{ "fetched_at": "2026-10-19T08:53:00Z", "source_url": "https://www.kaggle.com/competitions/cool-quiet-city-competition/data", "pages": [ { "name": "data-description", "content": "## Smartwatch data\nAll smartwatch-related data (collected by the Cozie platform) are contained in the training and test data files, for which there are details below explaining each of the columns. The physiological and environmental data were collected at a higher frequency than micro-survey responses. Therefore, these datasets are sparse, i.e., many values are empty as not every row contains a microsurvey response. In the following code snippets, we show a few examples of how to filter the data to reduce the sparsity:\n\n#### Filter for Cozie data from one particular participant: \n```python\nfilter = (df['id_participant']=='xesh001')\ndf[filter].head()\n```\n#### Filter for micro-survey responses from one particular participant:\n```python\nfilter = (df['id_participant']=='xesh001') & (df['ws_survey_count'].notna())\ndf[filter].head()\n```\n#### Filter for heart rate data from one particular participant:\n```python\nfilter = (df['id_participant']=='xesh001') & (df['ts_heart_rate'].notna())\ndf[filter]['ts_heart_rate'].head()\n```\n### Micro-survey data\nThere are two larger categories of data provided by the Cozie app: micro-survey data and physiological/activity/environmental data.\n\nOnly rows with micro-survey responses have an integer value in `ws_survey_count`. Columns with responses to the micro-survey question have the prefix `q_`. Only rows with micro-survey responses have longitude and latitude information (`ws_latitude`,`ws_longitude`). `ws_timestamp_location` is the timestamp of when the location data was retrieved. Usually, the value in `ws_timestamp_location` and `index` are close together. There are instances where the location information is much older than the watch survey response, i.e., `ws_timestamp_location`<<`index`. In some instances, no location data was collected for the micro-survey. In these cases, the longitude and latitude are zero.\n\nFor privacy reasons, some of the longitude and latitude data was altered. In cases where the micro-survey response column `q_location` value is 'Indoor - Home', the coordinates (longitude, latitude) were converted to a [hexagonal coordinate system](https://h3geo.org/) with an edge length of approximately 200m. Then, the hexagonal coordinates were converted back into longitude and latitude, resulting in intentionally degraded accuracy. `ws_timestamp_start` is the timestamp of when the micro-survey was started. For micro-surveys, the timestamp in `index` is the time when the watch survey was submitted.\n\n| Field name | Type | Unit | Description |\n|-------------------------|-----------|------|-------------|\n| `id_participant` | string | - | Unique identifier for each participant |\n| `id_unique` | string | - | Unique identifier for row |\n| `index` | timestamp | time |Timestamp (UTC) also serves as index |\n| `q_activity_category_alone` | string | - | Response to micro-survey question 'Category of activity? (alone)' |\n| `q_activity_category_group` | string | - | Response to micro-survey question 'Category of activity? (group)' |\n| `q_alone_group` | string | - | Response to micro-survey question 'Alone or in a group?' \n| `q_earphones` | string | - | Response to micro-survey question 'Wearing earphones?' |\n| `q_location` | string | - | Response to micro-survey question 'Where are you?' |\n| `q_location_office` | string | - | Response to micro-survey question 'What kind of office' |\n| `q_location_transport` | string | - | Response to micro-survey question 'What kind of transport?' |\n| `q_noise_kind` | string | - | Response to micro-survey question 'What kind of noise?' |\n| `q_noise_nearby` | string | - | Response to micro-survey question 'Noise distractions nearby? (without earphones)' |\n| `q_thermal_preference` | string | - | Response tomicro-survey question 'Thermally, what do you prefer now?' |\n| `ws_latitude` | float | ° | Latitude |\n| `ws_longitude` | float | ° | Longitude |\n| `ws_survey_count` | float | - | Increasing counter for each micro-survey response. Resets when Cozie app is (re-)installed. |\n| `ws_timestamp_location` | datetime | time | Timestamp of the location measurement |\n| `ws_timestamp_start` | datetime | time | Timestamp of when micro-survey was started |\n| `dT` | float | min | Duration between the current and the previous micro-survey response in minutes |\n\n### Physiological / Environmental / Activity data\nIndependent of the micro-survey data, the Cozie app also collects physiological (heart rate, blood oxygen saturation, resting heart rate), environmental (noise), and activity data (stand time, step count, walking distance). The sampling intervals for these columns differ between columns and may change within a column.\n\n| Field name | Type | Unit | Description |\n|-------------------------|-----------|------|-------------|\n| `id_participant` | string | - | Unique identifier for each participant |\n| `id_unique` | string | - | Unique identifier for row |\n| `index` | timestamp | time |Timestamp (UTC) of when micro-survey was submitted, also serves as index |\n| `ts_audio_exposure_environment` | float | dB(A)| Noise level, provided by [Apple HealthKit](https://developer.apple.com/documentation/healthkit/hkquantitytypeidentifier/3081271-environmentalaudioexposure) |\n| `ts_heart_rate` | float | bpm | Heart rate, provided by [Apple HealthKit](https://developer.apple.com/documentation/healthkit/hkquantitytypeidentifier/1615138-heartrate)|\n| `ts_oxygen_saturation` | float | % | Blood oxygen saturation provided by [Apple HealthKit](https://developer.apple.com/documentation/healthkit/hkquantitytypeidentifier/1615377-oxygensaturation) |\n| `ts_resting_heart_rate` | float | bpm | Resting heart rate, provided by [Apple HealthKit](https://developer.apple.com/documentation/healthkit/hkquantitytypeidentifier/2867756-restingheartrate) |\n| `ts_stand_time` | float | min | Stand time, provided by [Apple HealthKit](https://developer.apple.com/documentation/healthkit/hkquantitytypeidentifier/3174858-applestandtime) |\n| `ts_step_count` | float | steps | Number of steps walked, provided by [Apple HealthKit](https://developer.apple.com/documentation/healthkit/hkquantitytypeidentifier/1615548-stepcount). The timestamp `time` indicates the time of the first step in the sample. |\n| `ts_walking_distance` | float | m | Distance walked, provided by [Apple HealthKit](https://developer.apple.com/documentation/healthkit/hkquantitytypeidentifier/1615230-distancewalkingrunning). The timestamp `time` indicates the time of the meter in the sample. |\n\n## Spatial Indicators\nRows that contain micro-survey responses also contain the geographic longitude and latitude (`ws_longitude`, `ws_latitude`). These coordinates were used to calculate the spatial indicators using the [Urbanity Python package](https://urbanity.readthedocs.io/en/latest/index.html). The spatial indicators are grouped into **Building Morphology** and **Point of Interest**. Details about the spatial indicator columns can be found in the table below. For more information on the spatial indicators, please refer to [Table 1](https://www.nature.com/articles/s41597-023-02578-1/tables/1) of [Yap, W., Biljecki, F. A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses. Sci Data 10, 667 (2023). ](https://www.nature.com/articles/s41597-023-02578-1)\n\n| Field name | Type | Unit | Description |\n|-------------------------|----------|-----|-------------|\n| **Building Morphology** | | | |\n| `Footprint Proportion` | float | % | Footprint proportion |\n| `Footprint Mean` | float | m2 | Mean area |\n| `Footprint Stdev` | float | m2 | Area standard deviation |\n| `Perimeter Total` | float | m | Total perimeter |\n| `Perimeter Mean` | float | m | Mean perimeter |\n| `Perimeter Stdev` | float | m | Perimeter standard deviation |\n| `Complexity Mean` | float | m | Complexity mean |\n| `Complexity Stdev` | float | m | Complexity standard deviation |\n| `Building Count` | float | count | No. of buildings |\n| **Population** | | | |\n| `PopSum` | float | count | Total Population | \n| `Men` | float | count | Male population | \n| `Women` | float | count | Female population | \n| `Elderly` | float | count | Elderly (aged 60+ years) population | \n| `Youth` | float | count | Young (15-24 years) population | \n| `Children` | float | count | Child (<5 years) population | \n| **Point of Interest** | | | |\n| `Civic` | float | count | Civic amenities |\n| `Commercial` | float | count | Commercial amenities |\n| `Entertainment` | float | count | Entertainment amenities |\n| `Food` | float | count | Food amenities |\n| `Healthcare` | float | count | Healthcare amenities |\n| `Institutional` | float | count | Institutional amenities |\n| `Recreational` | float | count | Recreational amenities |\n| `Social` | float | count | Social amenities |\n| `Green View Mean` | float | - | Green view index |\n| `Green View Stdev` | float | - | Green view index, standard deviation |\n| `Sky View Mean` | float | - | Sky view index |\n| `Sky View Stdev` | float | - | Sky view index, standard deviation |\n| `Building View Mean` | float | - | Building view index |\n| `Building View Stdev` | float | - | Building view index, standard deviation |\n| `Road View Mean` | float | - | Road view index |\n| `Road View Stdev` | float | - | Road view index, standard deviation |\n| `Visual Complexity Mean` | float | - | Visual complexity index |\n| `Visual Complexity Stdev` | float | - | Visual complexity index, standard deviation |\n\n\n## Weather data\nThe weather data was retrieved from the [National Environment Agency (NEA) via data.gov.sg](https://beta.data.gov.sg/collections/1459/view). Data for each weather parameter is in a separate file. The column names indicate the weather station id. There is an additional file for the weather stations, with the name and coordinates of the weather station. The units and accuracy of the weather that is shown in the table below.\n\n| Measurement | Unit | Accuracy |\n|-------------------------|------|------------|\n| Air temperature | °C | ±0.1°C |\n| Relative humidity | % | ±1% |\n| Rain fall | mm | 2% at 1 l/h (0.26 gal/h) |\n| Wind speed | knots | 0-35 m/s: ±0.3 m/s or ±3 % whichever is greater
35 m/s ... 60 m/s: ±5 % | \n| Wind direction | ° | ±3° |\n\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "The data collection process for this competition utilized the open-source [Cozie Apple platform](https://cozie-apple.app/). This mobile and smartwatch application for iOS devices allows people to complete a watch-based micro-survey and provide real-time feedback about environmental conditions via their Apple Watch. It leverages the built-in sensors of the smartwatch to collect physiological (e.g., heart rate, activity), environmental (sound level), and location (latitude and longitude) data. The dataset from this deployment includes micro-survey responses from 98 people, who each provided at least 100 micro-survey responses from October 2022 to August 2023. The micro-survey question flow is seen in Figure 3. For each survey response location, digital-twin-created spatial indicators were calculated using the [Urbanity python package](https://github.com/winstonyym/urbanity). In addition, publicly available weather data is also provided.\n\nThe goal of the competition is to predict the values of the columns `q_noise_nearby`, `q_noise_kind`, `q_earphones`, `q_thermal_preference `as well as possible.