Design of Homogeneous Territorial Units: A Methodological Proposal

One of the main questions to solve when analysing geographically added information consists of the design of territorial units adjusted to the objectives of the study. This is related with the reduction of the effects of the Modifiable Areal Unit Problem (MAUP). In this paper an optimisation model to solve regionalisation problems is proposed. This model seeks to reduce some disadvantages found in previous works about automated regionalisation tools.contiguity constraint, zone design, optimisation, modifiable areal unit problem

Design of homogenous territorial units: a methodological proposal

One of the main questions to solve when analysing geographically added information consists of the design of territorial units adjusted to the objectives of the study. In fact, in those cases where territorial information is aggregated, ad-hoc criteria are usually applied as there are not regionalization methods flexible enough. Moreover, and without taking into account the aggregation method applied, there is an implicit risk that is known in the literature as Modifiable Areal Unit Problem (MAUP) (Openshaw, 1984). This problem is related with the high sensitivity of statistical and econometric results to different aggregations of geographical data, which can negatively affect the robustness of the analysis. In this paper, an optimization model is proposed with the aim of identifying homogenous territorial units related with the analyzed phenomena. This model seeks to reduce some disadvantages found in previous works about automated regionalisation tools. In particular, the model not only considers the characteristics of each element to group but also, the relationships among them, trying to avoid the MAUP. An algoritm, known as RASS (Regionalization Algorithm with Selective Search) it also proposed in order to obtain faster results from the model. The obtained results permit to affirm that the proposed methodology is able to identify a great variety of territorial configurations, taking into account the contiguity constraint among the different elements to be grouped.

Detection of Hotspot for Korea Earthquake Data using Echelon Analysis and Seismic Wave Energy

Echelon analysis (Myers et al., 1997) is a method to investigate the phase-structure of spatial data systematically and objectively. This method is also useful to prospect the areas of interest in regional monitoring of a surface variable. The spatial scan statistic (Kulldorff, 1997) is a method of detection and inference for the zones of significantly high or low rates based on the likelihood ratio. These zones are called hotspots. The purpose of this paper is to detect the hotspot area for spatial data using echelon. We perform echelon analysis for Korea earthquake data. We use ESRI’s ArcGIS that is geographical information system (GIS) software to make the meshed areas and get contiguity information of these areas. With this contiguity information on the meshed areas, we detect the hotspot area using echelon analysis and spatial scan statistics. In addition, we compare with the result of analysis based on the total of number of times simply and the seismic wave energy

Exploring Parallel Efficiency and Synergy for Max-P Region Problem Using Python

Given a set of n areas spatially covering a geographical zone such as a province, forming contiguous regions from homogeneous neighboring areas satisfying a minimum threshold criterion over each region is an interesting NP-hard problem that has applications in various domains such as political science and GIS. We focus on a specific case, called Max-p regions problem, in which the main objective is to maximize the number of regions while keeping heterogeneity in each region as small as possible. The solution is broken into two phases: Construction phase and Optimization phase. We present a parallel implementation of the Max-p problem using Python multiprocessing library. By exploiting an intuitive data structure based on multi-locks, we achieve up 12-fold and 19-fold speeds up over the best sequential algorithm for the construction and optimization phases respectively. We provide extensive experimental results to verify our algorithm

Spatial Optimization Methods And System For Redistricting Problems

Redistricting is the process of dividing space into districts or zones while optimizing a set of spatial criteria under certain constraints. Example applications of redistricting include political redistricting, school redistricting, business service planning, and city management, among many others. Redistricting is a mission-critical component in operating governments and businesses alike. In research fields, redistricting (or region building) are also widely used, such as climate zoning, traffic zone analysis, and complex network analysis. However, as a combinatorial optimization problem, redistricting optimization remains one of the most difficult research challenges. There are currently few automated redistricting methods that have the optimization capability to produce solutions that meet practical needs. The absence of effective and efficient computational approaches for redistricting makes it extremely time-consuming and difficult for an individual person to consider multiple criteria/constraints and manually create solutions using a trial-and-error approach. To address both the scientific and practical challenges in solving real-world redistricting problems, this research advances the methodology and application of redistricting by developing a new computational spatial optimization method and a system platform that can address a wide range of redistricting problems, in an automated and computation-assisted manner. The research has three main contributions. First, an efficient and effective spatial optimization method is developed for redistricting. The new method is based on a spatially constrained and Tabu-based heuristics, which can optimize multiple criteria under multiple constraints to construct high-quality optimization solutions. The new approach is evaluated with real-world redistricting applications and compared with existing methods. Evaluation results show that the new optimization algorithm is more efficient (being able to allow real-time user interaction), more flexible (considering multiple user-expressed criteria and constraints), and more powerful (in terms of optimization quality) than existing methods. As such, it has the potential to enable general users to perform complex redistricting tasks. Second, a redistricting system, iRedistrict, is developed based on the newly developed spatial optimization method to provide user-friendly visual interface for defining redistricting problems, incorporating domain knowledge, configuring optimization criteria and methodology parameters, and ultimately meeting the needs of real-world applications for tackling complex redistricting tasks. It is particularly useful for users of different skill levels, including researchers, practitioners, and the general public, and thus enables public participation in challenging redistricting tasks that are of immense public interest. Performance evaluations with real-world case studies are carried out. Further computational strategies are developed and implemented to handle large datasets. Third, the newly developed spatial optimization method is extended to solve a different spatial optimization problem, i.e., spatial community structure detection in complex networks, which is to partition networks to discover spatial communities by optimizing an objective function. Moreover, a series of new evaluations are carried out with synthetic datasets. This set of evaluations is different from the previous evaluations with case studies in that, the optimal solution is known with synthetic data and therefore it is possible to evaluate (1) whether the optimization method can discover the true pattern (global optima), and (2) how different data characteristics may affect the performance of the method. Evaluation results reveal that existing non-spatial methods are not robust in detecting spatial community structure, which may produce dramatically different outcomes for the same data with different characteristics, such as different spatial aggregations, sampling rates, or noise levels. The new optimization method with spatial constraints is significantly more stable and consistent. In addition to evaluations with synthetic datasets, a case study is also carried out to detect urban community structure with human movements, to demonstrate the application and effectiveness of the approach

