbnb_Cleaned.csv","type":"text/csv","lastModified":1588476066522.978,"size":1432106}] 2e:{"title":"Part 1 : Proposal","cells":[{"id":0,"value":"md`# Part 1 : Proposal`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"md`\n--- \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":25,"value":"md`## Domain of Interest:\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":18,"value":"md`\n--- \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":65,"value":"md`## Finding Data\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":35,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":45,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":48,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":59,"value":"md`\n--- \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"md`## Appendix`","pinned":false,"hidden":false,"mode":"ojs"},{"id":216,"value":"md`### Table Imports`","pinned":false,"hidden":false,"mode":"ojs"},{"id":190,"value":"\nSeattleLiving = d3.csvParse(await FileAttachment(\"Airbnb_Cleaned.csv\").text())","pinned":false,"hidden":false,"mode":"ojs"},{"id":242,"value":"SeattleSafety = d3.csvParse(await FileAttachment(\"SeattleCalls.csv\").text())","pinned":false,"hidden":false,"mode":"ojs"},{"id":172,"value":"weather2012to2015 = d3.csvParse(await FileAttachment(\"weather.csv\").text())","pinned":false,"hidden":false,"mode":"ojs"},{"id":263,"value":"weather2016to2019 = d3.csvParse(\n await FileAttachment(\"Seattle_Weather.csv\").text()\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":99,"value":"d3 = require(\"d3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":89,"value":"import {\n displayCaution,\n render_data_table,\n table_styles\n} from \"@info474/utilities\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":214,"value":"md`### Other potential link that is useful\n- https://data.seattle.gov/`","pinned":false,"hidden":false,"mode":"ojs"},{"id":12,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W34","stdlib":"1","titling":"auto"}