[NOTE: Original Title: Protecting critical infrastructure in urban areas from YOU, the terrorist
Previously, we covered such subjects as 'social networks' - where the intelligence agencies, the Navy postgraduate school, Harvard, MIT and others were using it as a way to 'profile' you and all your social contacts/daily behavior.
Now, today we are going to go one step further into the bleak heart of the New World Order - you've just gotta ask yourself one question:
* Are these guys crazy/paranoid enough that they want to 'profile' my daily walking routine/behavior?
And the answer is: You betcha. And it's scary as hell - and while they don't talk about Twitter, or Google Wave, or RFID public transport cards, DO CONSIDER that they have all CONSIDERED this beforehand and have integrated into their system.
So the guy who had reservations about Twitter -
Twitter - double edged sword?http://forum.prisonplanet.com/index.php?topic=157730.msg937046;boardseen#new
Was absolutely right. These guys are 'analyzing' traffic - they are investigating all the different 'routes' that binds groups of people together. Say, you have this shopping centre with a multitude of stores. The New World Order goons want to know - which kinds of people walk into this or that store, which kinds of people don't go to the store at all - which kinds of people stop to look at an advertisement in the shop window? Computational Science and Its applications - ICCSA 2008 - Part I
Now be forewarned - this book is 1283 (!!!) pages long. It covers everything from iris recognition systems to geospatial data mining to 'profiling' the way you walk. Remember Alex talking about how the face-scanning cameras will use the biometric footprint of your body (that is what they have of you when you walk through the body scanner at the airport) to recognize you by the way you walk? Something similar to that is being discussed right here.
But before we discuss that - let's first talk about these guys using geospatial data to 'secure infrastructure' from the pesky public. Remember Dr Ruth A. David saying in that Homeland Security podcast that 'protecting key infrastructure' was one of Homeland Security's most important tasks - both domestic and abroad? That nexuses in with this.
Geospatial Modelling of Urban Security: A Novel Approach with Virtual 3D City Models
Markus Wolff and Hartmut Asche
University of Potsdam, Department of Geography, Karl-Liebknecht-Strasse 24/25,
14476 Potsdam, Germany
Complex urban structures are exposed to a variety of security risks.Especially concerning man-made hazards, cities can be considered as particularly vulnerable. Due to their high concentration of population, technical and social infrastructure, as well as their importance in politics, culture, economy and finance, metropolitan areas, in particular, can be considered as vulnerable environments. Since not every part of an urban area is exposed to the same level of potential security threats, it can be assumed that this level differs regionally within a metropolis. Based on methods of geoinformation science, this paper presents an innovative approach to identify particularly vulnerable urban regions. Using the 3D city model of the German capital Berlin as an example, the potential of such models for mapping, analysis and assessment of different threat levels in urban environments is demonstrated. This geovisual and analytical potential of 3D city models can be instrumental for decision makers working in security agencies for both threat assessment and intuitive cartographic communication of spatial phenomena related to urban security issues.Keywords:
GIS, 3D city models, geovisualisation, civil security.1 Introduction
This paper presents an approach which couples GIS-based analysis with the visualisation potential of virtual three-dimensional city models. Taking the virtual 3D city model of the German capital Berlin as an example , we discuss selected geoanalysis methods targeted at data integration and vulnerability mapping of metropolitan regions. This geoanalytical method is then applied to investigate selected issues in the field of civil security
. This approach requires the integration of application-specific thematic information into the databases of existing city models
. This is accomplished by using a Geoinformation System (GIS) to manage large, heterogenous spatial data. Virtual representations of complex three-dimensional urban environments are constantly gaining popularity among both the scientific community and the wider public. To cater for these requirements we develop a workflow for extending, analysing, visualising and combining existing 3D city models with related thematic data of special interest within the domain of civil security.
Translation: they're tracking you - and they are determining and gauging whether you are a 'threat' to businesses or key infrastructure. As per Dr Ruth David's own admission, protecting key infrastructure is one of Homeland Security's most important assignments.
