Special Issue "Spationomy—Spatial Exploration of Economic Data"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 November 2020).

Special Issue Editors

Dr. Vít Pászto
E-Mail Website
Guest Editor
Palacký University Olomouc, Faculty of Science, Department of Geoinformatics, 17. listopadu 50, 771 46 Olomouc, Czech Republic
Interests: GIScience; spatial information; human and economic geography; spatial analysis; economic data; geocomputation
Prof. Dr. Carsten Juergens
E-Mail Website
Guest Editor
Ruhr-Universität Bochum, Faculty of Geosciences, Geography Department, Geomatics Group, Universitätsstraße 150/ Gebäude IA, 44801 Bochum, Germany
Interests: remote sensing/earth observation; digital image processing; change detection analysis; photogrammetry; interdisciplinary remote sensing and GIS applications
Assoc. Prof. Dr. Jarmila Zimmermannová
E-Mail Website
Guest Editor
Moravian Business College Olomouc, Department of Economics, tř. Kosmonautů 1288, 779 00 Olomouc, Czech Republic
Interests: environmental economics; microeconomics; macroeconomics; economic policy; environmental policy; public administration and governance

Special Issue Information

Dear Colleagues,

Economic data analysis is a very important part in a decision-making process. Today, the importance of the geospatial component inherent in most economic data is rapidly increasing. Therefore, the added value of introducing geospatial aspects to economic data analyses is highly appreciated. Many economists are implementing the spatial aspect more and more in their research, which creates strong ties between economy and GISscience (and related disciplines); and vice versa.

This Special Issue strives to establish a scientific platform for sharing results of an empirical research in the field of “spatial economy”. We invite all contributions that focus on spatial exploration and explanation of (socio)economic data of a contemporary world. Paper submission may be focused regionally as well as at different geographical scales, prefarably using quantitative data-driven or methods-driven approaches. Possible application areas include (but are not limited to):

- Social economy, demography, human capital;

- Spatial distribution of economic activities;

- Urban environment, cities, and spatial planning in cities;

- Spatial and economic evaluation of regional disparities;

- Spatial and economic development of rural areas;

- Health geography and its economical aspects;

- Transportation and economic commuting;

- Small and medium entrepreneurship;

- Environmental issues and spatial economy;

- Renewable energy and green policies;

- Innovation activities and R&D;

- Quality of life.

Dr. Vít Pászto
Prof. Dr. Carsten Juergens
Assoc. Prof. Dr. Jarmila Zimmermannová
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spationomy
  • spatial analysis
  • GIScience
  • economic data
  • economic geography
  • quantitative research

Published Papers (8 papers)

