scholarly journals Historical Regional Demographic Divergence in Latvia: Lessons of the Common Past with Eastern Partnership Countries

2014 ◽  
Vol 4 (2) ◽  
pp. 119-133
Author(s):  
Aleksandrs Dahs
Keyword(s):  
Regional Development ◽  
Spatial Data ◽  
Spatial Relations ◽  
Lessons Learned ◽  
Spatial Data Analysis ◽  
Eastern Partnership ◽  
Analysis Methodology ◽  
Demographic Indicators ◽  
Eastern Partnership Countries ◽  
Development Processes

AbstractIn recent decades, scientific literature on demographic research is increasing attention on spatial data analysis. It is considered a useful and reliable analysis methodology in evaluating complex regional development processes. However, due to its complexity and reliance on properly captured and quantified spatial relations, it remains a difficult topic for many scholars and practitioners. In Latvia, spatial demographic analysis may prove useful, providing opportunities for uncovering new dimensions of long-term regional demographic and economic development issues. Here, the author analyses spatial distribution aspects of key demographic indicators in Latvia’s municipalities, the associated socio-economic factors and their impact. The implications of the identified spatial processes and dependencies for regional development policy and aid are discussed, including possible lessons learned from or shared with the EU Eastern Partnership countries facing similar challenges.

scholarly journals C4.5 Decision Tree Algorithm for Spatial Data, Alternatives and Performances

2020 ◽  
Vol 27 (3) ◽  
pp. 29-43
Author(s):  
Sihem Oujdi ◽  
Hafida Belbachir ◽  
Faouzi Boufares
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Spatial Data ◽  
Computation Time ◽  
Spatial Relations ◽  
Spatial Data Analysis ◽  
Data Organization ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Decision Tree

Using data mining techniques on spatial data is more complex than on classical data. To be able to extract useful patterns, the spatial data mining algorithms must deal with the representation of data as stack of thematic layers and consider, in addition to the object of interest itself, its neighbors linked through implicit spatial relations. The application of the classification by decision trees combined with the visualization tools represents a convenient decision support tool for spatial data analysis. The purpose of this paper is to provide and evaluate an alternative spatial classification algorithm that supports the thematic-layered data organization, by the adaptation of the C4.5 decision tree algorithm to spatial data, named S-C4.5, inspired by the SCART and spatial ID3 algorithms and the adoption of the Spatial Join Index. Our work concerns both data organization and the algorithm adaptation. Decision tree construction was experimented on traffic accident dataset and benchmarked on both computation time and memory consumption according to different experimentations: study of phenomenon by a single and then by multiple other phenomena, including one or more spatial relations. Different approaches used show compromised and balanced results between memory usage and computation time.

scholarly journals Eye Movements in Map Comparison - Preliminary Results and Lessons Learned

2021 ◽  
Author(s):  
Claus Rinner ◽  
Susanne Ferber
Keyword(s):  
Decision Making ◽  
Eye Movements ◽  
Spatial Data ◽  
Fixation Duration ◽  
Neuropsychological Testing ◽  
Lessons Learned ◽  
Spatial Data Analysis ◽  
Gis Technology ◽  
Map Comparison ◽  
Movement Parameters

Comparing maps of different geographic phenomena, or maps of the same phenomenon at different points in time, is an important task in spatial data analysis and decision-making. The process of map comparison has been studied occasionally by cartographers since the 1970s, but recent improvements in neuropsychological testing equipment and GIS technology had us review this topic in a new light. In a pilot experiment, we presented pairs of maps to volunteer participants and recorded their eye movements while judging the maps’ similarity. We analysed average values of eye movement parameters such as fixation duration and proportions of saccades between the two maps in relation to three factors: the participant’s experience in reading maps; the type of map presented; and the actual similarity between the two maps. We found, for example, that different map types engaged viewers in different comparison strategies while we did not find behavioural differences between expert and novice map readers. We will speculate about implications of experimental cartography for GIS design and report on challenges encountered with this approach.

scholarly journals Lessons Learned from Development of a Mobile App for Cardiovascular Health Awareness

2021 ◽  
Vol 13 (11) ◽  
pp. 5985
Author(s):  
Bryan Weichelt ◽  
Jeffrey VanWormer ◽  
Yin Xu ◽  
Chris Kadolph ◽  
Simon Lin
Keyword(s):  
Cardiovascular Disease ◽  
Health Information ◽  
Healthcare System ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
The United States ◽  
Mobile App ◽  
Lessons Learned ◽  
Health Concern ◽  
Development Processes