This workflow as well as the extension of the database with frequency and sociodemographic data is presented in section 2. Based on these database enhancements an innovative method to map and analyse regions with different degrees of exposure is presented in section 3. It is shown, that investigating urban environments for increased security risks reveals an uneven distribution of threat levels over metropolitan space
. Thus different urban regions can be characterised by different levels of exposure, resistance and resilience compared to possible security threats. The end result is different levels and spatial distributions of vulnerability. In this context a GIS-based tool is presented which allows for an automated processing the input data and thus facilitates an easy repeatability of the analyses. Section 4 finally contains a short summary and introduces some perspectives.2 Augmenting Existing Geodatabases for Geovisual Analysis
Three-dimensional city models are more and more available, with various cities possessing their own digital 3D representations. Compared to the traditional medium for communication of geographical data – the two-dimensional map – virtual three-dimensional city models facilitate in-depth analysis and presentation of spatial data. Furthermore, from a cartographic point of view, three-dimensional geovisualisation can reveal “patterns that are not necessarily visible when traditional map display methods”  are used. Applying methods and functions of visual analytics facilitates to “detect the expected and discover the unexpected”  as by means of visual data mining . Thus, complex spatial situations like planning scenarios, noise and pollutant dispersal patterns are increasingly analysed and visualised by the use of 3D city models .
Typical datasets included in such databases contain, e.g., cadastral and topographic information as well as information relating to buildings, such as use or function, dimensions, height, etc. What is, however, widely lacking with these databases is application-specific thematic content linked to the different topographic and 3D data layers. To augment existing city model databases with thematic information required to conduct special-purpose geoanalysis in the field of civil security a workflow is developed to perform such operations in a systematic way (cf. figure 1). Within this workflow we use different methods of GIS-based analysis to extend the database. As an example, passenger-frequency information (cf. section 2.1) is added by linking this data to road segments. The GI-system database then provides the basis for further analysis and allows to generate different types of visualisations. Conceptually speaking, GIS serves as the centre for non-graphic data analysis. The 3D geovisualisation system serves as the visualisation front end, facilitating graphical analyses by interpreting spatial relationships in a three-dimensional environment. Applicationwise we use a commercial GIS package, namely Esri ArcGIS, and the LandXplorer 3D visualisation software system .To investigate the geoanalytical potential of this combined GIS/VIS system, it has been applied to model urban risks in the centre of the German capital Berlin. The study area covers a 13 km by 6 km strip of the city centre inside the inner metropolitan train ring between Westkreuz and Ostkreuz S-Bahn stations. This transect contains the “Western Centre” (Kurfuerstendamm boulevard) of the city as well as its “Eastern Centre” (Alexanderplatz Square).
As a database we have used the official Berlin city model which includes the following data sets:
• Built-up area of 57,096 buildings, with height data and building function
• Street network, compiled primary for car navigation issues (from Teleatlas)
• Public transport network: Metropolitan train (S-Bahn), underground, tram and bus routes
• Geotopographic data, namely topographic map K5 (scale 1:5,000), digital terrain model (resolution 25m), high resolution aerial photography (HRSC, resolution 20cm)2.1 Modelling Outdoor Bustle: Enhancing the Building Dataset with Frequency InformationAn important factor in analysis of security related issues is detailed information on the activity flows within a city.
With this information, areas, streets and single buildings in a city model can be identified which are daily frequented by many or few people. This kind of information is to date not included in the Berlin city model database. That is why data from the FAW Frequency Atlas of the German Association for Outdoor Advertising (FAW) are integrated into the 3D city model database. This atlas, originally developed for the advertising industry, has been compiled also for the city of Berlin and is based on Teleatlas road segments. The atlas data allow for an in-depth evaluation of pedestrian, car, and public transport frequencies. Frequencies are calculated as average values per hour on a working day basis for the years 1999 to 2005 . Technically speaking, for each road segment one FAW point exists with the corresponding frequency values. Based on its geocoded coordinates this dataset is imported into the existing Berlin city model. This point-based FAW information is then referred to the corresponding road segments via its unique segment-ID. By this each segment of the street network dataset is supplemented by new attributes: The number of pedestrians, cars and public transport carrier that frequent any given street segment per hour of an average working day (cf. figure 2).
