metadata

license: unknown
language:
  - en
tags:
  - legal

North Carolina Crime Dataset

Dataset Description

Homepage: The processed dataset, which will soon be updated, is available at the following Huggingface portal: https://huggingface.co/datasets/zwn22/NC_Crime. For the raw datasets, their respective homepages are:
- Cary:

Dataset Description

Homepage: Add homepage URL here if available (unless it's a GitHub repository)
Repository: If the dataset is hosted on github or has a github homepage, add URL here
Paper: If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)
Leaderboard: If the dataset supports an active leaderboard, add link here
Point of Contact: If known, name and email of at least one person the reader can contact for questions about the dataset.
Point of Contact: For any issues related to the raw datasets, please reach out to the respective government offices. For inquiries or issues regarding the processed data, you can contact zwn22 at Huggingface.

Dataset Summary

The dataset comprising public police incidents from various cities in North Carolina, spanning from the early 2000s to 2024, contains valuable information such as crime type, time, and location of occurrence.

Supported Tasks and Leaderboards

Crime Trend Analysis: Analyzing crime trends over time and across different locations. This could involve identifying patterns in crime rates, seasonal variations, or shifts in the types of crimes committed.
Predictive Policing: Developing models to predict future crime occurrences based on historical data. This could help in resource allocation and proactive policing strategies.
Geospatial Analysis: Mapping crime incidents to identify hotspots and regions with higher crime rates. This can aid in understanding geographical factors influencing crime and in deploying resources more effectively.
Crime Type Classification: Using machine learning algorithms to automatically classify incidents into different crime types based on the incident descriptions.
Time Series Analysis: Examining how crime rates change over time and understanding long-term trends or cyclic patterns.

Languages

English

Dataset Structure

Data Instances

Provide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.

{
  'example_field': ...,
  ...
}

Provide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.

Data Fields

List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.

example_field: description of example_field

Note that the descriptions can be initialized with the Show Markdown Data Fields output of the Datasets Tagging app, you will then only need to refine the generated descriptions.

Dataset Creation

Curation Rationale

What need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?

Source Data

This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)

Initial Data Collection and Normalization

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If data was collected from other pre-existing datasets, link to source here and to their Hugging Face version.

If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.

Who are the source language producers?

State whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.

If available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.

Describe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.

Describe other people represented or mentioned in the data. Where possible, link to references for the information.

Annotations

If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.

Annotation process

If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.

Who are the annotators?

If annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.

Describe the people or systems who originally created the annotations and their selection criteria if applicable.

If available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.

Describe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.

Personal and Sensitive Information

State whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See Larson 2017 for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).

State whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).

If efforts were made to anonymize the data, describe the anonymization process.

Considerations for Using the Data

Social Impact of Dataset

Please discuss some of the ways you believe the use of this dataset will impact society.

The statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.

Also describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.

Discussion of Biases

Provide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.

For Wikipedia text, see for example Dinan et al 2020 on biases in Wikipedia (esp. Table 1), or Blodgett et al 2020 for a more general discussion of the topic.

If analyses have been run quantifying these biases, please add brief summaries and links to the studies here.

Other Known Limitations

Rights reserved by governments.

Additional Information

Dataset Curators

List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.

Licensing Information

The dataset from cities in North Carolina, including Cary, Charlotte, Durham, and Raleigh, is sourced from their respective open data portals, each with its specific licensing information and terms of use.

Cary's Open Data Portal operates under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. This allows for both commercial and non-commercial use, distribution, and adaptation of the data, as long as proper attribution is given and any derived works are shared under the same terms.

Charlotte's Open Data Portal serves as a resource for the public to explore, visualize, and download data, supporting the creation of new applications and maps using the available information. Durham's Open Data Portal is committed to making all government data publicly available for unrestricted use.

Raleigh's Open Data Policy stipulates that by using data from their site, users agree to the terms and conditions described under the City of Raleigh Open Data Privacy Policy. The City of Raleigh makes no warranties regarding the completeness, accuracy, or timeliness of the data, and disclaims all express or implied warranties, including those of merchantability or fitness for a particular purpose. Users should be aware that the data format or schema may change as updates are made, and they use the information at their own risk.

Each city's portal reflects a commitment to transparency and public engagement, allowing unrestricted access and utilization of data for various purposes, with specific stipulations in Raleigh's policy and Cary's adoption of the CC BY-SA 4.0 license providing additional guidance on use and liability.

Citation Information

Contributions

Thanks to Town of Cary, City of Charlotte, and City of Raleigh for providing the raw dataset.