Resource frontiers and agglomeration economies: The varied logics of transnational land-based investing in Southern and Eastern Africa

Actor-level data on large-scale commercial agriculture in Sub-Saharan Africa are scarce. The peculiar choice of transnational investing in African land has, therefore, been subject to conjecture. Addressing this gap, we reconstructed the underlying logics of investment location choices in a Bayesian network, using firm- and actor-level interview and spatial data from 37 transnational agriculture and forestry investments across 121 sites in Mozambique, Zambia, Tanzania, and Ethiopia. We distinguish four investment locations across gradients of resource frontiers and agglomeration economies to derive the preferred locations of different investors with varied skillsets and market reach (i.e., track record). In contrast to newcomers, investors with extensive track records are more likely to expand the land use frontier, but they are also likely to survive the high transaction costs of the pre-commercial frontier. We highlight key comparative advantages of Southern and Eastern African frontiers and map the most probable categories of investment locations.

Exploratory Spatial Data Analysis of Traffic Forecasting: A Case Study

Transportation planning has historically relied on statistical models to analyze travel patterns across space and time. Recently, an urgency has developed in the United States to address outdated policies and approaches to infrastructure planning, design, and construction. Policymakers at the federal, state, and local levels are expressing greater interest in promoting and funding sustainable transportation infrastructure systems to reduce the damaging effects of pollutive emissions. Consequently, there is a growing trend of local agencies transitioning away from the traditional level-of-service measures to vehicle miles of travel (VMT) measures. However, planners are finding it difficult to leverage their investments in their regional travel demand network models and datasets in the transition. This paper evaluates the applicability of VMT forecasting and impact assessment using the current travel demand model for Dane County, Wisconsin. The main finding is that exploratory spatial data analysis of the derived data uncovered statistically significant spatial relationships and interactions that planners cannot sufficiently visualize using other methods. Planners can apply these techniques to identify places where focused VMT remediation measures for sustainable networks and environments can be most cost-effective.

A Survey on Spatio-temporal Data Analytics Systems

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

Situating Politics: Spatial Heterogeneity and the Study of Political History

While quantitative methods are routinely used to examine historical materials, critics take issue with the use of global regression models that attach a single parameter to each predictor, thereby ignoring the effects of time and space, which together define the context in which historical events unfold. This problem can be addressed by allowing for parameter heterogeneity, as highlighted by the proliferation of work on the use of time-varying parameter models. In this paper, I show how this approach can be extended to the case of spatial data using spatially-varying coefficient models, with an eye toward the study of electoral politics, where the use of spatial data is especially common in historical settings. Toward this end, I revisit a critical case in the field of quantitative history: the rise of electoral Populism in the American West in the period between 1890 and 1896. Upending popular narratives about the correlates of third- party support in the late nineteenth century, I show that the association between third- party vote share and traditional predictors such as economic hardship and ethnic composition varied considerably from one place to the next, giving rise to distinct varieties of electoral Populism—a finding that is missed by global models, which mistake the mathematically particular for the historically general. These findings have important theoretical and empirical implications for the study of political action in a world where parameter heterogeneity is increasingly recognized as a standard feature of modern social science.

Research on Spatial and Dynamic Planning Methods for Settlement Buildings Based on Data Mining

Traditional settlements are widely concerned by academic circles for their unique settlement patterns, exquisite residential buildings, and rich historical and cultural connotations, and their protection and development is an important proposition for rural revitalization. Therefore, from the perspective of big data mining (BDM), this paper explores its application in architectural space and settlement protection of traditional settlements in Hainan and provides new ideas for the protection and renewal of traditional settlements in Hainan. The attribute elements of spatial data of settlement groups are analyzed by the decision tree classification mining method. In order to avoid the multivalued tendency of ID3 algorithm and improve the efficiency of decision tree generation by ID3 algorithm, an improved ID3 algorithm is proposed by introducing user interest and simplifying the calculation process of the algorithm. At the same time, the graph theory recognition method of grid pattern is proposed. Aiming at the intersection graph and direction relation graph of straight line pattern, grid pattern recognition is realized by solving the connectivity, intersection, and subsequent construction of the maximum complete subgraph. Experiments show that the improved ID3 algorithm has better running efficiency than the parallel algorithm based on cooccurrence matrix. The analysis of the architectural space of traditional settlements in Hainan will help us better grasp social activities and provide direction for the protection and renewal of traditional settlements from the perspective of tourists and residents.

Full 4D Change Analysis of Topographic Point Cloud Time Series using Kalman Filtering

4D topographic point cloud data contain information on surface change processes and their spatial and temporal characteristics, such as the duration, location, and extent of mass movements, e.g., rockfalls or debris flows. To automatically extract and analyse change and activity patterns from this data, methods considering the spatial and temporal properties are required. The commonly used M3C2 point cloud distance reduces uncertainty through spatial averaging for bitemporal analysis. To extend this concept into the full 4D domain, we use a Kalman filter for point cloud change analysis. The filter incorporates M3C2 distances together with uncertainties obtained through error propagation as Bayesian priors in a dynamic model. The Kalman filter yields a smoothed estimate of the change time series for each spatial location, again associated with an uncertainty. Through the temporal smoothing, the Kalman filter uncertainty is, in general, lower than the individual bitemporal uncertainties, which therefore allows detection of more change as significant. In our example time series of bi-hourly terrestrial laser scanning point clouds of around 6 days (71 epochs) showcasing a rockfall-affected high-mountain slope in Tyrol, Austria, we are able to almost double the number of points where change is deemed significant (from 14.9 % to 28.6 % of the area of interest). Since the Kalman filter allows interpolation and, under certain constraints, also extrapolation of the time series, the estimated change values can be temporally resampled. This can be critical for subsequent analyses that are unable to deal with missing data, as may be caused by, e.g., foggy or rainy weather conditions. We demonstrate two different clustering approaches, transforming the 4D data into 2D map visualisations that can be easily interpreted by analysts. By comparison to two state-of-the-art 4D point cloud change methods, we highlight the main advantage of our method to be the extraction of a smoothed best estimate time series for change at each location. A main disadvantage of not being able to detect spatially overlapping change objects in a single pass remains. In conclusion, the consideration of combined temporal and spatial data enables a notable reduction in the associated uncertainty of the quantified change value for each point in space and time, in turn allowing the extraction of more information from the 4D point cloud dataset.

Environmental Protection, Industrial Structure and Urbanization: Spatiotemporal Evidence from Beijing–Tianjin–Hebei, China

The Beijing–Tianjin–Hebei urban agglomeration (BTH) is striving to realize the transformation process from a low-efficiency to a high-quality development mode; however, it still has problems regarding reducing energy consumption and ecological environment pressure. Based on panel data from 2013 to 2017, this paper proposes an evaluation index system based on BTH’s “environmental protection–industrial structure–urbanization” system. In the course of applying the coupling degree model (CDM) and the coupling coordination degree model (CCDM) with exploratory spatial data analysis (ESDA) methods, this paper discusses the spatiotemporal process, development level, and spatial agglomeration characteristics of the environmental protection–industrial structure–urbanization system in each city of the BTH area. The findings reveal that the coupling degree of the BTH system is gradually increasing, and that the development level of the BTH subsystem is unbalanced: the coupling coordination level of BTH shows a positive evolution process; however, it is in a stage of low-level collaborative development, and there are obvious differences in the level of BTH coupling coordination in space, revealing the convergence of low–high and high–low types. This paper concludes by putting forward the strategy of optimizing the regional spatial pattern of urban agglomeration and implementing integrated development in order to achieve the desired coupling and coordination effects.