Geospatial Intelligence

Crime Prevention Through Environmental Design (CPTED) is an approach to understanding crime which focuses on the built environment and geography instead of simply a potential offender’s behavior or socio-economic characteristics. The theory of CPTED proposes that characteristics of the built environment can lower or increase the risk of crime at a location, based on the principles of Natural Surveillance, Natural Access Control, Territoriality, and Image. For this study, I investigated how CPTED characteristics had an impact on the property crime rate in Beaumont, Texas. I observed six Beaumont neighborhoods, and their houses and other buildings were rated according to an audit list I developed, consisting of 12 quantifiable CPTED characteristics based on the four principles. The houses and buildings of the six neighborhoods were rated by their individual parcels, according to a 0-4 based scale. These rated parcels were aggregated to a combined total of 134 residential blocks in ArcGIS, with each block being assigned the average of the CPTED ratings of the combined parcels. Maps of the six neighborhoods and their blocks were created which showed their vulnerability to crime, based on CPTED characteristic ratings. In addition to the CPTED vulnerability maps, I also developed maps which showed actual crime rates, which rated residential blocks within neighborhoods based on the number of crimes per 1,000 houses. The maps and spreadsheet data from the audit were used to determine the relationship of CPTED characteristics to crime rate. The research hypothesis was that neighborhoods that exhibit comparatively more CPTED characteristics will have fewer instances of crime than neighborhoods that exhibit fewer CPTED characteristics. The null hypothesis was that neighborhoods that exhibit more CPTED characteristics will not show a significant difference in crime rate compared to neighborhoods with fewer CPTED characteristics. The data was collected by slowly traversing each neighborhood by car, and using high-definition video cameras to record each individual house. The recorded video was later played back, and the CPTED residential audit was completed for each house. Information from the audit was documented in a spreadsheet, and also entered into ArcGIS software in order to create the CPTED vulnerability maps. ArcGIS was also used to create crime rate maps, based on data from the Beaumont Police Department. The neighborhoods were ranked from highest CPTED rating down to lowest CPTED rating; and from lowest crime count to highest crime count, to see if there were similar rankings in which high CPTED rating is closely paired with low crime-count. In addition to the ranking method, the CPTED ratings and crime counts for each block of the six neighborhoods were also plotted on a graph, to determine if there is a valid linear regression in which crime rate is determined by the CPTED rating. The results show that the neighborhood with the highest CPTED rating had the second lowest crime density, and the neighborhood with the lowest CPTED rating had the highest crime density. The ranks of the neighborhoods according to CPTED rating and crime count show that there likely is some relationship between these two variables. A regression analysis shows that there is an inverse relationship (y = -550.49x + 1254.5) in which an increase in the CPTED rating corresponds to a decrease in crime rate per 1,000, with an R2 value of .1805. The Spearman-Rho test indicates that there is a moderate correlation (R = .447) between CPTED rating and crime rate per 1,000, at the .05 significance level with a P value of 0.00000007. When looking at the CPTED rating maps of these neighborhoods alongside the crime count maps, many values of blocks in the CPTED maps correspond to similar values in the crime count maps. This phenomenon can better be illustrated by determining where similar rank values in CPTED rating and crime rating “overlap.” Although this process is more subjective compared to other methods, it is helpful in illuminating the areas in which the CPTED rating procedure most strongly predicted areas which are most at risk for crime, and which areas are most defended against crime. Even though this process of finding overlap was somewhat subjective, it was able to correctly predict 85 out of 134 blocks (63%) while the remaining 49 blocks (37%) do not show the relationship being sought, according to the hypothesis. The regression analysis and Spearman’s test showed this relationship in a more objective manner than the maps, illustrating that crime rate falls as CPTED ratings rise.

Geospatial intelligence is a new direction in the development of geographic information science. But besides being a scientific discipline it is also an intelligence activity and technology directed towards ensuring the support for decision making processes. It complements the conventional GIS by combining data from various sources and creating interactive environment that involves simultaneous access and input to integrated information by a group of individuals with different areas of expertise. Most prominent fields of application of geospatial intelligence are military and disaster management. This discipline is currently in early stage of development in Croatia and is almost entirely associated with Croatian participation in Multinational Geospatial Co-production Program.