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Research

Open AccessArticle
Spationomy Simulation Game—Playful Learning in Spatial Economy Higher Education
ISPRS Int. J. Geo-Inf. 2021, 10(2), 74; https://doi.org/10.3390/ijgi10020074 - 13 Feb 2021
Viewed by 433
Abstract
Simulation games, as a method of playful learning, have been used for more than 70 years in various disciplines with the economy as a leading application field. Their development has been tied with advances in computer science, and nowadays, hundreds of simulation games [...] Read more.
Simulation games, as a method of playful learning, have been used for more than 70 years in various disciplines with the economy as a leading application field. Their development has been tied with advances in computer science, and nowadays, hundreds of simulation games exist. However, simulation games are not just useful for encouraging disciplinary knowledge production; they also promise to be effective tools for interdisciplinary collaboration. To further explore these promises, we report on the design and playing of a simulation game on the boundary of geoinformatics and business and economics; an interdisciplinary field we have termed Spationomy. Within this game, students from different disciplinary (and cultural) backgrounds applied their knowledge and skills to tackle interdisciplinary problems. In this paper, we also analyze students’ feedback on the game to complement this aspect. The main goal is to discuss the design process that went into creating the game as well as experiences from play sessions in relation to this increase of interdisciplinary knowledge among students. In the end, we present a new gaming concept based on real-world data that can be played in other interdisciplinary situations. Here, students´ feedback on individual features of the game helped to identify future directions in the development of our simulation game. Full article
(This article belongs to the Special Issue Spationomy—Spatial Exploration of Economic Data)
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Open AccessArticle
Geographical and Economic Factors Affecting the Spatial Distribution of Micro, Small, and Medium Enterprises: An Empirical Study of The Kujawsko-Pomorskie Region in Poland
ISPRS Int. J. Geo-Inf. 2020, 9(7), 426; https://doi.org/10.3390/ijgi9070426 - 01 Jul 2020
Viewed by 777
Abstract
Micro, small, and medium-sized enterprises (MSMEs) are an essential part of economies at the national, regional, and local levels. Understanding the determinants of the development of this sector is interesting not only for researchers but also for local governments to support the development [...] Read more.
Micro, small, and medium-sized enterprises (MSMEs) are an essential part of economies at the national, regional, and local levels. Understanding the determinants of the development of this sector is interesting not only for researchers but also for local governments to support the development of this sector. This paper analyses micro, small, and medium enterprises at the gmina (local) level in one region, the Kujawsko-Pomorskie voivodship (NUTS2) in Poland. The authors use multivariate linear regression, spatial econometrics, and classification trees to model the influence of different factors on the number of enterprises relative to population size. The authors found that the most crucial factor in all cases, independently of the method used, is the local government’s revenue from personal income tax per capita. This finding, together with the lack of significance of variables related to the distance to technological parks or economic zones, indicates that the enterprises in the region produce mainly for local consumption and lack innovativeness. The authors also examined the influence of spatial context on the number of enterprises. The most important factor seems to be the percentage of built-up areas, but there are also others, depending on the model type; again, this confirms the local character of the activity of micro, small, and medium enterprises in the region. Variables representing the spatial context can explain the relative number of enterprises with coefficient of determination (R2) between 0.30 and 0.45, which shows that this context played a relatively significant role in the development of the MSME sector in the region. On the other hand, the econometric models (that include the neighborhood) are only significant (improving R2) for medium enterprises, which means that medium enterprises expand their activity beyond the local range. Full article
(This article belongs to the Special Issue Spationomy—Spatial Exploration of Economic Data)
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Open AccessArticle
Spatial Exploration of Economic Data—Insight into Attitudes of Students towards Interdisciplinary Knowledge
ISPRS Int. J. Geo-Inf. 2020, 9(7), 421; https://doi.org/10.3390/ijgi9070421 - 30 Jun 2020
Cited by 1 | Viewed by 648
Abstract
This paper uses the technology acceptance model (TAM) framework for the research of economic and geography students’ attitudes towards interdisciplinary knowledge. Based on the SmartPLS Structural equation modelling SEM variance-based method, research results were gained through analysis of survey data of economic and [...] Read more.
