Minimizing uncertainty in complete automation of Douglas-Peucker Algorithm for geospatial mapping

It is qualitatively evident that the greater the map scale change, the greater the optimal distance threshold of the Douglas-Peucker Algorithm, which is used in polyline simplification. However, no specific quantitative relationships between them are known by far, causing uncertainties in complete automation of the algorithm. To fill this gap, the current paper constructs quantitative relationships based on the spatial similarity theories of polylines. A quantitative spatial similarity relationship model was proposed and evaluated by setting two groups of control experiments and taking <C, T> as coordinates. In order to realize the automatic generalization of the polyline, we verified whether these quantitative relationships could be fitted using the same function with the same coefficients. The experiments revealed that the unary quadratic function is the best, whether the polylines were derived from different or the same geographical feature area(s). The results also show that using the same optimal distance threshold is unreasonable to simplify all polylines from different geographical feature areas. On the other hand, the same geographical feature area polylines could be simplified using the same optimal distance threshold. The uncertainties were assessed by evaluating the automated generalization results for position and geometric accuracy perspectives using polylines from the same geographic feature areas. It is demonstrated that in addition to maintaining the geographical features, the proposed model maintains the shape characteristics of polylines. Limiting the uncertainties would support the realization of completely automatic generalization of polylines and the construction of vector map geodatabases.

A Novel Parallel Algorithm with Map Segmentation for Multiple Geographical Feature Label Placement Problem

Multiple geographical feature label placement (MGFLP) is an NP-hard problem that can negatively influence label position accuracy and the computational time of the algorithm. The complexity of such a problem is compounded as the number of features for labeling increases, causing the execution time of the algorithms to grow exponentially. Additionally, in large-scale solutions, the algorithm possibly gets trapped in local minima, which imposes significant challenges in automatic label placement. To address the mentioned challenges, this paper proposes a novel parallel algorithm with the concept of map segmentation which decomposes the problem of multiple geographical feature label placement (MGFLP) to achieve a more intuitive solution. Parallel computing is then utilized to handle each decomposed problem simultaneously on a separate central processing unit (CPU) to speed up the process of label placement. The optimization component of the proposed algorithm is designed based on the hybrid of discrete differential evolution and genetic algorithms. Our results based on real-world datasets confirm the usability and scalability of the algorithm and illustrate its excellent performance. Moreover, the algorithm gained superlinear speedup compared to the previous studies that applied this hybrid algorithm.

Tipologi batas wilayah administrasi untuk memudahkan pengelolaan batas daerah di Indonesia

Abstrak Terhitung sejak 1945 – 2017, baru sekitar 48% dari 977 segmen batas daerah di Indonesia yang disahkan melalui Peraturan Menteri Dalam Negeri tentang batas daerah. Pengelolaan batas wilayah daerah sangat penting untuk berbagai urusan pembangunan misalnya pengelolaan sumber daya alam. Oleh karena itu, penelitian ini mengusulkan pendekatan baru untuk mendukung pengelolaan batas wilayah yang efisien yaitu melalui segmentasi berbasis tipologi batas wilayah. Tahapan analisis meliputi: membandingkan, memotong, dan mengelompokkan garis batas. Single Buffer Overlay Method digunakan untuk membandingkan unsur geografis pada Peta Rupabumi Indonesia (data referensi) dan garis batas 2017 (data yang diuji). Selanjutnya, dilakukan pemotongan garis sesuai hasil perbandingan. Pada akhirnya, garis tersebut dikelaskan berdasarkan tipologinya (igir, jalan, dan sungai) menggunakan metode SQL (Structured Query Language). Hasil penelitian menunjukkan bahwa mayoritas (41,8%) batas daerah di Indonesia tidak menggunakan unsur geografis tertentu sebagai penanda batasnya, sedangkan persentase penanda batas berupa sungai 35,9%, igir 16,4%, dan jalan 5,8%. Abstract Since 1945 – 2017, only 48% of 977 regional borderlines of Indonesia were legalized by The Ministry of Home Affairs. Properly managed intra-national boundaries are fundamental for development purposes e.g. natural resource management. Therefore, this research proposed a new approach to help managing the boundary efficiently through typology-based borderlines segmentation which was conducted by some stages: compare, split, and classify lines. Single Buffer Overlay Method used for comparison purpose by utilizing some geographical features on a topographic map as a referenced dataset and boundary line (2017 database) as a tested dataset. Then we split the lines based on the comparison result. Finally, each split line was classified into border typologies (road, ridge, and stream) by using the SQL (Structured Query Language) method. We found that most of The Indonesian administrative boundary segments (41.8%) did not use a geographical feature, while the boundary on the rivers 35.9%, ridges 16.4%, and roads 5.8%.

Croatian external relations as reflected by the use of exonyms

Departing from the assumption that exonyms, in the sense of »names used in a specific language for a geographical feature situated outside the area where that language is spoken and differing in its form from the name used in an official or well-established language of the area where the geographical feature is situated« (UN Glossary definition 2007), are indicators of external historical as well as current political, cultural, and economic relations of a community, the article investigates the case of Croatian exonyms as documented by the recently published editions of Ivana Crljenko (2016, 2018). For comparison, (Austrian-)German, Hungarian and Italian exonyms are also examined in this respect. In essence, the assumption is found to also be confirmed by the Croatian case, although several linguistic factors distort the picture. The article also reveals the weaknesses of the current UN Glossary definitions of the terms »exonym« and »endonym«.

