Early Detection of Terrorism Outbreaks Using Prospective Space–Time Scan Statistics

Terrorism is a complex phenomenon with high uncertainty involving a myriad of dynamic known and unknown factors. It is and will remain a challenge to predict or detect terrorism outbreaks at an early stage. This research presents an alternative approach for modeling terrorism activity, one that monitors and detects space–time clusters of terrorist incidents using prospective space–time scan statistics. Such clusters provide indicators of potential outbreaks of terrorist incidents. To evaluate the effectiveness of the approach, we analyze the terrorist incidents in the Consortium for the Study of Terrorism and Responses to Terrorism's (START) Global Terrorism Database (GTD) from 1998 to 2004. Clusters of terrorist events are detected at each time stamp and life trajectories of these clusters are constructed based on their space–time relationship to each other. Through the life trajectories and trends of clusters, we demonstrate how space–time scan statistics detect terrorism outbreaks at an early stage.     

Twenty years of land use and livelihood changes in SE-Niger: Obsolete and short-sighted adaptation to climatic and demographic pressures?

This paper explores the evolution of land use and natural resource management strategies over the past twenty to fifty years in a remote Sahelian region. The empirical example is Karagou village in SE Niger. Building on an in-depth survey from 1992 and a targeted, follow-up analysis of contemporary land use strategies in 2011, the change process is scrutinized.The analysis uses the conceptual lenses of land systems science, human–environmental timelines, and the notion of multiple exposures. Enabling and constraining conditions for local livelihoods in terms of the resource base (landscape, water, and population) are described. Results characterize how land use strategies have developed and how local people opportunistically use different landscape elements such as dune landscapes, valley bottoms (bas-fonds) and oases (cuvettes). Major concerns are rainfall variability, saturation of cropland, and perceived dwindling groundwater resources. It is concluded that the land use and livelihood strategies have remained remarkably stable in the face of the changing socio-ecological fringe conditions, but that this situation may hamper a sustainable transformation.     

Ecosystem health assessment based on analysis of a land use database

Data resources and assessment frameworks are two keys to ecosystem health assessment (EHA). Application of the land use database together with the Pressure–State–Response (PSR) model in EHA provides better precision relative to traditional data resources and assessment frameworks for EHA at the county scale. The raw data are taken from The Anlu County Annals, The Soil Records of Anlu County, and The Statistical Yearbook of National Economy in Anlu County. The spatial and attribute data are partly extracted from the database of current land use in Anlu City using the MAPGIS platform. Based on the results from principal component analysis (PCA), a total of eleven indicators were selected to build an indicator system designed to assess the ecosystem health at the county scale. According to the PSR model, the scores from three indices (press index, state index, and response index) and a comprehensive assessment index (CAI) were calculated, and an assessment map was constructed. The results from the index scores and spatial analysis display a large range for the state of ecosystem health due to the evolution of natural ecosystems and human activities at the county scale. The precision of the current land use database (scale of 1:10,000) makes it a better candidate for evaluating ecosystem health than the traditional data resources. This study also demonstrates the numerous benefits of combining land use databases with GIS functions to assess ecosystem health at the county scale.     

Predicting socioeconomic conditions from satellite sensor data in rural developing countries: A case study using female literacy in Assam,India

Social data from census and household surveys provide key information for monitoring the status of populations, but the data utility can be limited by temporal gaps between surveys. Recent studies have pointed to the potential for remotely sensed satellite sensor data to be used as proxies for social data. Such an approach could provide valuable information for the monitoring of populations between enumeration periods. Field observations in Assam, north-east India suggested that socioeconomic conditions could be related to patterns in the type and abundance of local land cover dynamics prompting the development of a more formal approach. This research tested if environmental data derived from remotely sensed satellite sensor data could be used to predict a socioeconomic outcome using a generalised autoregressive error (GAR_err) model. The proportion of female literacy from the 2001 Indian National Census was used as an indicator of socioeconomic conditions. A significant positive correlation was found with woodland and a significant negative correlation with winter cropland (i.e., additional cropping beyond the normal cropping season). The dependence of female literacy on distance to nearest road was very small. The GAR_err model reduced residual spatial autocorrelation and revealed that the logistic regression model over-estimated the significance of the explanatory covariates. The results are promising, while also revealing the complexities of population–environment interactions in rural, developing world contexts. Further research should explore the prediction of socioeconomic conditions using fine spatial resolution satellite sensor data and methods that can account for such complexities.     

