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: https://data.townofcary.org/pages/homepage/
- Charlotte: https://data.charlottenc.gov/datasets/d22200cd879248fcb2258e6840bd6726/explore?showTable=true
- Durham: https://live-durhamnc.opendata.arcgis.com/documents/DurhamNC::dpd-incidents-ucr-nibrs-reporting/about
- Raleigh: https://data-ral.opendata.arcgis.com/datasets/09af62a32ae8436bae6eda74aa7f172b_0/explore?location=35.785903%2C-78.643000%2C10.73&showTable=true https://data-ral.opendata.arcgis.com/datasets/693811eb361f4da286891eca1fae5943_0/explore?filters=eyJDcmVhdGlvbkRhdGUiOlsxNzA2Njk1MjUzMjA5LDE3MDY2OTUyNTk2MTddfQ%3D%3D&location=35.797935%2C-78.624284%2C9.84
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 ofexample_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
Describe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.
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.
For Cary, Charlotte, and Durham, the data portals provide free and open access to government-held data. Durham's Open Data Portal is committed to making all government data publicly available for unrestricted use. Cary's Open Data Portal offers a platform for free data sharing, with data relating to Cary, NC, available for public use. Charlotte's Open Data Portal is a resource for the public to explore, visualize, and download data, encouraging the creation of new applications and maps using the available information.
Raleigh's Open Data Policy, outlined in their licensing information, 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.
The City of Raleigh reserves the right to modify or discontinue any data feeds, and may require termination of their use if terms of use are violated. Communication through the Raleigh data portal does not constitute a legal or official notice to the city or its officials, and use of the portal indicates acceptance of these policies. The city may amend these terms at any time without notice.
Each city's portal reflects a commitment to transparency and public engagement, allowing unrestricted access and utilization of data for various purposes, with the specific stipulations of Raleigh's policy 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.