scholarly journals A GIS Application to Explore Postal Retail Outlet Locations

1970 ◽  
Vol 24 (2) ◽  
pp. 161-168 ◽  
Author(s):  
Nikola Trubint
Keyword(s):  
Spatial Data ◽  
Spatial Data Analysis ◽  
Location Analysis ◽  
Retail Outlet ◽  
Analysis Model ◽  
Joint Project ◽  
Post Corporation ◽  
Project Location ◽  
Gis Software

The use of GIS in solving a wide variety of problems in postal operations is expanding. This approach provides the development and usage of new methods in spatial data analysis, as support in achieving a better quality of the decision-making process. The use of location analysis model based on GIS software is implemented in solving the Belgrade postal retail outlet problem. One of the most important experiences of model implementation is that the local environmental conditions have a significant impact on strategic as well as operational approach. A portion of the material included in the paper has resulted from the Serbian PTT and CPC (Canada Post Corporation) joint project Location Analysis.

scholarly journals Hotspot analysis: a first prototype Python plugin enabling exploratory spatial data analysis into QGIS

2016 ◽  
Author(s):  
Daniele Oxoli ◽  
Mayra A Zurbarán ◽  
Stanly Shaji ◽  
Arun K Muthusamy
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Point Of View ◽  
Geospatial Data ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Software Environment ◽  
Hotspot Analysis ◽  
Computational Procedures ◽  
Gis Software

The growing popularity of Free and Open Source (FOSS) GIS software is without doubts due to the possibility to build and customize geospatial applications to meet specific requirements for any users. From this point of view, QGIS is one of the most flexible as well as fashionable GIS software environment which enables users to develop powerful geospatial applications using Python. Exploiting this feature, we present here a first prototype plugin for QGIS dedicated to Hotspot analysis, one of the techniques included in the Exploratory Spatial Data Analysis (ESDA). These statistics aim to perform analysis of geospatial data when spatial autocorrelation is not neglectable and they are available inside different Python libraries, but still not integrated within the QGIS core functionalities. The main plugin features, including installation requirements and computational procedures, are described together with an example of the possible applications of the Hotspot analysis.

scholarly journals Identifying Spatial Relationships between Built Heritage Resources and Short-Term Rentals before the Covid-19 Pandemic: Exploratory Perspectives on Sustainability Issues

2020 ◽  
Vol 12 (11) ◽  
pp. 4533 ◽  
Author(s):  
Irene Rubino ◽  
Cristina Coscia ◽  
Rocco Curto
Keyword(s):  
Spatial Data ◽  
Spatial Data Analysis ◽  
Spatial Relationships ◽  
Short Term ◽  
Historic Center ◽  
Built Heritage ◽  
Socio Economic Variables ◽  
City Relations

Built heritage resources (BHRs) are multidimensional assets that need to be conceived under a sustainability and circular economy framework. Whereas it is essential that their conservation, management, and enjoyment are sustainable, it is also necessary that the environmental, cultural, and socio-economic contexts in which they are integrated are sustainable too. Like other amenities, BHRs can improve the quality of the urban environment and generate externalities; additionally, they may influence sectors such as real estate, hospitality, and tourism. In this framework, this contribution aims to identify spatial relationships occurring between BHRs and short-term rentals (STRs), i.e., a recent economic phenomenon facilitated by platforms such as Airbnb. Through the application of Exploratory Spatial Data Analysis techniques and taking Turin (Italy) as a case study, this article provides evidence that spatial correlation patterns between BHRs and STRs exist, and that the areas most affected by STRs are the residential neighborhoods located in the proximity of the historic center of the city. Relations with other sets of socio-economic variables are highlighted too, and conclusions suggest that future studies are essential not only to monitor sustainability issues and reflect on new housing models and sustainable uses of buildings, but also to understand the evolution of the phenomenon in light of the pandemic Covid-19.

scholarly journals Hotspot analysis: a first prototype Python plugin enabling exploratory spatial data analysis into QGIS

2016 ◽  
Author(s):  
Daniele Oxoli ◽  
Mayra A Zurbarán ◽  
Stanly Shaji ◽  
Arun K Muthusamy
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Point Of View ◽  
Geospatial Data ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Software Environment ◽  
Hotspot Analysis ◽  
Computational Procedures ◽  
Gis Software