Στο πλαίσιο εκπόνησης εργασίας εξαμήνου στο μάθημα Ερευνητικά Θέματα στα ΣΓΠ ανατέθηκε το διακριτό αντικείμενο Geospatial Intelligence. Σκοπός της παρούσας εργασίας είναι ο αναλυτικός προσδιορισμός της έννοιας που αποδίδεται με τον ομώνυμο όρο και μία ενήμερη ανασκόπηση του οικείου ερευνητικού πεδίου, αναφορικά με τις απαιτήσεις επίγνωσης του περιβάλλοντος, το εύρος των επιχειρησιακών εφαρμογών, και τις διε-επιστημονικές μεθοδολογίες παραγωγής, οργάνωσης, ανάλυσης, και αξιοποίησης των σχετικών πληροφοριών.

Every day, customs administrations around the world collect enormous amounts of data regarding the movement of passengers, vehicles, vessels, aircraft, cargo and currency and this stream of data is growing. With this expanding supply of data – and shrinking customs resources – a smarter way of making sense of this data is required, including mining the data to identify patterns of activity. Customs administrations could consider how policing agencies are mining their data holdings to derive meaningful intelligence and become intelligence-led organisations. It is postulated that customs administrations could learn from policing agencies (the scientific discoveries, the computing advances) and adapt developments by those agencies to the customs environment. This paper provides an overview of how policing agencies use geospatial intelligence and identifies ways that customs administrations would benefit from considering its use in collaborative border management.

Social media is increasingly taking the lead in spreading news about events happening in our societies. This phenomenon is largely driven by the advancements in technology, especially the introduction of the Web 2.0 as well as GPS-enabled devices. Now people are able to give their exact location using social media. Nevertheless, for informed decision making, data collected through social media has to be cleaned before analysis. The primary aim of this study is to investigate and evaluate the performance of geocoding systems to determine the best geocoding engine to use with Twitter data within South Africa.

The data for this study was collected using a Twitter scrapping application known as Twitter Archiver. Twitter Archiver was used to collect data within the boundaries of South Africa only. The collected data was then fed into various geocoding engines, and their performance was evaluated using certain selected criteria.
On the basis of the results of this research, it can be concluded that OpenStreetMap (OSM) is the best system to use for Twitter social media data. OSM passed the evaluation criteria and outperformed all the other engines compared in this research.

This study is relevant in identifying the opportunities in Geospatial Intelligence (GEOINT) which diminish its ability to respond to the public demand or geopolitical threats. This study has identified the majority of deficiencies stem from an insufficient talent pool to build and retain a stable workforce.

Social media generated from many individuals is playing a greater role in our daily lives and provides a unique opportunity to gain valuable insight on information flow and social networking within a society. Through data collection and analysis of its content, it supports a greater mapping and understanding of the evolving human landscape. The information disseminated through such media represents a deviation from volunteered geography, in the sense that it is not geographic information per se. Nevertheless, the message often has geographic footprints, for example, in the form of locations from where the tweets originate, or references in their content to geographic entities. We argue that such data conveys ambient geospatial information, capturing for example, people’s references to locations that represent momentary social hotspots. In this paper we address a framework to harvest such ambient geospatial information, and resulting hybrid capabilities to analyze it to support situational awareness as it relates to human activities. We argue that this emergence of ambient geospatial analysis represents a second step in the evolution of geospatial data availability, following on the heels of volunteered geographical information.