This paper uses the technology acceptance model (TAM) framework for the research of economic and geography students’ attitudes towards interdisciplinary knowledge. Based on the SmartPLS Structural equation modelling SEM variance-based method, research results were gained through analysis of survey data of economic and geography students. They participated in the Spationomy project in the period of 2017–2019. Online questionnaires were fulfilled before and after students’ participation in the project and their future behavioural intention to use interdisciplinary knowledge was analysed. Based on the research, we can confirm that the Spationomy project has achieved its purpose, as both groups of students (economic and geography students) have acquired interdisciplinary knowledge and students intend to use it also in the future. Therefore, we can argue that the students included in the project in practice gained recognition of systems thinking about the importance of mutual interdisciplinary cooperation towards achieving synergies. The results also show that TAM can be successfully implemented to analyse how students of economics and geography accept the use of interdisciplinary knowledge in the learning process, which is an important implication for management and education as well as from the theoretical implications viewpoint. While effective analysis using TAM has been used successfully and relatively frequently in economics and business field, we have not found relevant examples of its implementation in the broader field of geography. However, the acceptance of geographic information system (GIS) or other information technologies/information software (IT/IS) tool-based approaches of analysis in the geography field may be of most importance. Therefore, also, this represents an important implication for the research area. Full article
(This article belongs to the Special Issue Spationomy—Spatial Exploration of Economic Data)
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Open AccessArticle
Spatial Dimension of Unemployment: Space-Time Analysis Using Real-Time Accessibility in Czechia
ISPRS Int. J. Geo-Inf. 2020, 9(6), 401; https://doi.org/10.3390/ijgi9060401 - 18 Jun 2020
Cited by 2 | Viewed by 749
Abstract
This paper focuses on the analysis of unemployment data in Czechia on a very detailed spatial structure and yearly, extended time series (2002–2019). The main goal of the study was to examine the spatial dimension of disparities in regional unemployment and its evolutionary [...] Read more.
This paper focuses on the analysis of unemployment data in Czechia on a very detailed spatial structure and yearly, extended time series (2002–2019). The main goal of the study was to examine the spatial dimension of disparities in regional unemployment and its evolutionary tendencies on a municipal level. To achieve this goal, global and local spatial autocorrelation methods were used. Besides spatial and space-time analyses, special attention was given to spatial weight matrix selection. The spatial weights were created according to real-time accessibilities between the municipalities based on the Czech road network. The results of spatial autocorrelation analyses based on network spatial weights were compared to the traditional distance-based spatial weights. Despite significant methodological differences between applied spatial weights, the resulting spatial pattern of unemployment proved to be very similar. Empirically, relative stability of spatial patterns of unemployment with only slow shift of differentiation from macro- to microlevels could be observed. Full article
(This article belongs to the Special Issue Spationomy—Spatial Exploration of Economic Data)
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Open AccessArticle
Sustainable Spatial and Temporal Development of Land Prices: A Case Study of Czech Cities
ISPRS Int. J. Geo-Inf. 2020, 9(6), 396; https://doi.org/10.3390/ijgi9060396 - 16 Jun 2020
Cited by 1 | Viewed by 906
Abstract
Only a limited number of studies have examined land price issues based on official land price maps. A very unique timeline of official land price maps (2006–2019) allowed research to be conducted on four Czech cities (Prague, Olomouc, Ostrava, and Zlín). The main [...] Read more.
Only a limited number of studies have examined land price issues based on official land price maps. A very unique timeline of official land price maps (2006–2019) allowed research to be conducted on four Czech cities (Prague, Olomouc, Ostrava, and Zlín). The main aim of the research was to describe the links between land price, land use types, and macroeconomic indicators, and to compare temporal changes of these links in four cities of different size, type, and structure by using spatial data processing and regression analysis. The results showed that the key statistically significant variable in all cities was population size. The effect of this variable was mostly positive, except for Ostrava, as an example of a developing city. The second statistically significant variable affecting land prices in each city was discount rate. The effect of other variables differed according to the city, its characteristics, and stage of economic development. We concluded that the development of land prices over time was slightly different between the studied cities and partially dependent on local spatial factors. Nevertheless, stagnation in 2010–2011, probably as a consequence of the global economic crisis in 2009, was observed in each city. Changes in the monitored cities could be seen from a spatial point of view in similar land price patterns. The ratio of land area with rising prices was very similar in each city (85%–92%). The highest land prices were typically in urban centers, but prices rose only gradually. A much more significant increase in prices occurred in each city in their peripheral residential areas. The results of this study can improve understanding of urban development and the economic and spatial aspects of sustainability in land price changes. Full article
(This article belongs to the Special Issue Spationomy—Spatial Exploration of Economic Data)
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Open AccessArticle
Digital Data Literacy in an Economic World: Geo-Spatial Data Literacy Aspects
ISPRS Int. J. Geo-Inf. 2020, 9(6), 373; https://doi.org/10.3390/ijgi9060373 - 06 Jun 2020