\n\n## Micro-survey question flow\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8649844%2Fa2e63dc3ed89035e7e242230f94b9488%2Fwatch_survey_noise_and_privacy-36fcc4ca7094d37b0f0a79419cd93c4a.png?generation=1699286770839443&alt=media) Figure 3: Micro-survey question flow for this data set. Participants in the study were prompted to answer these questions in a diversity of indoor and outdoor environments over the course of 2-4 weeks. [Click here for a larger image.](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8649844%2Fa2e63dc3ed89035e7e242230f94b9488%2Fwatch_survey_noise_and_privacy-36fcc4ca7094d37b0f0a79419cd93c4a.png?generation=1699286770839443&alt=media) \n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Competition Details", "content": "## Team Limits\nYou may participate alone or create a team with a maximum size of up to 5 people.\n\n## Submission Limits\nYou may submit a maximum of 2 entries per day. The accuracy of these submissions will be reflected on the *Public Leaderboard*, which contains roughly half of the test data set. The remaining portion of the test data set will be scored after the competition completion. You may select up to 2 final submissions for this final *Private Leaderboard* scoring.\n\n#Timeline\n* November 15, 2023 - Start Date.\n* ~~February 1, 2024~~ February 23, 2024 (Extended deadline) - Final Submission Deadline.\n\nAll deadlines are at 11:59 PM GMT on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "# Rules\n## One account per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n## Use of Automated Machine Learning Tools (AMLT)\nThis Competition permits the use of automated machine learning tool(s) (“AMLT”) in the creation of Submissions.\n\n## No private sharing outside teams\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\n## Team Mergers\nTeam mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.\n\n## Team Limits\nThe maximum team size is 5.\n\n## Submission Limits\nYou may submit a maximum of 2 entries per day.\n\nYou may select up to 2 final submissions for judging.\n\n## Competition Timeline\n- November 15, 2023 - Start Date.\n- February 1, 2024 - Final Submission Deadline.\n\nAll deadlines are at 11:59 PM GMT on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.\n\n## Competition-specific terms\n\n- COMPETITION TITLE: Cool Quiet City Competition\n- COMPETITION WEBSITE: https://www.kaggle.com/competitions/cool-quiet-city-competition\n- PRIZE: [WIP]\n- WINNER LICENSE TYPE: Open-Source\n- DATA ACCESS AND USE: Competition Use, Non-Commercial Purposes & Academic Research, Commercial Purposes\n\nCompetitions are open to residents worldwide. Other local rules and regulations may apply to you, so please check your local laws to ensure that you are eligible to participate in skills-based competitions. The Competition Host reserves the right to award alternative Prizes where needed to comply with local laws.\n\nENTRY IN THIS COMPETITION CONSTITUTES YOUR ACCEPTANCE OF THESE OFFICIAL COMPETITION RULES.\n\nThe Competition named above is a skills-based competition to promote and further the field of data science. You must register via the Competition Website to enter. Your competition submissions (\"Submissions\") must conform to the requirements stated on the Competition Website. Your Submissions will be scored based on the evaluation metric described on the Competition Website. Subject to compliance with the Competition Rules, Prizes, if any, will be awarded to participants with the best scores, based on the merits of the data science models submitted. See below for the complete Competition Rules.\n\nA. COMPETITION-SPECIFIC RULES\nIn addition to the provisions of the General Competition Rules below, you understand and agree to these Competition-Specific Rules required by the Competition Host:\n\n1. WINNER LICENSE.\nUnder Section 11 (Winners Obligations) of the General Rules below, you hereby grant and will grant Competition Host the following license(s) with respect to your Submission if you are a Competition winner:\n\nOpen Source: You hereby license and will license your winning Submission and the source code used to generate the Submission under an Open-Source Initiative-approved license (see www.opensource.org) that in no event limits commercial use of such code or model containing or depending on such code. For generally commercially available software that you used to generate your submission that is not owned by you, but that can be procured by the Competition Host without undue expense, you do not need to grant the license in the preceding sentence for that software.\n\n2. AUTOMATED MACHINE LEARNING TOOLS (AMLT).\nThis competition permits the use of automated machine learning tool(s) (“AMLT”) in the creation of Submissions.\n\nB. GENERAL COMPETITION RULES\n1. BINDING AGREEMENT.\nTo enter the Competition, you must agree to these Official Competition Rules, which incorporate by reference the provisions and content of the Competition Website and any Specific Competition Rules above (collectively, the \"Rules\"). Please read these Rules carefully before entry to ensure you understand and agree. You further agree that submission of an entry in the Competition constitutes agreement to these Rules. You may not submit an entry to the Competition and are not eligible to receive the prizes associated with this Competition (“Prizes”) unless you agree to these Rules. These Rules form a binding legal agreement between you and the Competition Host with respect to the Competition.\n\n2. ELIGIBILITY. \nA. To be eligible to enter the Competition, you must be: (i) a registered account holder at Kaggle.com; (ii) the older of 18 years old or the age of majority in your jurisdiction of residence (unless otherwise agreed to by Competition Host and appropriate parental/guardian consents have been obtained by Competition Host).\n\nIf you are entering as a representative of a company, educational institution or other legal entity, or on behalf of your employer, these rules are binding on you, individually, and the entity you represent or are an employee. If you are acting within the scope of your employment, as an employee, contractor, or agent of another party, you warrant that such party has full knowledge of your actions and has consented thereto, including your potential receipt of a Prize. You further warrant that your actions do not violate your employer's or entity's policies and procedures.