Pada Laut Cina Selatan memiliki empat gugusan diantaranya : Pulau Pratas, Pulau Paracel, Maccakesfied dan pulau Spratly. Saat ini Laut cina selatan masih ada beberapa konflik yang belum terselesaikan dan adanya keterlibatan beberapa anggota ASEAN dalam persengketaan kepemilikan pulau Spratly dan Paracel di kawasan laut cina selatan yang mendorong bagaimana negara indonesia dalam menyelesaikan masalah tersebut. Sampai saat ini perundingan masih belum menemui titik temu atas kepemilikan pulau Spratly dan Paracel. Dalam mengembangkan dan memanfaatkan SIG dalam menghadapi ancaman tersebut pertama perlu perlu mengetahui analisa dalam menentukan variabel-variabel data penguasaan ekonomi terkait pengaruh Laut Cina Selatan, karena konflik yang terjadi di laut cina selatan berpengaruh pada kestabilan ekonomi negara republik indonesia, seperti berpengaruhnya harga minyak mentah dunia, yang kedua perlu adanya peta pembaharuan alutsista yang dapat menjangkau laut cina selatan untuk mengontrol jika terjadi gesekan oleh negara tetangga, karena dengan alutsista yang modern dapat menjaga stabilitas negara dalam keamanan regional maupun global, ketiga yaitu perlu adanya hak paten atas kepemilikan wilayah seperti memiliki peta dalam peta rupa bumi indonesia, Rencana Tata Ruang Wilayah karena dalam RTRW terdapat data ruang daratan, ruang lautan dan ruang udara sebagai satu kesatuan wilayah, tempat manusia serta mahluk lainnya hidup untuk melakukan kegiatan serta memelihara kelangsungan hidupnya, selanjutnya indonesia harus memahami data dari Peta Nine Dash Line yang dikeluarkan oleh China karena ini menyangkut pada wilayah zona ekonomi eksklusif indonesia. perlu adanya interoperability dan dikembangkan dengan teknologi terkini yang sudah memiliki kemampuan 4.0 interoperability ini menghubungkan 3 Matra TNI AD,AL,AU yang sudah memiliki keamanan sistem yang kuat, memiliki operator pada masing-masing matra yang handal dan terseleksi dengan baik

The ‘secret world of Russia’s Cold War mapmakers’, long an arcane topic of interest to a select few, first reached popular readership via a 2015 Wired article. Journalist Greg Miller interviewed geographers, cartographic scholars, and map dealers to tell the story of classified Soviet military maps, produced from the 1940s to the 1980s, depicting foreign locales in stunning detail. The hook of the story was, naturally, the clandestine and incisive nature of these maps and their data. The maps were marked ‘SECRET’ and subject to tight security controls. They included niche details not available on publically available Western maps of the same areas, details that must have been gathered in person or via overhead imagery. These included load capacities of bridges, specific outputs of individual factories, security perimeters, and infrastructural details of military installations left off public maps, even specific depths of littoral and riverine areas. How and why did the USSR map such details? John Davies and Alex Kent, two of Miller’s interviewees – indeed, two scholars largely responsible for the exhaustive collection and study of the maps at the center of the Wired article – have published their own treatment of the subject. Red Atlas is a masterful, expert dissection of the thousands of Soviet military maps that have bubbled up to public visibility in the West, ranging in scale and scope from large area 1:4,000,000 scale air navigation charts down to 1:10,000 city maps. Most importantly, it offers forensic analysis of who made the maps, how, and why. In grappling with these questions, Davies and Kent expand the literature on cartographic intelligence in new and exciting ways.

Numerous military missions require timely, accurate geospatial intelligence, yet the extraction of geospatial information from imagery data is generally a labor-intensive process. Automated tools to assist analysts in this process would be of tremendous benefit for the production of geospatial intelligence at the National Geospatial- Intelligence Agency (NGA). Methods that rely on data from a single image, while useful, generally fall short of producing useable geospatial products. Fusion- based methods which jointly process and exploit information from multiple sources offer ways to address many of the limitations of single-source analysis. This paper discusses the needs for geospatial intelligence and examines the benefits of fusion to address these needs. Results from several recent studies demonstrate the value added from a number of tools for automated data extraction (ADE). Experience gleaned from these studies also indicates several areas where new research and development is...

A system for automatically extracting or isolating structures
or areas of interest (e.g., built-up structures such as buildings,
houses, shelters, tents; agricultural areas; etc.) from HR/VHR
overhead imagery data by Way of making as little as a single
pass through a hierarchical data structure of input image
components (Where pixels are grouped into components
based on any appropriate definition or measure of dissimilarity between adjacent pixels of the input image) to identify
candidate components (e.g., possible structures of interest)
free of necessarily having to re-iterate the same operator
configured With different threshold parameters for a plurality
of values.

Social networks where the actors occupy geospatial locations are prevalent in military, intelligence, and policing operations such as counter-terrorism, counter-insurgency, and combating organized crime.  These networks are often derived from a variety of intelligence sources.  The discovery of communities that are geographically disperse stems from the requirement to identify higher-level organizational structures, such as a logistics group that provides support to various geographically disperse terrorist cells.  We apply a variant of Newman-Girvan modularity to this problem known as distance modularity.  To address the problem of finding geographically disperse communities, we modify the well-known Louvain algorithm to find partitions of networks that provide near-optimal solutions to this quantity.  We apply this algorithm to numerous samples from two real-world social networks and a terrorism network data set whose nodes have associated geospatial locations.  Our experiments show this to be an effective approach and highlight various practical considerations when applying the algorithm to distance modularity maximization. Several military, intelligence, and law-enforcement organizations are working with us to further test and field software for this emerging application.