Cardiovascular disease (CVD) is a major public health concern in the United States. In response to the federally sponsored Million Hearts Risk Check Challenge, a team of programmers, software developers, health-information technologists, and clinicians in an integrated healthcare system in Wisconsin collaborated to develop Heart Health MobileTM (HHM), designed to improve awareness of cardiovascular disease risk and promote risk factor control among users. This paper outlines the development processes and highlights key lessons learned for mobile health applications. An agile project management methodology was used to dedicate adequate resources and employ adaptive planning and iterative development processes with a self-organized, cross-functional team. The initial HHM iOS app was developed and tested, and after additional modifications, gamified and HTML 5 versions of the app were released. The development of an iOS app is low in cost and sustainable by a healthcare system. Future app modifications to enhance data security and link self-reported cardiovascular risk assessment data to patient medical records may improve performance, patient relevance, and clinician acceptance of HHM in the primary-care setting. Legal and institutional barriers regarding the capture and analyses of protected health information must be mitigated to fully capture, analyze, and report patient health outcomes for future studies.

scholarly journals Research on Spatiotemporal Differentiation and Influence Mechanism of Urban Resilience in China Based on MGWR Model

2021 ◽  
Vol 18 (3) ◽  
pp. 1056
Author(s):  
Yu Chen ◽  
Mengke Zhu ◽  
Qian Zhou ◽  
Yurong Qiao
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Correlation ◽  
Spatial Data ◽  
Central Area ◽  
Urban System ◽  
Entropy Method ◽  
Driving Factors ◽  
Spatial Data Analysis ◽  
Eastern Region ◽  
Urban Resilience

Urban resilience in the context of COVID-19 epidemic refers to the ability of an urban system to resist, absorb, adapt and recover from danger in time to hedge its impact when confronted with external shocks such as epidemic, which is also a capability that must be strengthened for urban development in the context of normal epidemic. Based on the multi-dimensional perspective, entropy method and exploratory spatial data analysis (ESDA) are used to analyze the spatiotemporal evolution characteristics of urban resilience of 281 cities of China from 2011 to 2018, and MGWR model is used to discuss the driving factors affecting the development of urban resilience. It is found that: (1) The urban resilience and sub-resilience show a continuous decline in time, with no obvious sign of convergence, while the spatial agglomeration effect shows an increasing trend year by year. (2) The spatial heterogeneity of urban resilience is significant, with obvious distribution characteristics of “high in east and low in west”. Urban resilience in the east, the central and the west are quite different in terms of development structure and spatial correlation. The eastern region is dominated by the “three-core driving mode”, and the urban resilience shows a significant positive spatial correlation; the central area is a “rectangular structure”, which is also spatially positively correlated; The western region is a “pyramid structure” with significant negative spatial correlation. (3) The spatial heterogeneity of the driving factors is significant, and they have different impact scales on the urban resilience development. The market capacity is the largest impact intensity, while the infrastructure investment is the least impact intensity. On this basis, this paper explores the ways to improve urban resilience in China from different aspects, such as market, technology, finance and government.

scholarly journals Spatial Agglomeration of China’s Forest Products Manufacturing Industry: Measurement, Characteristics and Determinants

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1006
Author(s):  
Zhenhuan Chen ◽  
Hongge Zhu ◽  
Wencheng Zhao ◽  
Menghan Zhao ◽  
Yutong Zhang
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Economies Of Scale ◽  
Forest Products ◽  
Spatial Data Analysis ◽  
Forest Protection ◽  
Spatial Agglomeration ◽  
Continuous Space

China’s forest products manufacturing industry is experiencing the dual pressure of forest protection policies and wood scarcity and, therefore, it is of great significance to reveal the spatial agglomeration characteristics and evolution drivers of this industry to enhance its sustainable development. Based on the perspective of large-scale agglomeration in a continuous space, in this study, we used the spatial Gini coefficient and standard deviation ellipse method to investigate the spatial agglomeration degree and location distribution characteristics of China’s forest products manufacturing industry, and we used exploratory spatial data analysis to investigate its spatial agglomeration pattern. The results show that: (1) From 1988 to 2018, the degree of spatial agglomeration of China’s forest products manufacturing industry was relatively low, and the industry was characterized by a very pronounced imbalance in its spatial distribution. (2) The industry has a very clear core–periphery structure, the spatial distribution exhibits a “northeast-southwest” pattern, and the barycenter of the industrial distribution has tended to move south. (3) The industry mainly has a high–high and low–low spatial agglomeration pattern. The provinces with high–high agglomeration are few and concentrated in the southeast coastal area. (4) The spatial agglomeration and evolution characteristics of China’s forest products manufacturing industry may be simultaneously affected by forest protection policies, sources of raw materials, international trade and the degree of marketization. In the future, China’s forest products manufacturing industry should further increase the level of spatial agglomeration to fully realize the economies of scale.

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