\n\nThe Competition Host reserves the right to verify eligibility and to adjudicate on any dispute at any time. If you provide any false information relating to the Competition concerning your identity, residency, mailing address, telephone number, email address, ownership of right, or information required for entering the Competition, you may be immediately disqualified from the Competition.\n\nB. Unless otherwise stated in the Specific Competition Rules above or prohibited by internal policies of the Competition Entities, employees, interns, contractors, officers and directors of Competition Entities may enter and participate in the Competition but are not eligible to win any Prizes. \"Competition Entities\" means the Competition Host, Kaggle Inc., and their respective parent companies, subsidiaries and affiliates. If you are such a participant from a Competition Entity, you are subject to all applicable internal policies of your employer with respect to your participation.\n\n3. HOSTING PLATFORM.\nThe Competition is hosted by Kaggle Inc. (\"Kaggle\"). You understand that Kaggle has no responsibility with respect to selecting the potential Competition winner(s) or awarding any Prizes. Kaggle will perform certain administrative functions relating to hosting the Competition, and you agree to abide by the provisions relating to Kaggle under these Rules. As a Kaggle.com account holder and user of the Kaggle competition platform, remember you have accepted and are subject to the Kaggle Terms of Service at www.kaggle.com/terms in addition to these Rules.\n\n4. COMPETITION PERIOD.\nFor the purposes of Prizes, the Competition will run from the Start Date and time to the End Date and time (such duration the “Competition Period”). The Competition Timeline is subject to change, and Competition Host may introduce additional hurdle deadlines during the Competition Period. Any updated or additional deadlines will be publicized on the Competition Website. It is your responsibility to check the Competition Website regularly to stay informed of any deadline changes. YOU ARE RESPONSIBLE FOR DETERMINING THE CORRESPONDING TIME ZONE IN YOUR LOCATION.\n\n5. COMPETITION ENTRY. \nNO PURCHASE NECESSARY TO ENTER OR WIN. To enter the Competition, you must register on the Competition Website prior to the Entry Deadline and follow the instructions for developing and entering your Submission through the Competition Website. Your Submissions must be made in the manner and format, and in compliance with all other requirements, stated on the Competition Website (the \"Requirements\"). Submissions must be received before any Submission deadlines stated on the Competition Website. Submissions not received by the stated deadlines will not be eligible to receive a Prize.\n\nSubmissions may not use or incorporate information from hand labeling or human prediction of the validation dataset or test data records.\n\nIf the Competition is a multi-stage competition with temporally separate training and/or test data, one or more valid Submissions may be required during each Competition stage in the manner described on the Competition Website in order for the Submissions to be Prize eligible.\n\nSubmissions are void if they are in whole or part illegible, incomplete, damaged, altered, counterfeit, obtained through fraud, or late. Competition Host reserves the right to disqualify any entrant who does not follow these Rules, including making a Submission that does not meet the Requirements.\n\n6. INDIVIDUALS AND TEAMS.\nA. Individual Account. You may make Submissions only under one, unique Kaggle.com account. You will be disqualified if you make Submissions through more than one Kaggle account or attempt to falsify an account to act as your proxy. You may submit up to the maximum number of Submissions per day as specified on the Competition Website.\n\nB. Teams. If permitted under the Competition Website guidelines, multiple individuals may collaborate as a team (a \"Team\"); however, you may join or form only one Team. Each Team member must be a single individual with a separate Kaggle account. You must register individually for the Competition before joining a Team. You must confirm your Team membership to make it official by responding to the Team notification message sent to your Kaggle account. Team membership may not exceed the Maximum Team Size stated on the Competition Website.\n\nC. Team Merger. Teams may request to merge via the Competition Website. Team mergers may be allowed provided that: (i) the combined Team does not exceed the Maximum Team Size; (ii) the number of Submissions made by the merging Teams does not exceed the number of Submissions permissible for one Team at the date of the merger request; (iii) the merger is completed before the Competition deadline; and (iv) the proposed combined Team otherwise meets all the requirements of these Rules.\n\n7. COMPETITION DATA.\n\"Competition Data\" means the data or datasets available from the Competition Website for the purpose of use in the Competition, including any prototype or executable code provided on the Competition Website. The Competition Data will contain private and public test sets. Which data belongs to which set will not be made available to participants.\n\nA. Data Access and Use. You may access and use the Competition Data for any purpose, whether commercial or non-commercial, including for participating in the Competition and on Kaggle.com forums, and for academic research and education. The Competition Host reserves the right to disqualify any participant who uses the Competition Data other than as permitted by the Competition Website and these Rules.