Panos Melas

Gianluca Correndo

Lee Middleton

Zoheir Sabeur

Advanced Data Analytics and Visualisation for the Management of Human Perception of Safety and Security in Urban Spaces

Keywords: safety perception, urban security, data analytics, visualisation, open data

The genesis of this work began during the DESURBS 1 project. The scope of the project was to help build a collaborative decision-support system portal where spatial planning professionals could learn about designing much more secure and safer spaces in urban areas. The portal achieved this via integrating a number of tools under a common, simple to use, interface. However, the deficiencies in the project became apparent with subsequent development.

Many of the open data employed changed format while applications were increasingly custom built for a single dataset. In order to overcome this a system called KnowDS was redesigned. The essence of the new design includes decoupling acquisition, analysis and overall presentation of data components. The acquisition component was designed to snap-shot the "data providing methods" and query data provenance in a similar way to a source code repository. The analysis component is built under a number of modular tools with a common interface which allows analysis to build in a plug&play approach. Finally, the data presentation component is where the custom logic goes. Under such design approach, the building of future applications becomes less challenging. As a consequence, two case studies using the new framework were considered. Firstly, a UK crime web-browser which allows data analytics performances at various granularities of crime types while correlating crimes across various UK cities has been achieved. Secondly, a mobile application which enables to generate reports on citizens' perception of safety in urban zones has also been developed. The two applications were efficiently built under the new design framework; and they clearly demonstrate the capacity of the new system while they actively generate new knowledge about safety in urban spaces.

Introduction

Large volumes of open observation and contextual data concerning urban spaces have become more accessible for processing in recent years. The open data can be potentially aggregated for better usage and improvement of existing decision-support sys-tems, specializing in the dissemination of safety related issues in cities to the public [START_REF] Vilajosana | Bootstrapping smart cities through a self-sustainable model based on big data flows[END_REF]. The potentially useful data aggregation exercise starts with the use of the various open data sources which are provided as part of the open government movements globally [START_REF] Chun | Government 2.0: Making connections between citizens, data, and government[END_REF]. Additionally, social network data, or so-called social sensing [START_REF] Aggarwal | Social Sensing[END_REF] data can be used [START_REF] Prasetyo | Social Sensing for Urban Crisis Management: The case of the Singapore Haze[END_REF][START_REF] Lehmann | Dynamical classes of collective attention in Twitter[END_REF]. Furthermore, survey information from custom written mobile applications can be employed. The aggregation with private (closed) data such as local authority data or data from telecom providers can be employed [START_REF] Ceolin | Two procedures for analyzing the reliability of open government data[END_REF]. The heterogeneous nature of the data can be improved via a data normalisation process. This converts all data to a common schema. However, the data is semi-structured and therefore concepts from NoSQL databases can be adopted [START_REF] Leavett | Will NoSQL databases live up to their promise?[END_REF]. Thus, all sources of data are analogous to a function which maps from space-time to a series of values. Data collection is performed using a periodic process which polls the data source for any changes.

Because the data sources are potentially changing, it is important that the system not only stores the latest snapshot but also changes in data. For both operations, i.e. data collection and data normalisation, different filters/adapters are developed in order to fetch and normalise data. For example, the anonymisation of sensitive data might take place during normalisation. The system should be able to handle multiple adapters in order to fetch and normalise data accordingly. An advanced Knowledge Discovery System (KnowDS) that can aggregate open data provided on the cloud has been developed in this study following the progress made in the DESURBS project [START_REF] Bonastos | Crime Open data aggregation and management for the design of safer spaces in urban environments[END_REF]. The system partially structures the data in terms of a common schema and provides methodologies for updating and analysing urban data. Furthermore, the use of common external API allows applications to be built efficiently on top of the system. This paper is structured as follows. Firstly, a technical overview of KnowDS is presented with highlights on the encountered significant components and challenges. Secondly, a set of concrete examples of the framework in operation are described. Finally, conclusions are drawn along with future work and recommendations.

KnowDS Design Overview

It is becoming increasingly common for data platforms to bring together open datasets and perform visualisations upon them. Such systems have three main components. Firstly, there is the acquisition system which downloads the data from an external API and stores in a custom database. Secondly, there is the processing system which takes the data form the database and converts it ready for display. Finally, there is a client application, typically web-based, which embeds the data and displays it. As they are designed for a specific purpose and each steps feeds to the next, it is difficult to repurpose the data easily and efficiently. As an example a change in the external API may break the system completely. This issue was experienced in the early ongoing work phase of the DESURBS project. Building upon such experience in DESURBS, the KnowDS framework was developed. The framework is divided into three components: Data collection and normalisation; Data processing and analysis; and applications. These are illustrated in Fig. 1 above and delineated by the dashed lines. Each component is composed from individual modules, i.e. adapters and processing elements. Processing elements can provide a standardised API that frontend client applications and employ to interact with the system. Both adaptors and processing elements have clear interfaces while they perform a single specified task. Nevertheless, more complex tasks and analyses can be achieved by combining a set of processing elements. The overall system design is open and flexible to extend and accommodate new data sources as well as new data processing elements for performing knowledge discovery. The core implementation is primarily based on open source technologies and languages. The core technologies include Python, PostgreSQL, PostGIS, Django and Apache. The various components are discussed with more details in the next sub-sections.