Note what they are doing here - they admit they are treating the entire CITY as a 'network' with 'activity flows' - which means they're treating you as a data packet inside a network. Yes, that's kind of a lame analogy, but it holds true when you see the picture below and you see the streams with the alternate colours - they show you the frequency of people walking on a daily basis in this or that street. Juxtapose all that with the cameras that also determine how many people are frequenting this or that road or area (and I have an official RFID book by the way that confirms that - I'll pull it up later to confirm this to you lot) - and this is surveillance on a far greater scale than anything ever imagined in George Orwell's 1984. This is why they need all those sensors inside the cities - predictive modeling of the pedestrians' behavior so that they can 'protect' key infrastructure - which is the police/law enforcement's chief focus today. THEY ARE NO LONGER ABOUT PROTECTING CITIZENS - I HAVE HAD THIS VALIDATED BY SEVERAL COPS WHO TOLD THIS SHIT RIGHT IN FRONT OF MY FACE.
In addition to frequency data referred to street segments similar information for buildings along streets can be relevant for civil security issues which are often relating to single potentially vulnerable buildings
(My note: See? Protecting critical infrastructure - this is what the militarized/joint combat police are for in conjunction with Homeland Security. Remember Dr Ruth David in that Homeland Security broadcast saying Homeland Security's prime directive was protecting key infrastructure and the 'away game' (as in: operating overseas) was most important? That's what this is
). For that purpose frequency values have been assigned to adjacent buildings by analysing distances from buildings to street segments (cf. figure 3). First, centroids of each building are calculated. Second, new points are created along the road segments for every 30 meters. Third, the four nearest segment points, based on each centroid, are identified. Finally, an average value is calculated from their frequency values which is assigned to the whole building. As a result, such “smart” buildings can be queried for their frequency data.
See? They're not just tracking the traffic flows of the cars and other automobiles using GIS - no, they're also recording and profiling the traffic flows of PEDESTRIANS - meaning, when you walk on the goddamn street for no particular reason other than to go shopping, get drunk or visit your significant other.
2.2 Modelling Building-Related Population Parameters: Augmenting the Building Dataset with Socio-demographic DataTo identify and analyse potentially vulnerable city regions additional socio-demographic data on a building block basis is required. Such data are, e.g., population density, family income or purchasing power. Knowledge of these patterns enables one to draw conclusions from exposure, resistance and resilience concerning a possible hazardous event.
To map vulnerability patterns within an urban region, additional population data of the German Society for consumer research (GfK) are also integrated into the existing city model database. Here such data are only available for the central Berlin postal code zones 10115 and 10117, respectively. This is an area of 6 km² stretching from Brandenburg Gate in the west to Hackescher Market in the east, Bundesrat building in the south and Schwarzkopfstrasse underground station in the north.
This area is both suitable and interesting for security related analysis since numerous embassies, consulates and government buildings, as well as highly frequented touristic sites, such as Friedrichstrasse street, Unter den Linden boulevard, Gendarmenmarkt square, are located here. In addition, this area will house from 2011 the new headquarters of the German intelligence service (Bundesnachrichtendienst, BND) which will be located on the former World Youth Stadium (Stadion der Weltjugend) site.
As with processing of FAW frequency data, GfK data are imported using GIS functions. The available source dataset of 2006 includes numerous socio-demographic features such as population data, household size, household net income, building structure, building use, purchasing power. For data privacy reasons data are, however, not available for single buildings but for all buildings along a given street segment
(My note: Oh, gee, thank goodness - they just 'profile' whether or not I fit inside some kind of poor or lower social class that is likely to pose a 'problem' for them - and hey, they can always extrapolate from that my household size and net income even though they basically deny here they have that capability
.). Spatial reference of these point based data is by coordinates. Reference of those points to the appropriate buildings is verified by the matching of street names of GfK data points and belonging buildings, respectively. Therefore an algorithm is applied which searches for each building (its centroid) the nearest GfK point with the same street name. Attributes of this point are transferred to the building. Figure 4 is a 3D visualisation showing purchasing power related to each building block.
BTW - just as a side note - I thought that liberals were so fond of saying there are no longer any social classes or differences in social class? Well, this does seem to fly in the face of that - since they're giving certain residential zones 'certain colors' on the basis of their income status/purchasing power, and household size - and they 'infer' from that how likely you are a threat to 'key infrastructure'.