This chapter addresses the main existing issues concerning industrial heritage as a territorial resource for the revitalization or valorization of functional landscapes (former or existing). It addresses the conceptual framework of cultural landscape and its possibility as a “horizon concept,” as well as an object of intervention according to a territorial dimension. The proposal of “Cultural Landscape of Alentejo Pyrite” based on three mining sites—Lousal, Aljustrel, and São Domingos within the Iberian Pyrite—was designed to be a territorial project for mining landscapes. This is a previous response to an existing demand for operative methodologies that can convey a new paradigm of territorial planning, with emphasis on interdisciplinary and prospect views. It provides a voice to an architectural and urban planning point of view to these particular landscapes.

One does not routinely see “diplomacy” in the same sentence as “Maritime Domain Awareness” (MDA).  A common misconception is that diplomacy only takes place in formal meeting rooms, and MDA activities solely occur in intelligence/military settings.  In reality, however, diplomacy and MDA are independent force multipliers that function in a system of international law to advance national security objectives.  This article explores the vital relationship between diplomacy and MDA, and briefly highlights the value of an Office of the Director of National Intelligence sponsored fellowship at the Georgetown University Institute for the Study of Diplomacy was a worthy investment for an intelligence professional.

Beyond serendipity, analytical perspective has much to guide for future discoveries. And for this it is of key importance that a structured view of Earths geo ecosystems be detailed. It is time to connect the dots on an Earth wide system and start seeing the story of Humans as one larger story.  As the Helmland, Gonur, much much more lies undiscovered, awaiting.

Crime Prevention Through Environmental Design (CPTED) is an approach to understanding crime which focuses on the built environment and geography instead of simply a potential offender’s behavior or socio-economic characteristics. The theory of CPTED proposes that characteristics of the built environment can lower or increase the risk of crime at a location, based on the principles of Natural Surveillance, Natural Access Control, Territoriality, and Image. For this study, I investigated how CPTED characteristics had an impact on the property crime rate in Beaumont, Texas. I observed six Beaumont neighborhoods, and their houses and other buildings were rated according to an audit list I developed, consisting of 12 quantifiable CPTED characteristics based on the four principles. The houses and buildings of the six neighborhoods were rated by their individual parcels, according to a 0-4 based scale. These rated parcels were aggregated to a combined total of 134 residential blocks in ArcGIS, with each block being assigned the average of the CPTED ratings of the combined parcels. Maps of the six neighborhoods and their blocks were created which showed their vulnerability to crime, based on CPTED characteristic ratings. In addition to the CPTED vulnerability maps, I also developed maps which showed actual crime rates, which rated residential blocks within neighborhoods based on the number of crimes per 1,000 houses. The maps and spreadsheet data from the audit were used to determine the relationship of CPTED characteristics to crime rate. The research hypothesis was that neighborhoods that exhibit comparatively more CPTED characteristics will have fewer instances of crime than neighborhoods that exhibit fewer CPTED characteristics. The null hypothesis was that neighborhoods that exhibit more CPTED characteristics will not show a significant difference in crime rate compared to neighborhoods with fewer CPTED characteristics. The data was collected by slowly traversing each neighborhood by car, and using high-definition video cameras to record each individual house. The recorded video was later played back, and the CPTED residential audit was completed for each house. Information from the audit was documented in a spreadsheet, and also entered into ArcGIS software in order to create the CPTED vulnerability maps. ArcGIS was also used to create crime rate maps, based on data from the Beaumont Police Department. The neighborhoods were ranked from highest CPTED rating down to lowest CPTED rating; and from lowest crime count to highest crime count, to see if there were similar rankings in which high CPTED rating is closely paired with low crime-count. In addition to the ranking method, the CPTED ratings and crime counts for each block of the six neighborhoods were also plotted on a graph, to determine if there is a valid linear regression in which crime rate is determined by the CPTED rating. The results show that the neighborhood with the highest CPTED rating had the second lowest crime density, and the neighborhood with the lowest CPTED rating had the highest crime density. The ranks of the neighborhoods according to CPTED rating and crime count show that there likely is some relationship between these two variables. A regression analysis shows that there is an inverse relationship (y = -550.49x + 1254.5) in which an increase in the CPTED rating corresponds to a decrease in crime rate per 1,000, with an R2 value of .1805. The Spearman-Rho test indicates that there is a moderate correlation (R = .447) between CPTED rating and crime rate per 1,000, at the .05 significance level with a P value of 0.00000007. When looking at the CPTED rating maps of these neighborhoods alongside the crime count maps, many values of blocks in the CPTED maps correspond to similar values in the crime count maps. This phenomenon can better be illustrated by determining where similar rank values in CPTED rating and crime rating “overlap.” Although this process is more subjective compared to other methods, it is helpful in illuminating the areas in which the CPTED rating procedure most strongly predicted areas which are most at risk for crime, and which areas are most defended against crime. Even though this process of finding overlap was somewhat subjective, it was able to correctly predict 85 out of 134 blocks (63%) while the remaining 49 blocks (37%) do not show the relationship being sought, according to the hypothesis. The regression analysis and Spearman’s test showed this relationship in a more objective manner than the maps, illustrating that crime rate falls as CPTED ratings rise.