State Transformation,Policy Learning,and Exclusive Displacement in the Process of Urban Redevelopment in Taiwan

Abstract

This paper explores the dynamics behind the changing regimes of urban renewal and its social impacts in Taiwan. Before the 1980s, the state was willing to solely shoulder the job of urban renewal with a wholly supportive financial budget and land appropriation law, while in the 1990s it became financially overburdened due to its renewal policy. Around the year 2000, the state turned towards promoting urban regeneration as a key business model. Through this historical exposition, the Taiwanese story of state transformation in urban renewal policy brings two issues to the fore. The first issue is the learning process concerning the policy of public-private partnership (PPP) initiatives. Trans-border policy mobility connects and constitutes cities, such as Taipei, with other places, such as London, through visits and seminars attended by policy makers and experts. However, policy transferred from abroad is “localized” in the learning process and used to prioritize the regeneration of public lands in the urban area. The PPP model is transformed in the face of domestic political struggles. The second issue is the social exclusion as a result of property-led regeneration. Rather than playing the role of an impartial institutional moderator, the state privileged landowners and developers and sacrificed the rights of tenants to stay put. By doing so, the state secures political support from landowner-cum-citizens and initiates a political culture of property in which local citizenship is predicated on ownership.     

Modeling uncertainty of moving objects on road networks via space–time prisms

Moving objects produce trajectories, which are typically observed in a finite sample of time‐stamped locations. Between sample points, we are uncertain about the moving objects's location. When we assume extra information about an object, for instance, a (possibly location‐dependent) speed limit, we can use space–time prisms to model the uncertainty of an object's location.

Until now, space–time prisms have been studied for unconstrained movement in the 2D plane. In this paper, we study space–time prisms for objects that are constrained to travel on a road network. Movement on a road network can be viewed as essentially one‐dimensional. We describe the geometry of a space–time prism on a road network and give an algorithm to compute and visualize space–time prisms. For experiments and illustration, we have implemented this algorithm in MATHEMATICA.

Furthermore, we study the alibi query, which asks whether two moving objects could have possibly met or not. This comes down to deciding if the chains of space–time prisms produced by these moving objects intersect. We give an efficient algorithm to answer the alibi query for moving objects on a road network. This algorithm also determines where and when two moving objects may have met.     

A new algorithm for distance cartogram construction

A distance cartogram is a diagram that visualizes the proximity indices between points in a network, such as time–distances between cities. The Euclidean distances between the points on the distance cartogram represent the given proximity indices. This is a useful visualization tool for the level of service of transport, e.g. difference in the level of service between regions or points in a network and its improvement in the course of time. The two previously proposed methods—multidimensional scaling (MDS) and network time–space mapping—have certain advantages and disadvantages. However, we observe that these methods are essentially the same, and the merits of both these methods can be combined to formulate a generalized solution. In this study, we first formulate the time–space mapping problem, which includes the key features of both of the above stated methods, and propose a generalized solution. We then apply this solution to the time–distances of Japan's railway networks to confirm its applicability.     