\n\nB. Data Security. You agree to use reasonable and suitable measures to prevent persons who have not formally agreed to these Rules from gaining access to the Competition Data. You agree not to transmit, duplicate, publish, redistribute or otherwise provide or make available the Competition Data to any party not participating in the Competition. You agree to notify Kaggle immediately upon learning of any possible unauthorized transmission of or unauthorized access to the Competition Data and agree to work with Kaggle to rectify any unauthorized transmission or access.\n\nC. External Data. You may use data other than the Competition Data (“External Data”) to develop and test your models and Submissions. However, you will (i) ensure the External Data is available to use by all participants of the competition for purposes of the competition at no cost to the other participants and (ii) post such access to the External Data for the participants to the official competition forum prior to the Entry Deadline.\n\n8. SUBMISSION CODE REQUIREMENTS.\nA. Private Code Sharing. Unless otherwise specifically permitted under the Competition Website or Competition Specific Rules above, during the Competition Period, you are not allowed to privately share source or executable code developed in connection with or based upon the Competition Data or other source or executable code relevant to the Competition (“Competition Code”). This prohibition includes sharing Competition Code between separate Teams, unless a Team merger occurs. Any such sharing of Competition Code is a breach of these Competition Rules and may result in disqualification.\n\nB. Public Code Sharing. You are permitted to publicly share Competition Code, provided that such public sharing does not violate the intellectual property rights of any third party. If you do choose to share Competition Code or other such code, you are required to share it on Kaggle.com on the discussion forum or kernels associated specifically with the Competition for the benefit of all competitors. By so sharing, you are deemed to have licensed the shared code under an Open-Source Initiative-approved license (see www.opensource.org) that in no event limits commercial use of such Competition Code or model containing or depending on such Competition Code.\n\nC. Use of Open Source. Unless otherwise stated in the Specific Competition Rules above, if open-source code is used in the model to generate the Submission, then you must only use open-source code licensed under an Open-Source Initiative-approved license (see www.opensource.org) that in no event limits commercial use of such code or model containing or depending on such code.\n\n9. DETERMINING WINNERS. \nEach Submission will be scored and ranked by the evaluation metric stated on the Competition Website. During the Competition Period, the current ranking will be visible on the Competition Website's public leaderboard. The potential winner(s) are determined solely by the leaderboard ranking on the private leaderboard, subject to compliance with these Rules. The public leaderboard will be based on the public test set and the private leaderboard will be based on the private test set.\n\nIn the event of a tie, the Submission that was entered first to the Competition will be the winner. In the event a potential winner is disqualified for any reason, the Submission that received the next highest score rank will be chosen as the potential winner.\n\n10. NOTIFICATION OF WINNERS & DISQUALIFICATION.\nThe potential winner(s) will be notified by email.\n\nIf a potential winner (i) does not respond to the notification attempt within one (1) week from the first notification attempt or (ii) notifies Kaggle within one week after the End Date that the potential winner does not want to be nominated as a winner or does not want to receive a Prize, then, in each case (i) and (ii) such potential winner will not receive any Prize, and an alternate potential winner will be selected from among all eligible entries received based on the Competition’s judging criteria.\n\nIn case (i) and (ii) above Kaggle may disqualify the participant. However, in case (ii) above, if requested by Kaggle, such potential winner may provide code and documentation to verify the participant’s compliance with these Rules. If the potential winner provides code and documentation to the satisfaction of Kaggle, the participant will not be disqualified pursuant to this paragraph.\n\nCompetition Host reserves the right to disqualify any participant from the Competition if the Competition Host reasonably believes that the participant has attempted to undermine the legitimate operation of the Competition by cheating, deception, or other unfair playing practices or abuses, threatens or harasses any other participants, Competition Host or Kaggle.\n\nA disqualified participant may be removed from the Competition leaderboard, at Kaggle's sole discretion. If a Participant is removed from the Competition Leaderboard, additional winning features associated with the Kaggle competition platform, for example Kaggle points or medals, may also not be awarded.\n\nThe final leaderboard list will be publicly displayed at Kaggle.com. Determinations of Competition Host are final and binding.\n\n11. WINNERS OBLIGATIONS.\nAs a condition to being awarded a Prize, a Prize winner must fulfill the following obligations:\n\n(a) deliver to the Competition Host the final model's software code as used to generate the winning Submission and associated documentation. The delivered software code should follow these documentation guidelines, must be capable of generating the winning Submission, and contain a description of resources required to build and/or run the executable code successfully; For generally commercially available software that you used to generate your submission that is not owned by you, but that can be procured by the Competition Host without undue expense, you do not need to grant the license in the preceding sentence for that software\n\n(b) grant to the Competition Host the license to the winning Submission stated in the Competition Specific Rules above and represent that you have the unrestricted right to grant that license.