The crucial and contested concept of the endonym/exonym divide

Paul Woodman has called it the “great toponymic divide”, but the endonym/exonym distinction is not a concept confined solely to toponymy, it can be transferred to all name categories, where the name used by insiders may differ from the name used by outsiders, e.g., to ethnonyms, anthro ponyms, names of institutions, where we frequently meet, for instance nicknames and derogative designa- tions used by outsiders. But there is no doubt that this divide has its focus on toponymy, since it corresponds there to two basic human attitudes: (1) to the distinction between ‛mine’ and ‛yours’, ‛ours’ and ‛theirs’, and (2) to territoriality, the desire to own a place, which appears at all levels of the construction of human community — from the level of the family up to that of nations. Thus, it has always a political, social, and juridical meaning and is frequently a reason for dispute and conflict. However, even after long and intensive discussions, e.g., in the UNGEGN Working Group of Exonyms, to date we can still see rather divergent approaches to this divide. There is the linguistic approach regarding the endonym and the exonym rather as poles of a continuum, with various intermediary stages. Alternatively, there is the cultural-geographical approach that accepts no other criteria than the spatial relation between the name-using community and the geographical feature denoted by the name. The article elaborates on these items, mainly on the basis of the discussions and publications of the UNGEGN Working Group on Exonyms since 2002.

The Impact of Economic Sanctions on Landlocked States (Comparative Case Studies of CAR and Mali)

Landlocked states are a special category of countries whose economic and social development is associated with a number of additional difficulties due to their geographical location. Among them are: limitation of participation in the international division of labor, high transport costs and costs associated with bureaucratic procedures for crossing the borders of third countries, as well as reducing the competitiveness of exports. The African continent has the largest number of such states. Simultaneously with the indicated political and geographical feature, various sanctions are in force or imposed on a number of this category of African countries, both by the UN and states individually. The sum of these factors negatively affected the development of these states. This article examines in detail two country cases of applying international sanctions against landlocked African countries: the CAR and Mali. The study led to the conclusion that the effectiveness of sanctions imposed against these countries and targeted sanctions against members of their political elites is low. The main damage and negative consequences are for the general population, since they directly relate to everyday life needs and requirements. For a significant part of the population of both countries, the costs of sanctions are compensated by the possibilities of the “economy of war”: illegal extraction of local natural resources, smuggling and speculation of essential goods. Peculiarities of the country’s geographical position, lack of access to the sea, under these conditions, can serve as a factor for further “decoupling” of elites from the sanctions issue and the continuation of their policies.

Geo-ALM: POI Recommendation by Fusing Geographical Information and Adversarial Learning Mechanism

Learning user’s preference from check-in data is important for POI recommendation. Yet, a user usually has visited some POIs while most of POIs are unvisited (i.e., negative samples). To leverage these “no-behavior” POIs, a typical approach is pairwise ranking, which constructs ranking pairs for the user and POIs. Although this approach is generally effective, the negative samples in ranking pairs are obtained randomly, which may fail to leverage “critical” negative samples in the model training. On the other hand, previous studies also utilized geographical feature to improve the recommendation quality. Nevertheless, most of previous works did not exploit geographical information comprehensively, which may also affect the performance. To alleviate these issues, we propose a geographical information based adversarial learning model (Geo-ALM), which can be viewed as a fusion of geographic features and generative adversarial networks. Its core idea is to learn the discriminator and generator interactively, by exploiting two granularity of geographic features (i.e., region and POI features). Experimental results show that Geo- ALM can achieve competitive performance, compared to several state-of-the-arts.

A Parallel Method for Accelerating Visualization for Vector Tiles

<p><strong>Abstract.</strong> Vector tile technology is developing rapidly and has received increasing attention in recent years. Compared to the raster tile, the vector tile has shown incomparable advantages, such as flexible map styles, suitability for high-resolution screens and ease of interaction. Recent studies on vector tiles have mostly focused on improving the efficiency on the server side and have overlooked the efficiency on the client side, which would actually affect user experience. Parallel computing provides solutions to this issue. Parallel visualization for vector tiles is a typical example of embarrassing parallelism, because there is no need for communications between computing units during parallel computing. Therefore, the performance of parallel visualization for vector tiles mainly depends on how the workload is accurately estimated and evenly decomposed onto the computing units.</p><p>The estimation of workload of vector tile visualization is essentially an accurate estimation of the computing time of geographical feature visualization in the tile. This article uses the computational weight to represent the computing time of geographical feature visualization. The visualization process for geographical feature consists of three main steps: retrieving geographical feature, symbolizing geographical feature and rendering geographical feature. This article analysis the influential factors and building the computational weight functions (CWFs) of different types of geographical feature (point, linear and area) in different visualization steps. Then, by analysing the linear relationship between the influential factors and the computing time of geographical feature visualization, the coefficients of CWFs can be obtained by linear regressions. The goodness of fit of all the linear regressions are significant (<i>R</i>²&amp;thinsp;&amp;gt;&amp;thinsp;0.9), which means the computing time of geographical feature visualization, can be accurately estimated by CWFs.</p><p>Once the computational weight of vector tiles is calculated, the workload decomposition is the next key issue. The traditional decomposition methods widely used in spatial domain decomposition are based on evenly divided spatial areas, such as vertical decomposition, horizontal decomposition and so on. However, the distribution of geographical features are usually uneven, the traditional decomposition methods may introduce large imbalance of workload for parallel computing and degrade the efficiency and performance. This article proposes a workload decomposition method based on the computational weight of vector tiles to improve the parallel visualization efficiency of vector tiles. Experiments show that the computational efficiency of parallel visualization of vector tiles with the proposed workload decomposition method is 18.6% higher than that with traditional decomposition methods.</p>