Search results

This study used crime count data from the Pittsburgh, Pennsylvania, Bureau of Police offense reports and 911 computer-aided dispatch (CAD) calls to determine the best univariate forecast method for crime and to evaluate the value of leading indicator crime forecast models.

The researchers used the rolling-horizon experimental design, a design that maximizes the number of forecasts for a given time series at different times and under different conditions. Under this design, several forecast models are used to make alternative forecasts in parallel. For each forecast model included in an experiment, the researchers estimated models on training data, forecasted one month ahead to new data not previously seen by the model, and calculated and saved the forecast error. Then they added the observed value of the previously forecasted data point to the next month's training data, dropped the oldest historical data point, and forecasted the following month's data point. This process continued over a number of months.

A total of 15 statistical datasets and 3 geographic information systems (GIS) shapefiles resulted from this study.

The statistical datasets consist of

• Univariate Forecast Data by Police Precinct (Dataset 1) with 3,240 cases
• Output Data from the Univariate Forecasting Program: Sectors and Forecast Errors (Dataset 2) with 17,892 cases
• Multivariate, Leading Indicator Forecast Data by Grid Cell (Dataset 3) with 5,940 cases
• Output Data from the 911 Drug Calls Forecast Program (Dataset 4) with 5,112 cases
• Output Data from the Part One Property Crimes Forecast Program (Dataset 5) with 5,112 cases
• Output Data from the Part One Violent Crimes Forecast Program (Dataset 6) with 5,112 cases
• Input Data for the Regression Forecast Program for 911 Drug Calls (Dataset 7) with 10,011 cases
• Input Data for the Regression Forecast Program for Part One Property Crimes (Dataset 8) with 10,011 cases
• Input Data for the Regression Forecast Program for Part One Violent Crimes (Dataset 9) with 10,011 cases
• Output Data from Regression Forecast Program for 911 Drug Calls: Estimated Coefficients for Leading Indicator Models (Dataset 10) with 36 cases
• Output Data from Regression Forecast Program for Part One Property Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 11) with 36 cases
• Output Data from Regression Forecast Program for Part One Violent Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 12) with 36 cases
• Output Data from Regression Forecast Program for 911 Drug Calls: Forecast Errors (Dataset 13) with 4,936 cases
• Output Data from Regression Forecast Program for Part One Property Crimes: Forecast Errors (Dataset 14) with 4,936 cases
• Output Data from Regression Forecast Program for Part One Violent Crimes: Forecast Errors (Dataset 15) with 4,936 cases.
• The GIS Shapefiles (Dataset 16) are provided with the study in a single zip file: Included are polygon data for the 4,000 foot, square, uniform grid system used for much of the Pittsburgh crime data (grid400); polygon data for the 6 police precincts, alternatively called districts or zones, of Pittsburgh(policedist); and polygon data for the 3 major rivers in Pittsburgh the Allegheny, Monongahela, and Ohio (rivers).

The Army Map Service was a cartographic agency that focused on the compilation, publication, and distribution of military topographic maps. This collection contains georeferenced historical maps of India and Pakistan collected from 1955-1963 from the U502 series.

The maps are provided as TIFF files that include spatial references that can be read by GIS software. These maps are organized by segments which are then divided into square tiles. The corners of each of these tiles contain an anchor point with corresponding coordinates alongside additional anchor points like a: coastal region, legend, glossary, scale, and a location diagram.

This study examined the geospatial contexts of where terrorism incidents occur, where terrorists plan and prepare for their crimes, and where terrorists reside in the United States. The researchers examined data linked to terrorism-related incidents in the United States from the time of the 9/11 terror attacks in 2001 through 2019. Using these data, the researchers applied innovative analytical methodologies of Risk Terrain Modeling (RTM) and Conjunctive Analysis of Case Configurations (CACC) to evaluate their utility in assessing risk of terrorism.

Risk terrain modeling is a method for identifying situational, place-based risk factors most associated with locations where terrorist incidents are likely to be planned or occur. This method looks at specific aspects of the physical landscape, such as locations of buildings or parking lots. The place-based analysis approach to terrorism investigation represents a shift from the conventional research emphasis on targeting suspicious persons by their demographic or other traits. This approach investigates the importance of location in explanations of crime and terrorism.

According to the American Terrorism Study, during this time between 2001 (after 9/11 and 2019) there were 296 terrorism incidents and 617 pre-incident activities occurred where the state was known. In addition, there were 420 known residences tied to terrorism-related incidents in particular states.

This dataset contains measures of the number and density of businesses and their employees deemed essential in the first year (2020) of the COVID-19 pandemic by the US Department of Homeland Security’s Cybersecurity & Infrastructure Security Agency (CISA) in versions 3.0 (April 17, 2020) and 4.0 (August 18, 2020) of their advisory guidance on the essential critical infrastructure workforce. Measures are provided for 2020 per United States Census Tract or ZIP Code Tabulation Area (ZCTA). This 2020 dataset includes four separate files for four different geographic areas (GIS shapefiles from the United States Census Bureau). The four geographies include:

• Census Tract 2010
• Census Tract 2020
• ZIP Code Tabulation Area (ZCTA) 2010
• ZIP Code Tabulation Area (ZCTA) 2020

Information about which dataset to use can be found in the Usage Notes section of the data documentation.

The TAZAMA Health and Demographic Surveillance System (HDSS) study site is located in the Kisesa and Bukandwe rural electoral wards in the Magu district of the Mwanza Region in Northern Tanzania. The two wards are comprised of six villages. There is one health center and five dispensaries (3 public and 2 private) in the study area. The two wards have eleven government primary schools (at least one in each village) and two secondary schools. Both Mwanza city and Magu town are accessible to residents; buses run along the main road and take about an hour and a half to get to Mwanza. Most of the residents are subsistence farmers; a lot of surplus agricultural produce is traded in Mwanza, which is Tanzania's second city. In the year 2012, the research study covered a population of about 30,000 people who live in the Kisesa and Bukandwe wards. The majority of the residents (about ninety five per cent) belong to the Sukuma ethnic group.

The DSS collects information on births and deaths and movements in and out of the households. It helps researchers to understand the population dynamics in the study area including fertility, mortality and migration patterns. It provides information on the structure of families that live together. The DSS study is also used to identify people who are eligible to participate in the serological surveys (the right age group, and continuously resident rather than just visiting). It provides the data for calculating the denominators for demographic rates.

The objectives of this study are as follows: (1) to improve understanding of the dynamics of the HIV epidemic; (2) to assess the demographic, social and economic impacts of the HIV/AIDS epidemic; (3) to evaluate the effects of national prevention, treatment and care interventions as implemented in Kisesa Ward; (4) to measure child and adult mortality and fertility in the general population and by HIV status; (5) to asses the leading causes of death through verbal autopsy; (6) to assess changes in the family structure due to HIV epidemic; and (7) to provide reliable data for district health planning.