Ambulance Locations in a Tiered Emergency Medical System in a City

This paper deals with optimizing the location of ambulance stations in a two-tiered emergency medical system in an urban environment. Several variants of station distribution are calculated by different mathematical programming models and are evaluated by a detailed computer simulation model. A new modification of the modular capacitated location model is proposed. Two ways of demand modelling are applied; namely, the aggregation of the ambient population and the aggregation of permanent residents at the street level. A case study of the city of Prešov, Slovakia is used to assess the models. The performance of the current and proposed sets of locations is evaluated using real historical data on ambulance trips. Computer simulation demonstrates that the modular capacitated location model, with the ambient population demand, significantly reduces the average response time to high-priority patients (by 79 s in the city and 62 s in the district) and increases the percentage of high-priority calls responded to within 8 min (by almost 4% in the city and 5% in the district). Our findings show that a significant improvement in the availability of the service can be achieved when ambulances are not accumulated at a few stations but rather spread over the city territory.

Towards a Comprehensive Measure of the Ambient Population: Building Estimates Using Geographically Weighted Regression

Estimates of the resident population fail to account for human mobility, which significantly impacts the numbers of people in urban areas. Employing the ambient population provides a more nuanced approach to small-area population estimation. This paper utilises statistical modelling and novel data to estimate the size of the ambient population in an urban area. Models of the daytime and night-time ambient populations are produced for the city of Leeds, West Yorkshire, UK. Interestingly, the presence of cash machines and hospitality venues were found to be statistically significant and were identified as the most important predictors of the ambient population. In contrast to the literature, the number of retail hubs, transport hubs, and the density of mobile phone cell towers were not found to have statistically significant relationships with footfall camera counts. Footfall camera data and the results of the predictive model were validated through comparison with manually collected pedestrian counts. The results of this validation process demonstrated that at five out of the six locations in Leeds city centre, the model produced expected estimates of the size of the ambient population. The results suggest that the approach of this study can be used as a tool to inform decision-making within local government and studies in which small area estimates of ambient populations are required.

A data fusion approach to the estimation of temporary populations: An application to Australia

This study establishes a new method for estimating the monthly Average Population Present (APP) in Australian regions. Conventional population statistics, which enumerate people where they usually live, ignore the significant spatial mobility driving short term shifts in population numbers. Estimates of the temporary or ambient population of a region have several important applications including the provision of goods and services, emergency preparedness and serve as more appropriate denominators for a range of social statistics. This paper develops a flexible modelling framework to generate APP estimates from an integrated suite of conventional and novel data sources. The resultant APP estimates reveal the considerable seasonality in small area populations across Australia’s regions alongside the contribution of domestic and international visitors as well as absent residents to the observed monthly variations. The modelling framework developed in the paper is conceived in a manner such that it can be adapted and re-deployed both for use with alternative data sources as well as other situational contexts for the estimation of temporary populations.

Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime?

This article assesses whether ambient population is a more suitable population-at-risk measure for crime types with mobile targets than residential population for the purpose of intelligence-led policing applications. Specifically, the potential use of ambient population as a crime rate denominator and predictor for predictive policing models is evaluated, using mobile phone data (with a total of 9,397,473 data points) as a proxy. The results show that ambient population correlates more strongly with crime than residential population. Crime rates based on ambient population designate different problem areas than crime rates based on residential population. The prediction performance of predictive policing models can be improved by using ambient population instead of residential population. These findings support that ambient population is a more suitable population-at-risk measure, as it better reflects the underlying dynamics in spatiotemporal crime trends. Its use has therefore much as-of-yet unused potential not only for criminal research and theory testing, but also for intelligence-led policy and practice.

Estimates of the Ambient Population: Assessing the Utility of Conventional and Novel Data Sources

This paper will critically assess the utility of conventional and novel data sources for building fine-scale spatio-temporal estimates of the ambient population. It begins with a review of data sources employed in existing studies of the ambient population, followed by preliminary analysis to further explore the utility of each dataset. The identification and critiquing of data sources which may be useful for building estimates of the ambient population are novel contributions to the literature. This paper will provide a framework of reference for researchers within urban analytics and other areas where an accurate measurement of the ambient population is required. This work has implications for national and international applications where accurate small area estimates of the ambient population are crucial in the planning and management of urban areas, the development of realistic models and informing policy. This research highlights workday population estimates, in conjunction with footfall camera and Wi-Fi sensors data as potentially valuable for building estimates of the ambient population.

Assessing the Intraday Variation of the Spillover Effect of Tweets-Derived Ambient Population on Crime

The spatial pattern of geotagged tweets reflects the dynamic distribution of the ambient population during day and night as a result of people’s routine activities. A few studies have assessed the impact of tweets-derived ambient population on crime and the spillover effect of such impact at different spatial and temporal scales. However, none has revealed the intraday variation of such spillover effect. This study analyzes both the direct and spillover effects of tweets-derived ambient population on crime and its intraday difference in day and night during weekdays and weekends. Four crime types, including assault, burglary, robbery, and theft, are examined at the neighborhood level. The analysis is based on negative binomial regression models, with the control of necessary socioeconomic and land-use variables driven by criminology theories. Results show (1) tweets-derived ambient population affects the magnitude of crime, but this effect varies by types of crime at different time periods of the day and week, and (2) the spillover effect of the tweets-derived ambient population exists for all four types of crime during most of the time periods at the neighborhood level and is particularly pronounced for thefts at all time periods. Similar results are seen in the block-level analysis. This study further confirms the utility of the count of geotagged tweets as a measure of the ambient population and its spatial lag for intraday analyses of crime, particularly theft.

Modeling Crime Density with Population Dynamics in Space and Time: An Application of Assault in Gangnam, South Korea

The current study explores populational and environmental factors associated with violent crime. Specifically, it compares ambient and residential populations with regard to their association with assault density at a fine spatial and temporal unit in a city with socio-economic control variables. The results show that the ambient population are consistently associated with the level of assaults throughout the four time periods in a day, while residential population does not contribute much to explaining its variation. In addition, we also find that the percentage of single-member households and the distance to the nearest subway station are constantly associated with assault density, while the proportion of non-residential use and the land price are partially associated.