Exploring space–time paths in physical and social closeness spaces: a space–time GIS approach

Exploring the evolution of people’s social interactions along with their changing physical locations can help to achieve a better understanding of the processes that generate the relationships between physical distance and social interactions, which can benefit broad fields of study related to social networks. However, few studies have examined the evolving relationships between physical movements and social closeness evolution. This is partially related to the shortage of longitudinal data in both physical locations and social interactions and the lack of an exploratory analysis environment capable of effectively investigating such a process over space and time. With the increasing availability of sociospatiotemporal data in recent years, it is now feasible to examine the relationships between physical separation and social interactions at the individual level in a space–time context. This research was intended to offer a spatiotemporal exploratory analysis approach to address this challenge. The first step was to propose the concept of a social closeness space–time path, which is an extension of the space–time path concept in time geography, to represent evolving human relationships in a social closeness space. A space–time geographical information system (GIS) prototype was then designed to support the representation and analysis of space–time paths in both physical and social closeness spaces. Finally, the effectiveness of the proposed concept and design in gaining insight into the impact of physical migration on online social closeness was demonstrated through an empirical study. The contributions of this study include an extension of the time–geographic framework from physical space to social closeness space, the development of a multirepresentation approach in a GIS to integrate an individual’s space–time paths in both physical and social closeness spaces, and an exploratory analysis of the evolving relationships between physical separation and social closeness over time.     

Spatio-temporal analysis of tree height in a young cork oak plantation

Cork oak is one of the most valuable natural forest genera in the Mediterranean basin. Modelling cork oak growth has been a challenge for foresters in recent years because of strong site and genetic influences, below-ground competition, management regimes and age effects. Because cork productivity is related to forest height, which is, in turn, related directly with site characteristics, an increase in the accuracy of height prediction implies improved productivity estimation. A Bayesian maximum entropy (BME) geostatistical model was applied to characterize the space–time pattern of height of young cork oak in a forest stand from central Sardinia in the years 2000, 2002, 2003, 2006 and 2008. Cork oak height maps were produced for each of the 5 years. The main goals were to analyse and interpret through time (i) the changes in spatial correlation and (ii) the changes in spatial distribution of cork oak height. The plantation was characterized by an increasing spatial dependence through time, whereas the temporal range was 2 years. Cork oak height was significantly correlated with wind speed (reduced by a neighbouring forest) in all the years implying a single trend. The correlations were larger for 2006 and 2008 than for previous years. Three other environmental variables (shade, elevation and slope) were less significant and their influence restricted to 2 years only. This research has several implications for the management of cork oak in the young phase.     

Hydro-climatic trends in the Abay/Upper Blue Nile basin,Ethiopia

Trends of the three hydro-meteorological variables precipitation, temperature and stream flow, represented by 13, 12, and 9 gauging stations, respectively, within the Abay/Upper Blue Nile basin have been studied to support water management in the region. The Trends were evaluated over different time periods depending on data availability at the stations. The statistical Mann–Kendall and Pettitt tests have been used to assess trends and change points respectively. The tests have been applied to mean annual, monthly, seasonal, 1- and 7-days annual minimum and maximum values for streamflow, while mean annual, monthly and seasonal timescales were applied to meteorological variables. The results are heterogeneous and depict statistically significant increasing/decreasing trends. Besides, it showed significant abrupt change of point upward/downward shift for streamflow and temperature time series. However, precipitation time series did not show any statistically significant trends in mean annual and seasonal scales across the examined stations.Increasing trends in temperature at different weather stations for the mean annual, rainy, dry and small rainy seasons are apparent. The mean temperature at Bahir Dar – typical station in the Lake Tana sub basin, has been increasing at the rate of about 0.5 °C/decade, 0.3 °C/decade in rainy season (June–September), 0.6 °C/decade in small rainy season (March–May), and 0.6 °C/decade in dry season (October–February). Other stations in the Abay/Upper Blue Nile show comparable results. Overall it is found that trends and change point times varied considerably across the stations and catchment to catchment. Identified significant trends can help to make better planning decisions for water management. However, the cause attributes to the observed changes in hydro-meteorological variables need further research. In particular the combined effects of land use/land cover change and climate variability on streamflow of Abay/Blue Nile basin and its tributaries needs to be understood better.