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Purpose An analysis and identification of the hidden relationships between effective factors in the mortality rate caused by road accidents in Fars Province of Iran to prevent and reduce traffic accidents in the future. Methods This cross-sectional study was conducted to integrate all the pervious researches performed on mortality rate of road traffic accidents in Fars Province from March 21, 2013 to March 20, 2017. In order to reveal the relationships between the factors affecting mortality rates of road traffic accidents, the data regarding road traffic accidents extracted from resources such as Legal Medicine Organization, Traffic Police, Accident & Emergency Department, as well as Department of Roads and Urban Development of Fars Province, then cleaned and the applicable attributes embedded in the data all aggregated for further analysis. It should be noted that the data not related to Fars Province were deleted, the data analyzed, converted and the aggregation between various attributes identified. The aggregation between these different attributes as well as the FP-growth algorithm and two indexes of support and confidence calculated and interesting and effective rules extracted. In the end, several accident-provoking factors, the degree of consecutive and interdependence of each one in road accidents identified and introduced. The statistical analysis was conducted by using Rapid Miner software. Results Of the 6216 people dead due to road traffic accidents, 4865 (79.02%) were male and 1292 (20.98%) were female, 59 of them have no clear gender. The largest portion of people died of road traffic accidents belonged to married and self-employed men who collided with motorcycles in autumn. Moreover, young individuals (aged 19–40 years) with secondary educational level who died of accidents in summer at 12:00 a.m. and then 5:00 p.m. in outer city main roads of Kazerun-Shiraz, then Darab-Shiraz, Fasa-Darab and in within-city main streets had the highest mortality rates. Among women, the middle-aged group (aged 41–65 years) followed by young-aged group (aged 19–40 years) with elementary educational level and then illiterate accounted for the highest mortality rate of road traffic accidents. The automobiles involved in accidents included Pride, Peugeot 405, Peykan pickup, Samand, Peugeot Pars, other vehicles and motorcycles. Conclusion The high mortality rate of illiterate and low-literate in various age groups indicates that educational level plays a crucial role as a factor in road accidents, requiring related organizations such as Traffic Police and Ministry of Education to take necessary measures and policies.

Advances in sensing techniques and IOT enabled the possibility to gain precise information about devices in smart home and smart city environments. Data analysis for sensors and devices may help us develop friendlier systems for smart city or smart home. Sequence pattern mining extracts interesting sequence pattern from data. Electricity usage dose follow a sequence of events. In this study the authors investigate this issue and extracted valuable sequence pattern from real appliances' power usage dataset using PrefixSpan. The experiments in this research is implemented on Spark as a novel distributed and parallel big data processing platform on two different clusters and interesting findings are obtained. These findings show the importance of extracting sequence pattern from power usage data to various applications such as decreasing CO2 and greenhouse gas emission by decreasing the electricity usage. The findings also show the needs to bring big data platforms to processing such kind of data which is captured in smart home and smart cities.

It is vital to capture and analyze, from various sources in smart cities, the data that are beneficial in urban planning and decision making for governments and individuals. Urban policy makers can find a suitable solution for urban development by using the opportunities and capacities of big data, and by combining different heterogeneous data resources in smart cities. This paper presents data related to urban computing with an aim of assessing the knowledge that can be obtained through integration of multiple independent data sources in Smart Cities. The data contains multiple sources in the city of Aarhus, Denmark from August 1, 2014 to September 30, 2014. The sources include land use, waterways, water barriers, buildings, roads, amenities, POI, weather, traffic, pollution, and parking lot data. The published data in this paper is an extended version of the City Pulse project data to which additional data sources collected from online sources have been added.

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