\n\n(c) sign and return all Prize acceptance documents as may be required by Competition Host or Kaggle, including without limitation: (i) eligibility certifications; (ii) licenses, releases and other agreements required under the Rules.\n\n12. PRIZES. \nPrize(s) are as described on the Competition Website and are only available for winning during the time period described on the Competition Website. The odd of winning any Prize depends on the number of eligible Submissions received during the Competition Period and the skill of the participants.\n\nAll Prizes are subject to Competition Host's review and verification of the participant’s eligibility and compliance with these Rules, and the compliance of the winning Submissions with the Submissions Requirements. In the event that the Submission demonstrates non-compliance with these Competition Rules, Competition Host may at its discretion take either of the following actions: (i) disqualify the Submission(s); or (ii) require the potential winner to remediate within one week after notice all issues identified in the Submission(s) (including, without limitation, the resolution of license conflicts, the fulfillment of all obligations required by software licenses, and the removal of any software that violates the software restrictions).\n\nA potential winner may decline to be nominated as a Competition winner in accordance with Section 10.\n\nPotential winners must return all required Prize acceptance documents within two (2) weeks following notification of such required documents, or such potential winner will be deemed to have forfeited the prize and another potential winner will be selected. Prize(s) will be awarded within approximately 30 days after receipt by Competition Host or Kaggle of the required Prize acceptance documents. Transfer or assignment of a Prize is not allowed.\n\nYou are not eligible to receive any Prize if you do not meet the Eligibility requirements in Section 2 above.\n\n13. GENERAL CONDITIONS. \nAll federal, state, provincial and local laws and regulations apply.\n\n14. PUBLICITY.\nBy accepting a Prize, you agree that Competition Host, Kaggle and its affiliates may use your name and likeness for advertising and promotional purposes without additional compensation, unless prohibited by law.\n\n15. PRIVACY.\nYou acknowledge and agree that Competition Host and Kaggle may collect, store, share and otherwise use personally identifiable information provided by you during the Kaggle account registration process and the Competition, including but not limited to, name, mailing address, phone number, and email address (“Personal Information”). Kaggle acts as an independent controller with regard to its collection, storage, sharing, and other use of this Personal Information, and will use this Personal Information in accordance with its Privacy Policy , including for administering the Competition. As a Kaggle.com account holder, you have the right to request access to, review, rectification, portability or deletion of any personal data held by Kaggle about you by logging into your account and/or contacting Kaggle Support at .\n\nAs part of Competition Host performing this contract between you and the Competition Host, Kaggle will transfer your Personal Information to Competition Host, which acts as an independent controller with regard to this Personal Information. As a controller of such Personal Information, Competition Host agrees to comply with all U.S. and foreign data protection obligations with regard to your Personal Information. Kaggle will transfer your Personal Information to Competition Host in the country specified in the Competition Host Address listed above, which may be a country outside the country of your residence. Such country may not have privacy laws and regulations similar to those of the country of your residence.\n\n16. WARRANTY, INDEMNITY AND RELEASE.\nYou warrant that your Submission is your own original work and, as such, you are the sole and exclusive owner and rights holder of the Submission, and you have the right to make the Submission and grant all required licenses. You agree not to make any Submission that: (i) infringes any third-party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; or (ii) otherwise violates any applicable U.S. or foreign state or federal law.\n\nTo the maximum extent permitted by law, you indemnify and agree to keep indemnified Competition Entities at all times from and against any liability, claims, demands, losses, damages, costs and expenses resulting from any of your acts, defaults or omissions and/or a breach of any warranty set forth herein. To the maximum extent permitted by law, you agree to defend, indemnify and hold harmless the Competition Entities from and against any and all claims, actions, suits or proceedings, as well as any and all losses, liabilities, damages, costs and expenses (including reasonable attorney's fees) arising out of or accruing from: (a) your Submission or other material uploaded or otherwise provided by you that infringes any third party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; (b) any misrepresentation made by you in connection with the Competition; (c) any non-compliance by you with these Rules or any applicable U.S. or foreign state or federal law; (d) claims brought by persons or entities other than the parties to these Rules arising from or related to your involvement with the Competition; and (e) your acceptance, possession, misuse or use of any Prize, or your participation in the Competition and any Competition-related activity.\n\nYou hereby release Competition Entities from any liability associated with: (a) any malfunction or other problem with the Competition Website; (b) any error in the collection, processing, or retention of any Submission; or (c) any typographical or other error in the printing, offering or announcement of any Prize or winners.\n\n17. INTERNET.