The chapter aims to point out the most emerging technologies in analysing and sharing knowledge about ‘not outstanding’ cultural landscapes. Therefore the chapter starts focusing on the concept of ‘minor’ cultural landscapes in the wider debate on heritage, then shows the changing approach to the question: the relevance of bottom-up vision in considering heritage. Secondly are taken into account image and information technologies through some definite research topics: Educating by multimedia; Experiencing and sharing new contents by people; Transmitting local heritage; Using image and information technologies to share collective experiences of places; Answering demand for social participation and free access to sources; Connecting tangible and intangible heritage in a tourist perspective. The goal of the chapter is to show how digital technologies can support knowledge and share of values about ‘minor’ cultural landscapes both through inhabitants and potential tourists to be attract...

This paper proposes two novel nature inspired decision level fusion techniques, Cuckoo Search Decision Fusion (CSDF) and Improved Cuckoo Search Decision Fusion (ICSDF) for enhanced and refined extraction of terrain features from remote sensing data. The developed techniques derive their basis from a recently introduced bio-inspired meta-heuristic Cuckoo Search and modify it suitably to be used as a fusion technique. The algorithms are validated on remote sensing satellite images acquired by multispectral sensors namely LISS3 Sensor image of Alwar region in Rajasthan, India and LANDSAT Sensor image of Delhi region, India. Overall accuracies obtained are substantially better than those of the four individual terrain classifiers used for fusion. Results are also compared with majority voting and average weighing policy fusion strategies. A notable achievement of the proposed fusion techniques is that the two difficult to identify terrains namely barren and urban are identified with sim...

We are moving towards digitization and making all our devices, such as sensors and cameras, connected to internet, producing bigdata. This bigdata has variety of data and has paved the way to the emergence of NoSQL databases, like Cassandra, for achieving scalability and availability. Hadoop framework has been developed for storing and processing distributed data. In this chapter, the authors investigated the storage and retrieval of geospatial data by integrating Hadoop and Cassandra using prefix-based partitioning and Cassandra's default partitioning algorithm (i.e., Murmur3partitioner) techniques. Geohash value is generated, which acts as a partition key and also helps in effective search. Hence, the time taken for retrieving data is optimized. When users request spatial queries like finding nearest locations, searching in Cassandra database starts using both partitioning techniques. A comparison on query response time is made so as to verify which method is more effective. R...

Data mining is one of the current vital fields for all types of data including spatial data. An example of useful extracted patterns from spatial data is to describe changes in metropolitan poverty rates based on city distances from major highways. Geospatial is a term that has recently been gaining in popularity; moreover, many applications on geospatial have different uses in different fields such as geo-tagging, geospatial technology, and geo-fencing. Analyzing spatial data is considered a complex task due to its details as it is related to locations with a special representation such as longitude and latitude. Other attributes are involved in the description of objects which can be analyzed using different data mining techniques. In this chapter, a demonstration of the basic information is performed considering spatial data and spatial data mining including all aspects such as the different type of data, different methods of analysis, different mining techniques, and other relat...

Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visual data collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.