Update README.md

README.md

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 ---
 license: unknown
+language:
+- en
+tags:
+- legal
 ---
+# North Carolina Crime Dataset
+## Dataset Description
+
+- **Homepage:**
+  - https://live-durhamnc.opendata.arcgis.com/
+  - https://data.townofcary.org/pages/homepage/
+  - https://data.charlottenc.gov/
+- **Point of Contact:** 
+
+### Dataset Summary
+
+The dataset comprising public police incidents from various small 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
+
+1. **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.
+
+2. **Predictive Policing**: Developing models to predict future crime occurrences based on historical data. This could help in resource allocation and proactive policing strategies.
+
+3. **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.
+
+4. **Crime Type Classification**: Using machine learning algorithms to automatically classify incidents into different crime types based on the incident descriptions.
+
+5. **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](https://huggingface.co/spaces/huggingface/datasets-tagging), 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](https://huggingface.co/datasets/dataset_name).
+
+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](https://www.aclweb.org/anthology/W17-1601.pdf) 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](https://www.aclweb.org/anthology/W17-1601.pdf) 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](https://www.aclweb.org/anthology/W17-1601.pdf) 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)](https://arxiv.org/abs/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) 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
+
+If studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.
+
+## 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
+
+- Durham Open Data Portal: Our mission is to make all government-held data available to the public. Open data is information that is available to and can be freely used by all.
+
+
+### Citation Information
+
+
+
+
+### Contributions
+
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
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