\nCompetition Entities are not responsible for any malfunction of the Competition Website or any late, lost, damaged, misdirected, incomplete, illegible, undeliverable, or destroyed Submissions or entry materials due to system errors, failed, incomplete or garbled computer or other telecommunication transmission malfunctions, hardware or software failures of any kind, lost or unavailable network connections, typographical or system/human errors and failures, technical malfunction(s) of any telephone network or lines, cable connections, satellite transmissions, servers or providers, or computer equipment, traffic congestion on the Internet or at the Competition Website, or any combination thereof, which may limit a participant’s ability to participate.\n\n18. RIGHT TO CANCEL, MODIFY OR DISQUALIFY.\nIf for any reason the Competition is not capable of running as planned, including infection by computer virus, bugs, tampering, unauthorized intervention, fraud, technical failures, or any other causes which corrupt or affect the administration, security, fairness, integrity, or proper conduct of the Competition, Competition Host reserves the right to cancel, terminate, modify or suspend the Competition. Competition Host further reserves the right to disqualify any participant who tampers with the submission process or any other part of the Competition or Competition Website. Any attempt by a participant to deliberately damage any website, including the Competition Website, or undermine the legitimate operation of the Competition is a violation of criminal and civil laws. Should such an attempt be made, Competition Host and Kaggle each reserves the right to seek damages from any such participant to the fullest extent of the applicable law.\n\n19. NOT AN OFFER OR CONTRACT OF EMPLOYMENT.\nUnder no circumstances will the entry of a Submission, the awarding of a Prize, or anything in these Rules be construed as an offer or contract of employment with Competition Host or any of the Competition Entities. You acknowledge that you have submitted your Submission voluntarily and not in confidence or in trust. You acknowledge that no confidential, fiduciary, agency, employment or other similar relationship is created between you and Competition Host or any of the Competition Entities by your acceptance of these Rules or your entry of your Submission.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "abstract", "content": "**2026 Update! The competition results have been [open-access published here](https://iopscience.iop.org/article/10.1088/1742-6596/3140/11/112017)! (See also citations section below)**\n\nUrban areas face the growing challenge of noise and heat, which affect our comfort, health, and productivity. Effective management of these factors is crucial as they influence the productivity, satisfaction, and stress levels of people in indoor and outdoor environments.\n\nKnowing and predicting locations with comfortable thermal and acoustic conditions empowers people to find refuge from heat and noise. This knowledge could provide relief from the negative impacts, making the city appear cooler and quieter while still accounting for personal comfort preferences. \n\nHence, this competition's goal is to use contextual data to predict noise distraction and thermal preference across a diversity of indoor and outdoor spaces.\n\nThis competition includes the city-scale collection of 9,808 smartwatch-driven micro-survey responses that were collected alongside 2,659,764 physiological and environmental measurements from 98 people using the open-source Cozie-Apple platform ([website](https://cozie-apple.app/), [paper](https://arxiv.org/abs/2210.13977)) combined with geolocation-driven urban digital twin metrics from the [Urbanity Python package](https://urbanity.readthedocs.io/en/latest/index.html). Figure 1 shows some of these data spatially distributed across Singapore.\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8649844%2Fc5df74fe67c96c97027cf5bf63235ff7%2Fcozie-map.png?generation=1699943959047398&alt=media) Figure 1: Micro-survey response example: location context of participants in Singapore
\n\nAn overview of the input data sources and prediction objectives are outlined in Figure 2. The winning solutions of this competition will provide evidence of the types of pre-processing, modeling, and ensembling methods that provide the most accurate solutions for this context.\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F23583%2F92cbf24951db70c16defd8737d758829%2Fpaper_cozie_3.png?generation=1696826167498337&alt=media) Figure 2: Overview of competition data and structure
\n\n## If you have questions or comments, please leave a post on the [Discussion Board](https://www.kaggle.com/competitions/cool-quiet-city-competition/discussion)\n\nThe discussion board is also useful for sharing strategies, insights, baselines, etc., that can make the competition richer through sharing. We encourage teams to share exploratory data analysis and other notebooks that could help the community.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "## Metric\nThe evaluation metric for this competition is *accuracy*. The metric is calculated as:\n\n$$accuracy = \\frac{TP+ TN}{TP+TN+FP+FN}$$\n\nWhere:\n\n- TP is *true positive*, positive cases predicted as positive\n- TN is *true negative*, negative cases predicted as negative\n- FP is *false positive*, negative cases predicted as positive\n- FN is *false negative*, positive cases predicted as negative\n- TP+TN+FP+FN is equivalent to the number of cases predicted\n\nAs we have four targets here (`q_noise_nearby`, `q_noise_kind`, `q_earphones`, `q_thermal_preference`), accuracy for each target column is calculated and the average of the four of them is returned.\n\n## Notebook Submissions\nYou can make submissions directly from Kaggle Notebooks. By adding your teammates as collaborators on a notebook, you can share and edit code privately with them.