Spatial modeling of data with excessive zeros applied to reindeer pellet-group counts

We analyze a real data set pertaining to reindeer fecal pellet-group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi-Poisson hierarchical generalized linear model (HGLM), zero-inflated Poisson (ZIP), and hurdle models. The quasi-Poisson HGLM allows for both under- and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi-Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi-Poisson HGLM with spatial random effects.

On processing GPS tracking data of spatiotemporal car movements : a case study

The advancement of GPS technology has made it possible to use GPS devices as orientation and navigation tools, but also as tools to track spatiotemporal information. GPS tracking data can be broadly applied in location-based services, such as spatial distribution of the economy, transportation routing and planning, traffic management and environmental control. Therefore, knowledge of how to process the data from a standard GPS device is crucial for further use. Previous studies have considered various issues of the data processing at the time. This paper, however, aims to outline a general procedure for processing GPS tracking data. The procedure is illustrated step-by-step by the processing of real-world GPS data of car movements in Borlänge in the centre of Sweden.

Using modeling as a co-design approach in the planning process of new care environments

Current research shows a relationship between healthcare architecture and patient-related Outcomes. The planning and designing of new healthcare environments is a complex process; the needs of the various end-users of the environment must be considered, including the patients, the patients’ significant others, and the staff. The aim of this study was to explore the experiences of healthcare professionals participating in group modelling utilizing system dynamics in the pre-design phase of new healthcare environments. We engaged healthcare professionals in a series of workshops using system dynamics to discuss the planning of healthcare environments in the beginning of a construction, and then interviewed them about their experience. An explorative and qualitative design was used to describe participants’ experiences of participating in the group modelling projects. Participants (n=20) were recruited from a larger intervention study using group modeling and system dynamics in planning and designing projects. The interviews were analysed by qualitative content analysis. Two themes were formed, representing the experiences in the group modeling process: ‘Partaking in the G-M created knowledge and empowerment’and ‘Partaking in the G-M was different from what was expected and required time and skills’. The method can support participants in design teams to focus more on their healthcare organization, their care activities and their aims rather than focusing on detailed layout solutions. This clarification is important when decisions about the design are discussed and prepared and will most likely lead to greater readiness for future building process.

Residential planning, driver mobility and CO2 emission

The literature on residences and citizens’ transports has focused on either reforming traffic managing in response to residential relocation or post-evaluation of urban planning policies or the evolution of the urban spatial form. In a city there are hotspots that attract the citizens and most of the transportation in the city arises as the citizens’ movement between their residence and the hotspots. Little scholarly attention has been devoted to the possibility to minimize citizens’ transportation in the city by the urban planning of residential areas. In this paper we propose a method to evaluate the environmental impact (in terms of CO2-emissions) of urban plans of residential areas. The method is illustrated in a Swedish case of a midsize city which is presently preoccupied with urban planning of new residential areas in response to substantial population growth due to immigration. The residential plans aims to increase the compactness and residential density in the current center and sub centers leads to less CO2 emissions compare to urban expansion to the edge of the city. The plans of concentrated apartment buildings are more effective in meeting residential needs and mitigating CO2 emissions than dispersed single-family houses.

Trips and their CO2 emissions induced by a shopping center

Most previous studies have focused on entire trips in a geographic region, while a few of them addressed trips induced by a city landmark. Therefore paper explores trips and their CO2 emissions induced by a shopping center from a time-space perspective and their usage in relocation planning. This is conducted by the means of a case study in the city of Borlänge in mid-Sweden where trips to the city’s largest shopping mall in its center are examined. We use GPS tracking data of car trips that end and start at the shopping center. Thereafter, (1) we analyze the traffic emission patterns from a time-space perspective where temporal patterns reveal an hourly-based traffic emission dynamics and where spatial patterns uncover a heterogeneous distribution of traffic emissions in spatial areas and individual street segments. Further, (2) this study reports that most of the observed trips follow an optimal route in terms of CO2 emissions. In this respect, (3) we evaluate how well placed the current shopping center is through a comparison with two competing locations. We conclude that the two suggested locations, which are close to the current shopping center, do not show a significant improvement in term of CO2 emissions.

On processing GPS tracking data of car-movements in Borlänge, Sweden

The advancement of GPS technology enables GPS devices not only to be used as orientation and navigation tools, but also to track travelled routes. GPS tracking data provides essential information for a broad range of urban planning applications such as transportation routing and planning, traffic management and environmental control. This paper describes on processing the data that was collected by tracking the cars of 316 volunteers over a seven-week period. The detailed information is extracted. The processed data is further connected to the underlying road network by means of maps. Geographical maps are applied to check how the car-movements match the road network. The maps capture the complexity of the car-movements in the urban area. The results show that 90% of the trips on the plane match the road network within a tolerance.

Reliability of GPS based traffic data: an experimental evaluation

GPS tracking of mobile objects provides spatial and temporal data for a broad range of applications including traffic management and control, transportation routing and planning. Previous transport research has focused on GPS tracking data as an appealing alternative to travel diaries. Moreover, the GPS based data are gradually becoming a cornerstone for real-time traffic management. Tracking data of vehicles from GPS devices are however susceptible to measurement errors – a neglected issue in transport research. By conducting a randomized experiment, we assess the reliability of GPS based traffic data on geographical position, velocity, and altitude for three types of vehicles; bike, car, and bus. We find the geographical positioning reliable, but with an error greater than postulated by the manufacturer and a non-negligible risk for aberrant positioning. Velocity is slightly underestimated, whereas altitude measurements are unreliable.

Modeling and forecasting long-term natural gas (NG) consumption in Iran, using particle swarm optimization (PSO)

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.