The growing popularity of Free and Open Source (FOSS) GIS software is without doubts due to the possibility to build and customize geospatial applications to meet specific requirements for any users. From this point of view, QGIS is one of the most flexible as well as fashionable GIS software environment which enables users to develop powerful geospatial applications using Python. Exploiting this feature, we present here a first prototype plugin for QGIS dedicated to Hotspot analysis, one of the techniques included in the Exploratory Spatial Data Analysis (ESDA). These statistics aim to perform analysis of geospatial data when spatial autocorrelation is not neglectable and they are available inside different Python libraries, but still not integrated within the QGIS core functionalities. The main plugin features, including installation requirements and computational procedures, are described together with an example of the possible applications of the Hotspot analysis.

scholarly journals Hotspot analysis: a first prototype Python plugin enabling exploratory spatial data analysis into QGIS

2016 ◽  
Author(s):  
Daniele Oxoli ◽  
Mayra A Zurbarán ◽  
Stanly Shaji ◽  
Arun K Muthusamy
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Point Of View ◽  
Geospatial Data ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Software Environment ◽  
Hotspot Analysis ◽  
Computational Procedures ◽  
Gis Software

The growing popularity of Free and Open Source (FOSS) GIS software is without doubts due to the possibility to build and customize geospatial applications to meet specific requirements for any users. From this point of view, QGIS is one of the most flexible as well as fashionable GIS software environment which enables users to develop powerful geospatial applications using Python. Exploiting this feature, we present here a first prototype plugin for QGIS dedicated to Hotspot analysis, one of the techniques included in the Exploratory Spatial Data Analysis (ESDA). These statistics aim to perform analysis of geospatial data when spatial autocorrelation is not neglectable and they are available inside different Python libraries, but still not integrated within the QGIS core functionalities. The main plugin features, including installation requirements and computational procedures, are described together with an example of the possible applications of the Hotspot analysis.

scholarly journals Hotspot analysis: a first prototype Python plugin enabling exploratory spatial data analysis into QGIS

2016 ◽  
Author(s):  
Daniele Oxoli ◽  
Mayra A Zurbarán ◽  
Stanly Shaji ◽  
Arun K Muthusamy
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Point Of View ◽  
Geospatial Data ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Software Environment ◽  
Hotspot Analysis ◽  
Computational Procedures ◽  
Gis Software

The growing popularity of Free and Open Source (FOSS) GIS software is without doubts due to the possibility to build and customize geospatial applications to meet specific requirements for any users. From this point of view, QGIS is one of the most flexible as well as fashionable GIS software environment which enables users to develop powerful geospatial applications using Python. Exploiting this feature, we present here a first prototype plugin for QGIS dedicated to Hotspot analysis, one of the techniques included in the Exploratory Spatial Data Analysis (ESDA). These statistics aim to perform analysis of geospatial data when spatial autocorrelation is not neglectable and they are available inside different Python libraries, but still not integrated within the QGIS core functionalities. The main plugin features, including installation requirements and computational procedures, are described together with an example of the possible applications of the Hotspot analysis.

scholarly journals The Improvement of the spatial structure and quality of rural communication system in loess upland watershed

2010 ◽  
Vol 42 (1) ◽  
pp. 169-176
Author(s):  
Rafał Wawer ◽  
Eugeniusz Nowocień ◽  
Bugusław Podolski ◽  
Szymon Szewrański ◽  
Romuald Żmuda
Keyword(s):  
Spatial Structure ◽  
Spatial Data ◽  
Communication System ◽  
Actual State ◽  
Rural Roads ◽  
The Road ◽  
Improvement Measures ◽  
Gis Software ◽  
Upland Watershed

The Improvement of the spatial structure and quality of rural communication system in loess upland watershed In strongly eroded terrains, a considerable part of agricultural roads are transformed into ravines, as a result of their inappropriate location in the terrain relief. Hence the arrangement and hardening of rural roads remains an essential component of formation of arable areas on eroded terrains. Correct derivation of the roads, which need the protective measures or even re-projecting improves the state of the road network in economically effective way, as it allocates the available resources to the most threatened areas. The article presents a study on the actual state of the net of rural roads on loess upland terrain of Mielnica watershed. The analysis, performed on digital spatial data within GIS software, revealed considerable needs for improvements of rural roads on investigated area. Basing on the results, the map of improvement measures has been developed, presenting a spatial base for a decision support system for future land improvements and allocation of economical resources for the management of rural surface communication network in rural-environmental plans.

scholarly journals ASSESSING SPATIAL DATA QUALITY OF PARTICIPATORY GIS STUDIES: A CASE STUDY IN CAPE TOWN

2015 ◽  
Vol II-2/W2 ◽  
pp. 75-82 ◽  
Author(s):  
K. Musungu
Keyword(s):  
Data Quality ◽  
Spatial Data ◽  
Quality Criteria ◽  
Participatory Gis ◽  
Use Of Data ◽  
Starting Point ◽  
Gis Data ◽  
Gis Software

Participatory GIS (PGIS) has been prescribed by scholars who sought to find a means to enable more equitable access to GIS data, diversifying the types of knowledge captured by a GIS and re-engineering GIS software. The popularity of PGIS is evident in the various studies and contexts in which it has been utilised. These include studies in risk assessment, land administration, resource management, crime mapping and urban design to mention but a few. Despite the popularity of PGIS as a body of research, little has been done in the analysis of the quality of PGIS information. The study investigated the use of data quality criteria commonly used in traditional GIS systems and shows that it is possible to apply the criteria used in traditional GIS to PGIS. It provides a starting point for PGIS studies to assess the quality of the product. Notably, this a reflective exercise on one case study, but the methodologies used in this study have been replicated in many others undertaken by Community Based Organisations as well as Non-Governmental Organisations. Therefore the findings are relevant to such projects.

scholarly journals Inequalities in the spatial concentration of agricultural crops among the micro- and mesoregions of Minas Gerais, Brazil

2020 ◽  
Vol 50 (5) ◽  
Author(s):  
Erica Costa Rodrigues ◽  
Ricardo Tavares ◽  
Adriana Lúcia Meireles
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Minas Gerais ◽  
The State ◽  
Spatial Data Analysis ◽  
P Value ◽  
Agricultural Crops ◽  
Analysis Model ◽  
Modernization Process ◽  
Local Moran

ABSTRACT: The present research aimed to map and estimate the spatial autocorrelation of agricultural crops of coffee, corn, soybeans, sugarcane and beans in the state of Minas Gerais and analyzed in the period from 2011 to 2015. The planted area data were obtained of the Systematic Survey of Agricultural Production - IBGE. The Exploratory Spatial Data Analysis model was used to calculate spatial autocorrelation using the Global and Local Moran Index. Significant spatial self-correction was reported in all studied cultures (P-value <0.05). The regions with the highest concentration of planted area are located in the western portion of the state. The least significant planting regions were the municipalities located in the Jequitinhonha and Vale do Mucuri regions. The results pointed to a micro and mesoregional inequality in the distribution of agricultural activities in the mining territory that seems to reflect the incomplete agricultural modernization process that occurred in the state in the 70 s and 80 s.

scholarly journals Development of a Spatial Path-Analysis Method for Spatial Data Analysis

2018 ◽  
Vol 8 (4) ◽  
pp. 2456
Author(s):  
Wiwin Sulistyo ◽  
Subanar Subanar ◽  
Reza Pulungan
Keyword(s):  
Path Analysis ◽  
Spatial Data ◽  
Method Development ◽  
Spatial Regression ◽  
Spatial Data Analysis ◽  
Analysis Method ◽  
Analysis Model ◽  
Process Data ◽  
Spatial Dependencies ◽  
Indirect Relationship

Path analysis is a method used to analyze the relationship between independent and dependent variables to identify direct and indirect relationship between them. This method is developed by Sewal Wright and initially only uses correlation analysis results in identifying the variables' relationship. Path analysis method currently is mostly used to deal with variables with non-spatial data type. When analyzing variables that have elements of spatial dependency, path analysis could result in a less precise model. Therefore, it is necessary to build a path analysis model that is able to identify and take into account the effects of spatial dependencies. Spatial autocorrelation and spatial regression methods can be used to develop path analysis method so as to identify the effects of spatial dependencies. This paper proposes a method in the form of path analysis method development to process data that have spatial elements. This study also discusses our effort on establishing a method that could be used to identify and analyze the spatial effect on data in the framework of path analysis; we call this method spatial path analysis.

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