Update README.md

README.md

@@ -76,7 +76,9 @@ Note that the descriptions can be initialized with the **Show Markdown Data Fiel
 
 ### 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?
+### Curation Rationale
+
+The dataset, covering police incidents in select North Carolina cities from 2000 to 2024, aims to aid crime research. It provides a long-term view of crime patterns and trends, useful for criminologists, sociologists, and public policy researchers. The comprehensive data enables analyses of crime evolution and its socio-economic correlations. It also supports the development of predictive models for law enforcement and policy planning. Additionally, the dataset's multi-city scope allows for comparative studies to understand unique challenges and inform localized crime prevention strategies.
 
 ### Source Data
 
@@ -98,76 +100,39 @@ I mainly uses 5 datasets as source data:
        'Total Incidents', 'Year'
 - **Charlotte**:
   - [Charlotte Open Data Portal - CMPD Incidents](https://data.charlottenc.gov/datasets/d22200cd879248fcb2258e6840bd6726/explore?showTable=true)
-
+  - Details:
+    - Size: 483632 rows * 30 columns
+    - Column names: 'X', 'Y', 'YEAR', 'INCIDENT_REPORT_ID', 'LOCATION', 'CITY', 'STATE',
+       'ZIP', 'X_COORD_PUBLIC', 'Y_COORD_PUBLIC', 'LATITUDE_PUBLIC',
+       'LONGITUDE_PUBLIC', 'DIVISION_ID', 'CMPD_PATROL_DIVISION', 'NPA',
+       'DATE_REPORTED', 'DATE_INCIDENT_BEGAN', 'DATE_INCIDENT_END',
+       'ADDRESS_DESCRIPTION', 'LOCATION_TYPE_DESCRIPTION',
+       'PLACE_TYPE_DESCRIPTION', 'PLACE_DETAIL_DESCRIPTION',
+       'CLEARANCE_STATUS', 'CLEARANCE_DETAIL_STATUS', 'CLEARANCE_DATE',
+       'HIGHEST_NIBRS_CODE', 'HIGHEST_NIBRS_DESCRIPTION', 'OBJECTID', 'Shape',
+       'GlobalID'
 - **Durham**:
   - [Durham Open Data Portal - DPD Incidents UCR/NIBRS Reporting](https://live-durhamnc.opendata.arcgis.com/documents/DurhamNC::dpd-incidents-ucr-nibrs-reporting/about)
-
+  - Details:
+    - Size: 149924 rows * 16 columns
+    - Column names: 'Case Number', 'Report Date', 'Report Time', 'Status', 'Sequence',
+       'ATT/COM', 'UCR Code', 'Offense', 'Address', 'X', 'Y', 'District',
+       'Beat', 'Tract', 'Premise', 'Weapon'
 - **Raleigh**:
   - [Raleigh Open Data Portal - Police Incidents (1)](https://data-ral.opendata.arcgis.com/datasets/09af62a32ae8436bae6eda74aa7f172b_0/explore?location=35.785903%2C-78.643000%2C10.73&showTable=true)
+  - Details:
+    - Size: 422975 rows * 6 columns
+    - Column names: 'LCR Code', 'LCR Description', 'Incident Date', 'Incident #', 'DISTRICT', 'Yesterday'
   - [Raleigh Open Data Portal - Police Incidents (2)](https://data-ral.opendata.arcgis.com/datasets/693811eb361f4da286891eca1fae5943_0/explore?filters=eyJDcmVhdGlvbkRhdGUiOlsxNzA2Njk1MjUzMjA5LDE3MDY2OTUyNTk2MTddfQ%3D%3D&location=35.797935%2C-78.624284%2C9.84)
-
-
-#### 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.
-
+  * This dataset is updated daily. I will monitor these updates and continue collecting data until February 15th, 2024.
+  
 ## 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