3 Geospatial Modelling of Urban Risks
The augmented 3D city model database is a prerequisite for further and in-depth geoanalysis of urban risks. The broad range of thematic data integrated into the database allows, e.g., for further spatial analysis in the context of an urban impact assessment. Such analysis will, however, not produce any precise indication which building (the element-at-risk, EAR), e.g., is exposed to an increased threat of what nature. For such evaluation of factual threat levels, a substantial amount of additional data would be required, many of which are not available publicly. In this context Koonce et al.  state that this knowledge is "best left to the security and intelligence agencies". In this study it is therefore assumed that only buildings with specific occupancies, such as government offices, or embassies, are particularly vulnerable to security threats.
OK, and what conclusion are we to draw from that? That the 'intelligence agencies' are monitoring and building maps of my entire residential area looking for threats to THEIR 'key infrastructures'? I thought the terrorists were over there, so we didn't have to fight them over here - why am I the threat? Oh why even entertain cognitive dissonance, we know how this game is being played.
Dealing with the nature and location of potentially hazardous events in urban environments [2, 10], an evaluation of the surroundings of a particular building exposed to a given risk is of special importance. Thus, for protection as well as for counteractive measures it is decisive to differentiate whether the structure is surrounded by open space or by dense urban housing.
To perform such distance based analysis on the city model built-up area layers, a first step requires the creation of circular impact zones, with the element at risk in its centre. In our case the radii are defined at 150m, 300m, 500m, 1,000m and 2000m intervals. In a second step the intersections of impact zones and buildings allow for statistical analysis based on the built-up-area database. This analysis shows that buildings within zones one to three (up to 500 m from the EAR) are passed by an average of 200 pedestrians. This value is decreasing with increasing distance. Because of the EAR location in a business district of central Berlin, the greater the distance from the EAR the more buildings have residential instead of business and administrative occupancies. As a consequence the number of potentially affected pedestrians is decreasing while the number of residents is increasing (cf. figure 5). 3.1 Modelling the Distribution of Exposure Levels in Urban EnvironmentsIn the following an approach is presented to identify urban regions characterised by different degrees of exposure of a potential impact. The underlying assumption of this investigation is that not every area of an urban environment is equally exposed to the same level of potential threat
. Rather a regional variation of threat levels can be found, as buildings potentially exposed to an increased security risk are not evenly distributed in city space. In this study the term "highly increased" threat is assumed for buildings housing embassies, consulates and government offices. An "increased" threat is assumed for the following buildings: Shopping centres, petrol stations (danger of explosion), police posts, power or transformer stations (critical infrastructure) etc. The following analysis is based on the buildings dataset of these categories (=exposed buildings). It can, however, be expanded to any user-defined set of buildings within a city model.
To map regionally different exposure levels the city model is first overlaid with a user-defined grid. Second, the distance of each grid cell to the closest exposed building is calculated. The resulting grid pattern is composed of cells each of which contains one distance value of the respectively closest exposed building. The grid can be further differentiated by building occupancy. The embassy grid, e.g., contains distance cell values of the closest buildings used as embassies or consulate offices. This set of exposure grids generated by proximity analysis forms the basis to identify different levels of threat exposure. For that purpose, each function-specific grid is reclassified in relation to proximity: Thus, grid cells closer to an exposed building are assigned a higher exposure level than cells with greater distance (cf. tab.1).
GIS-based grid analysis, as used here, allows for convenient overlays and combinations of areal data by using map algebra functions. Hence all function-specific exposure grids generated are combined into one single “exposure grid” by summation of their respective pixel values. It has been mentioned that different building uses can be assigned different levels of threat exposure. Thus summation is performed by weighting the respective grids according to their threat exposure: The embassy offices grid (embassies, consulates) and the government offices grid are weighted with a factor of 4, the shopping centre grid with a factor of 2 and the service and utilities grid (police posts, petrol stations, power stations) with a factor of 1 (figure 6).
The workflow described here has been automated by developing a GIS tool using ESRIs ArcObjects based software development framework. Our tool facilitates an automated and fast processing of the single grids (figure 7).