Cited by 2 | Viewed by 1019
Abstract
Data literacy is an essential skill for today’s digital way of life, to be able to judge the reliability of different data presented, for instance in news and media or in business processes. Geo-Spatial data are a specific kind of data and influence [...] Read more.
Data literacy is an essential skill for today’s digital way of life, to be able to judge the reliability of different data presented, for instance in news and media or in business processes. Geo-Spatial data are a specific kind of data and influence most of our daily decisions. In school one learns how to critically read and reflect texts and literature. So one is literate in the case of textual data. For other data, and especially geo-spatial data, one seems to be less skilled. This contribution is supposed to open one’s eyes to understand the origin of geo-spatial data sets, their specific nature and how to gain geo-spatial data sets with specific focus on economic applications. In addition to that, how the selection of geo-spatial data and the processing of geo-spatial data can influence the decision-making of people in thematic fields such as economy and business is discussed. The overall goal is to make people of disciplines other than those that are geo-related aware of the characteristics and possible ways of manipulation of maps and geo-spatial products as well as of the power of geo-spatial data and map products in their specific thematic field of operation. Full article
(This article belongs to the Special Issue Spationomy—Spatial Exploration of Economic Data)
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Open AccessArticle
Subjective or Objective? How Objective Measures Relate to Subjective Life Satisfaction in Europe
ISPRS Int. J. Geo-Inf. 2020, 9(5), 320; https://doi.org/10.3390/ijgi9050320 - 12 May 2020
Cited by 1 | Viewed by 725
Abstract
Quality of life and life satisfaction are topics that currently receive a great deal of attention across the globe. Many approaches exist, which use both qualitative and quantitative methods, to capture these phenomena. Historically, quality of life was measured exclusively by economic indicators. [...] Read more.
Quality of life and life satisfaction are topics that currently receive a great deal of attention across the globe. Many approaches exist, which use both qualitative and quantitative methods, to capture these phenomena. Historically, quality of life was measured exclusively by economic indicators. However, it is indisputable that other factors influence people’s life satisfaction, which is captured by subjective survey-based data. By contrast, objective data can easily be obtained and cover a wider range, in terms of population and area. In this research, the multiple fuzzy linear regression model is applied in order to explain the relationship between subjective life satisfaction and selected objective indicators used to evaluate quality of life. The great advantage of the fuzzy model lies in its ability to capture uncertainty, which is undoubtedly associated with the vague concept of subjective life satisfaction. The main outcome of the paper is the detection of indicators that have a statistically significant relationship with life satisfaction. Subsequently, a pan-European sub-national prediction of life satisfaction after the consideration of the most relevant input indicators was proposed, including the uncertainty associated with the prediction of such a phenomenon. The study revealed significant regional differences and similarities between the originally reported satisfaction of life and the predicted one. With the help of spatial and non-spatial statistics supported by visual analysis, it is possible to assess life satisfaction more precisely, while taking into account the ambiguity of the perception of life satisfaction. Additionally, predicted values supplemented with the uncertainty measure (fuzzy approach) and the synthesis of results in the form of European typology help to compare and contrast the results in a more useful manner than in existing studies. Full article
(This article belongs to the Special Issue Spationomy—Spatial Exploration of Economic Data)
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Open AccessArticle
Missing Data Imputation for Geolocation-based Price Prediction Using KNN–MCF Method
ISPRS Int. J. Geo-Inf. 2020, 9(4), 227; https://doi.org/10.3390/ijgi9040227 - 08 Apr 2020
Cited by 2 | Viewed by 765
Abstract
Accurate house price forecasts are very important for formulating national economic policies. In this paper, we offer an effective method to predict houses’ sale prices. Our algorithm includes one-hot encoding to convert text data into numeric data, feature correlation to select only the [...] Read more.
Accurate house price forecasts are very important for formulating national economic policies. In this paper, we offer an effective method to predict houses’ sale prices. Our algorithm includes one-hot encoding to convert text data into numeric data, feature correlation to select only the most correlated variables, and a technique to overcome the missing data. Our approach is an effective way to handle missing data in large datasets with the K-nearest neighbor algorithm based on the most correlated features (KNN–MCF). As far as we are concerned, there has been no previous research that has focused on important features dealing with missing observations. Compared to the typical machine learning prediction algorithms, the prediction accuracy of the proposed method is 92.01% with the random forest algorithm, which is more efficient than the other methods. Full article
(This article belongs to the Special Issue Spationomy—Spatial Exploration of Economic Data)
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