Telephone Communication Patterns in Austria. A Comparison of the IPFP based Graph-Theoretic and the lntramax Approaches

Two alternative methodological approaches (the IPFP based and the intramax procedures) to the problem of pattern identification in spatial interaction data are compared and evaluated in this paper. After a general discussion of the major characteristics and shortcomings of these methodologies, the paper presents the findings of a case study relying on telecommunication data measured by the Austrian PTT in 1991, in terms of erlangs. The results clearly illustrate the superiority of the intramax approach in the context of mediumsized and relatively centralised flow systems. (authors' abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScienc

Studi Konvergensi Ageing (Penuaan) Di Negara-Negara Mediterania

The ageing phenomenon became a much-discussed issue in demographic research lately. This study focuses on ageing in the area of Western Europe, especially in the Mediterranean countries. It is a well established that people leave Northern Europe and other region, making Southern Europe as a destination for living. So this contributes to the greater presence of “old” people in Mediterranean countries. We want to explore whether there is a convergence of ageing in the Mediterranean countries. One of convergence approach is σ convergence. This approach views convergence as the reduction of the standard deviation in each period. In this study, we aim at investigating territorial’s ageing behaviors over certain period in order to identify the convergence of ageing. To measure the ageing in a territory, we adopt a common indicator of ageing: the proportion of people aged 65 years and over. We study the ageing behavior of four Mediterranean countries: Italy, France, Spain and Portugal that are divided into geographical unit (in France we recognize this geographical unit as department) over three periods in 1990, 2000 and 2010. For that task, we apply several statistical methods, among those classification, Markov chain approach, regression model, and spatial statistics. Classification method allows us obtaining five groups of territories with similar ageing behavior over time. A characterization of ageing trajectories within groups is given by determination of territories condition over time (young, rather young, moderate, rather old and old). We find that the median of each trajectories group increases over period, so they are heading to ageing. A modeling by Markov chain allows us to describe ageing trajectories using five states obtained earlier. This approach confirmed that all states have the bigger probability going older. In the long run, Markov chain approach predicts that there will be no territory in the young state and almost all territories studied will tend to be in old state. Regression methods leads to the same conclusion as above. Moreover, we observe that territories and their neighbors are becoming increasingly similar over time. So we can conclude that there is convergence of ageing in the Mediterranean based on this model. To improve this study, it would be better add the series of data in order not to lead to inaccurate conclusion. We can also apply other methods e.g. continuous-time Markov chain, space-time dynamic Markov chain, and generalized equation estimation model

Use of Constraints in the Hierarchical Aggregation Procedure Intramax

Background: Intramax is a hierarchical aggregation procedure for dealing with the multi-level specification problem and with the association issue of data set reduction, but it was used as a functional regionalization procedure many times in the past. Objectives: In this paper, we analyse the simultaneous use of three different constraints in the original Intramax procedure, i.e. the contiguity constraint, the higher-inner-flows constraint, and the lower-variation-of-inner-flows constraint. Methods/Approach: The inclusion of constraints in the Intramax procedure was analysed by a programme code developed in Mathematica 10.3 by the processing time, by intra-regional shares of total flows, by self-containment indexes, by numbers of singleton and isolated regions, by the number of aggregation steps where a combination of constraints was applied, by the number of searching steps until the combination of constraints was satisfied, and by surveying the results geographically. Results: The use of the contiguity constraint is important only at the beginning of the aggregation procedure; the higher-inner-flows constraint gives singleton regions, and the lower-variation constraint forces the biggest employment centre as an isolated region up to a relatively high level of aggregation. Conclusions: The original Intramax procedure (without the inclusion of any constraint) gives the most balanced and operative hierarchical sets of functional regions without any singletons or isolated regions