To employ this tool a buildings dataset containing information on building use and functions, respectively, is mandatory. The current version requires the user has to create an ASCII remap table according to table 1.The combined weighted exposure grid can be visualised in different user-centered ways for further geoanalytical processing. Presented here is a 3D visualisation of a virtual threat surface based on the Berlin city digital terrain model
. For easy comprehension exposure grid values are exaggerated by a height factor of 10 and added to the original height values of the digital terrain model. The resulting 3D map is a graphic, easy-to-read visualisation of the spatial distribution of threats in urban environments.
Intersection of the summation exposure grid with the buildings layer results in additional threat information in the built-up area dataset. As this dataset has also been augmented by socio-demographic data, a variety of geographical correlations of building occupancy, socio-demographic situation, infrastructure etc. with regional threat exposure can be mapped, visualised and analysed. For instance, all buildings located within grid cells with values of combined exposure levels greater than a given value can be selected. Also statistical analyses can be performed to distinguish between spatially varied socio-demographic feature states. As a result, it is feasible to map those regions characterised by a number of inhabitants above average, significant purchasing power and high financial status (derived from net income per household)
So, what conclusion am I supposed to draw from this? That the Census Bureau is out of a job? (or not, depending on who you think is going to be running this system) Can we let machines run this thing - possibly employing a neural network or a 'world brain' for a particular city segment? Do machines start analyzing sociodemographic data and basing their agenda on it?
4 SummaryThis paper presents an approach to combine GIS-based spatial analysis with innovative 3D visualisations using virtual three-dimensional city models for applications in civil security. Based on augmenting the existing spatial database of the virtual 3D city model of the German capital Berlin by a variety of parameters including building occupancy, frequency values and socio-demographic parameters, areas and objects exposed to specific levels of threat can be identified
By combining function-specific grids with threat exposure levels the spatial distribution of threat levels can be mapped. The resulting geographic distribution can subsequently be combined with additional socio-demographic or infrastructure data for further geovisual analysis
. (My note: So, what they have here is a realtime virtual environment MODELLED on the actual environment that basically includes you, your entire residential block, the income and purchasing power of your residential block, and all of this with the specific intent and purpose to profile whether or not you will EVENTUALLY pose a threat to 'key infrastructure' and the government. What this is, is SimCity for psychopaths - SimCity for control freaks - they are running here a color-coded version of SimCity that is modelled on the real-world. This is the kind of depersonalization that enabled the Nazi death camps - reducing men with real lives, with real emotions, with real feelings to mere statistics and engraved dots inside some Hollerith supercomputer which is continuously fed punch cards
) Perspectively some of the presented methods and functions will be modified: The ArcObjects tool presented in section 3.2 will be extended to a larger ArcMap plug-in which also allows for an automated processing of the GIS features for the 3DLandXplorer System. Furthermore the presented approach of modelling and analysing
urban security by using GIS and 3D city models will be broadened by introducing some standards of thematic 3D cartography. Thus, an efficient communication of spatial phenomena shall be ensured.AcknowledgementsFunding of this study by the German Federal Ministry of Education and Research (BMBF)
within the framework of the InnoProfile research group ‘3D Geoinformation’ (www.3dgi.de
) is gratefully acknowledged. The authors also like to thank the German Association for Outdoor Advertising (FAW) for providing frequency atlas data and Berlin Partner GmbH for use of the official Berlin 3D city model.References
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BTW - know what they did here? They've just applied the DHS original color-coded threat assessment system onto the entire world map - they've 'color coded' entire residential zones according to the threat they pose to DHS' to-be-protected key infrastructure, and on top of that they can also pull in all of the sociodemographic data that is also going into this system - they can see if you're living inside a residential area with relatively low household income, they can probably infer from that the racial or ethnic makeup, and so on. This is the kind of stuff that enabled the Nazi concentration camps - because what they do is, they de-personify you and entire classes of people - and turn it into a number, a statistic, a binary floating inside some computer system. How 'convenient' that the concentration camps were all enabled by IBM Hollerith supercomputers with punch card index systems used to compile much of the sociodemographic and racial makeup that the Nazis desired. It could 'never happen again', eh? Combine this system with the Global Information Grid, with Interpol's DNA databank and it absolutely DWARFS anything the Nazis ever came up with - it's full spectrum dominance of everything inside the public and private sector.