\n\n## Submission File\nFor each `id_unique` in the test set, you must predict the target variables: `q_noise_nearby`, `q_noise_kind`, `q_earphones`, `q_thermal_preference`. The file should contain a header and have the following format:\n\n```python\nid_unique,q_noise_nearby,q_noise_kind,q_earphones,q_thermal_preference\n1,None,Not applicable,Not applicable,Cooler\n2,A little,Appliances,No earphones,No change,Public\n3,A little,Talking,No earphones,Cooler\n```", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Winning Solutions", "content": "We will announce the winning teams in the Discussion Board and on LinkedIn. We plan to highlight the winning solutions as part of a Workshop at the ACM BuildSys 2024 conference. Members of the teams that created the winning solutions may be invited to participate in publications.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Tutorials and Example Code", "content": "## Tutorials for this Data/Competition\nWe have prepared some example notebooks for the competition data below. The purpose of these notebooks is twofold: Firstly, provide examples of how to wrangle with the data provided in this competition. Secondly, give some basic initial guidance and ideas for beginners.\n- [Example Notebook 1 - Basic Visualizations](https://www.kaggle.com/code/mariofrei/01-example-basic-visualizations)\n This notebook visualizes the raw data provided for this competition. The visualizations give a high-level insight into the data and demonstrate how to access the data best. The notebook is beginner-friendly.\n- [Example Notebook 2: Minimal Viable ML Example](https://www.kaggle.com/code/mariofrei/02-example-minimal-viable-ml-example)\n This notebook shows a minimal viable machine learning approach that uses the competition data to create a valid submission file. The notebook is beginner-friendly. The default score of the submission file is rather low, but suggestions for improvements are listed at each step of the process.\n- [Example Notebook 3: Exploratory Data Analysis](https://www.kaggle.com/code/ponybiam/03-example-eda)\n This notebook goes beyond Example Notebook 1 and does a deeper, more detailed dive into the competition data.\n- [Example Notebook 4: Data Processing & Feature Engineering](https://www.kaggle.com/code/ponybiam/04-example-data-processing-and-feature-engineer)\n This notebook goes into more detail about data processing and feature engineering using the competition data set.\n\n## General ML Tutorials that might be helpful\nBelow, we have listed some more tutorial notebooks that could be helpful for beginners.\n- [Preprocessing pt. 1: data transformation](https://www.kaggle.com/ponybiam/preprocessing-pt-1-data-transformation)\n- [Preprocessing pt. 2: encoding categorical variables](https://www.kaggle.com/ponybiam/preprocessing-pt-2-encoding-categorical-variable/)\n- [Preprocessing pt. 3: handling missing values](https://www.kaggle.com/ponybiam/preprocessing-pt-3-handling-missing-values)\n- [Feature engineering pt. 1: simple features](https://www.kaggle.com/code/ponybiam/feature-engineering-1-simple-features/notebook)\n- [Feature engineering pt. 2: clustering & PCA](https://www.kaggle.com/ponybiam/feature-engineering-pt-2-clustering-pca)\n- [Feature engineering pt. 3: target encoding](https://www.kaggle.com/ponybiam/feature-engineering-pt-3-target-encoding)\n\n## Kaggle courses\n- [Python](https://www.kaggle.com/learn/python)\n- [Pandas](https://www.kaggle.com/learn/pandas)\n- [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning)\n- [Feature Engineering](https://www.kaggle.com/learn/feature-engineering)", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "What is Cozie?", "content": "Cozie is a smartwatch application for human comfort data collection. It prompts users to respond to quick right-here-right-now surveys on smartwatches. Each survey response is tagged with the location of the participant. Cozie is also able to collect additional information, such as heart rate, noise, blood oxygen saturation, step count, etc. Below are screenshots of Cozie on the Apple Watch and the companion app on the iPhone.\n
\n\n  \n\n\n  \n\n\n
\n\n##Publications\nBelow is a list of publications about Cozie or research involving Cozie, as well as research relevant to Cozie.\n - [Research using Cozie](https://cozie.app/docs/research/publications-cozie)\n - [Review papers including Cozie](https://cozie.app/docs/research/publications-cozie-reviews)\n - [Reading recommendations for Environmental Momentary Assessment (EMA)](https://cozie.app/docs/research/publications-ema)", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Acknowledgments", "content": "\n\n[National University of Singapore (NUS)](https://nus.edu.sg)\n\nThis competition is part of the NUS Digital Twin project that is made up of the following research teams:\n\n\n\n\n[Building and Urban Data Science (BUDS) Lab](https://budslab.org)\n\n\n\n[Urban Analytics Lab (UAL)](https://ual.sg)\n\n\n\n[Integrated Data, Energy Analysis + Simulation (IDEAS) Lab](https://ideaslab.io)\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Citation (BuildSys Paper)", "content": "Please cite these papers if you use the data/code/etc:\n\nClayton Miller, Michael Ibrahim, Iqbal Akbar, Bianca Picchetti, Yun Xuan Chua, Mario Frei, Filip Biljecki, Adrian Chong, Matias Quintana, Chun Fu. The Cool, Quiet City machine learning competition: Overview and results. J Phys Conf Ser. 2025;3140(11):112017. [https://doi.org/10.1088/1742-6596/3140/11/112017](https://doi.org/10.1088/1742-6596/3140/11/112017)\n \n\nClayton Miller, Matias Quintana, Mario Frei, Yun Xuan Chua, Chun Fu, Bianca Picchetti, Winston Yap, Adrian Chong, and Filip Biljecki. 2023. Introducing the Cool, Quiet City Competition: Predicting Smartwatch-Reported Heat and Noise with Digital Twin Metrics. In Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys '23). Association for Computing Machinery, New York, NY, USA, 298–299. [https://doi.org/10.1145/3600100.3626269](https://doi.org/10.1145/3600100.3626269)\n\nIgnore the \"Citation\" section below.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }