When Competitive Intelligence Meets Geospatial Intelligence

Given current economic uncertainties, organizations need to efficiently generate new knowledge and incorporate it into their products and services. In this regard, competitive intelligence provides such strategic knowledge to support the creation of competitive advantages. Part of the knowledge created by competitive intelligence concerns the geographical components forming the basis of geospatial analysis. This study sought to identify, among some 40 competitive analytical methods, those that may be enhanced by geospatial intelligence capabilities. To this end, the authors identified the key subject area of each competitive analytical method and the key spatial analysis operations with geospatial intelligence capability, and then they identified those with a strong potential for providing more strategic knowledge when used in combination. They provide various case scenarios to support their propositions. The results demonstrate that geospatial intelligence may leverage competitive intelligence by more efficiently highlighting business opportunities.

The Fundamentals and Applications of Geo-Spatial Research

This chapter is about the fundamentals of geo-spatial research. The Earth's make-up and position in the entirety of the universe and its systems thereof is revealed. It also categorizes the Earth movements into types, causative effects, and their measurable, predictable time beat. It resonates together with Chapter 2 to form a bigger picture. The scenario draws out whole complete discussions of geoscience study on the origins of matter, space, time and energy entities. The revelations of what is known about the Cosmos today and therefore the Universe is the painstaking work of several scientists. This knowledge is fundamental to all Geo-spatial science research. For one to successfully carry out the research of this nature, it is imperative that one is fully conversant with how the Universe and therefore the Earth and its systems function. The discussions also include a map as a reporting platform for processes of the geospatial science research.

Quantifying Urban Sprawl With Spatial Autocorrelation Techniques Using Multi-Temporal Satellite Data

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

Research in Agricultural and Environmental Information Systems

Information systems relate to diverse applications, but, until recently, the use of this technology in agriculture and environment has been relatively behind the applications in the industrial sector. The publication of IJAEIS started in 2010 in order to promote the new research advances in information systems applied to agriculture and environment. This paper presents an overview of the different scientific issues presented in the 50 papers published in IJAEIS between 2010 and 2013. The authors summarize the different contributions presented in IJAEIS and the authors identify the main trends in the field of agricultural and environmental information systems (ontologies, communication systems, spatial information processing, etc.).

Detection of Urban Expansion by Using DMSP-OLS, Landsat Data and Linear Spectral Unmixing Method

Urbanization has been increasingly continuing in Turkey and in the world for the last 30 years. Especially for the developing countries, urbanization is a necessary fact for the sustainability of the urban growth. Yet, this growth should be controlled and planned; otherwise, many environmental problems might occur. Therefore, the urban areas having dynamic structure should be monitored periodically. Monitoring the changes in urban environment can be provided with land cover land use (LCLU) maps produced by the pixel based classification methods using ‘maximum likelihood' and ‘isodata' techniques. However, these thematic maps might bring about inaccurate classification results in heterogeneous areas especially where low spatial resolution satellite data is used since, in these approaches, each pixel is represented with only one class value. In this study, considering the spectral mixture analysis (SMA) each pixel is represented by endmember fractions. The earth is represented more accurately using 'substrate (S)', ‘green vegetation (V)' and ‘dark surfaces (D)' spectral endmember reflectances with this analysis based on linear mixture model. Here, the surrounding of Izmit Gulf, one of the most industrialized areas of Turkey, has been chosen as the study area. SMA has been applied to LANDSAT images of the years of 1984, 1999 and 2009. In addition, DMSP-OLS data of 1992, 1999 and 2009 has been used to detect urban areas. According to the results, the changes in LCLU and especially the urban growth areas have been detected accurately using the SMA method.

FogLearn

Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This article discusses the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This article proposes and develops a fog computing-based framework, i.e. FogLearn. This is for the application of K-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes mellitus. The proposed architecture employs machine learning on a deep learning framework for the analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results show that fog computing holds an immense promise for the analysis of medical and geospatial big data.

Use of Cloud, Multimedia, and QR Codes to Enhance Print Maps

Quick response (QR) codes are two-dimensional codes. They are machine-readable optical labels which contain information of the object on which they are attached. Due to their large storage capacity and faster readability, they are becoming more popular than one-dimensional bar codes. They are widely used in creation of inventories, product tracking, document management, etc. With the widespread use of smart phones, QR codes are becoming more popular. QR code interpreting apps are freely available and all modern smartphones are equipped with a camera and internet to process and interpret these codes. The value of printed maps can also be enhanced by using QR codes. This chapter reviews how the value of printed or hard copy maps can be enhanced by adding QR codes that have multimedia content placed on the Cloud.

On the Intersection Between Speaker Installations and Urban Environments

This chapter deals with speaker installations and the potential to use such installations for designing soundscapes in cities. Through employment of a designer's perspective, eight intersections between speaker sounds and the environment in which they are installed are brought forward and discussed. The intersections were originally deduced by the author theoretically but have subsequently also been examined in relation to existing speaker installations. This chapter describes and exemplifies each of the eight intersections, which have been denoted as sound sculpture, sound space, atmospheric design, sound and light, sound binocular, sound postcard, interactive event, and retuning of soundscape. Discussions in the chapter cover the role of speaker-induced sound in relation to the notion of acousmatics as well as urban design.

The Use of Social Media for Urban Planning

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

Characterization of Fire Regime Descriptors in Botswana Using Remotely Sensed Data

Despite its role and relevance in environmental management at all scales the use of fire has been contentious. The absence of information on fire parameters compounds the situation. This study derives fire parameter information for Botswana by analyzing MODIS fire data for (2001-2012), using conditional statements, and cluster mapping in ArcGIS. The study also related the fire information to other variables to examine how they interact with fire. The results of the study indicates that over the 12 year period the burned area has exhibited an upward trend. It has also shown that most of the fire in the country occur over the late dry season when the fires are potentially destructive. A south-north transect of fire frequency is observed, accompanied by an inverse relationship between frequency and intensity. Of all the factors, rainfall (0.638) and biomass(NDVI) (0.355) were the most significant contributors to the fire activity. The study demonstrated the utility of the MODIS fire data in characterizing the fire regime of the country and thus contribute to the policy process.