Data Collection and Normalisation

The purpose of the Data Collection and Normalisation component is to gather and extract data of interest from various data sources. This has a similar purpose to conventional ETL (Extract-Transform-Load) [START_REF] Agrawal | An Enhanced Extract-Transform-Load System for Migrating Data in Telecom Billing[END_REF][START_REF] Henrard | Strategies for data reengineering[END_REF] systems which are common in data warehouses. In the extract process consumes web APIs (but more generally any data source). The transform serves to add structure to the original data which is often unstructured and heterogeneous. In contrast to ETL systems the reconstruction of the history of the data as well as current snapshot is enabled. For example, it is important to know that a specific API splits one field into two at specific date and provide snapshots of the data before and after the change. In order to achieve this, a method of tracking the changes to the data, similar to those employed by version control systems, has been purposely implemented. The KnowDS data store is composed of a combination of databases, directory structure and a storage archiving system. The current implementation of the data store employs a combination of databases and an auxiliary hierarchical directory structure to store binaries and data blobs. In order to realise the above vision, the nature of the datasets available (i.e. from data.gov.uk) to classify their nature was studied. In all cases the dataset was modelled as a functional

Fig. 1. Overall Design of KnowDS

mapping from a spatio-temporal attribute with a resource identifier to a multidimensional vector. This vision is fully documented for multiple datasets, as illustrated in Table 1 above. The fields often require conversions in order to put them under a common representation. For instance, geographical data for twitter are in WGS84 format, while the UK crime data are in Easting/Northing. Thus, the structuring process of data takes the arbitrary dataset and converts it into table format with several mandatory fields and a blob which contains the remainder of the data. It is for this reason that semi-structured data formats need to be described. The specific implementation of the component is made up of a number of autonomous modules (adapters) that are capable of accessing the source data, conversion, structuring, and storage. Specific adapters are lightweight and reusable since they are coded in a common pattern. Typical activities with the adapters include fetching the data from the source (one shot or periodic), data normalisation and anonymisation (of sensitive data). An example of data conversion processing can be illustrated through a relatively simple geographical dataset. Urban spaces can be simply defined as geospatial polygons. However, their representations differ among local authorities. Among the representations commonly used in the UK include:

• Cities: A boundary dataset containing polygons for each one of our cities. Defined at the level of government. • Neighbourhoods: This is defined by the census Middle Layer Super Output Areas (MSOAs) are population-wise similar (~7400). Provided by Ordinance Survey. • Postcodes: The highest level of resolution. Often down to the granularity of a few households. This is provided by the postal service.

These each are at a different zoom level and size. While some of the boundaries match (city and neighbourhood) together, others do not (postcodes). This means that a typical postcode can be contained in 1 or more neighbourhoods. Additionally, different schemes use different coordinate systems. WGS84 2 was used as a common coordinate scheme for our geographical coordinates. This means that on ingest the conver-2 http://earth-info.nga.mil/GandG/wgs84/index.html

Dataset Spatial Temporal ID Twitter  1 /   Postcodes   UK Crime   Weather    Pollution    Conservation Areas   Building information  
House prices  

StreetView data 

Table 1. Exemplar Datasets with the Common Attributes.

sion to the WGS84 reference coordinates will be required. In order to cope with changes in data structure, many sorts of transformations that happen over the lifetime of datasets were addressed. The mapping relationship which was described earlier can be considered as data model. The model transformations are thus: Creation, Modification and Deletion. In order to handle such transformations, the history of the dataset which can be used for replay has been maintained. Hence, the restoration of previous states of the data even when the dataset has changed can be achieved. An example of the need for changes can be seen by studying the history of a particular API. For example the UK Police crime data API. In 2013, there were several breaking changes to it. Firstly, a refresh of the crime data meant that several crime types were reclassified. Secondly, the ability to obtain crimes in a specific Police neighbourhood (in Easting/Northing) was removed and replaced by street-level information (WGS84). As a result, several police administrative regions were merged. The reclassification of the crime types required the addition and removal of fields to the data model. The API change can be dealt with via the addition of a new model for the data.

The merging of regions can be solved via modification of the data in the model.

Dataset Processing Modules

Whilst the purpose of the previous module was to collect and structure the data, the data processing module performs operations on the data for subsequent use by an application. The processing takes place in the central portion of Fig. 1. There are a number of specialised modules which perform processing of data which are contained within the dataset and provide output. Like the data collection adaptors, the modules are written under a common pattern. They specifically manage the access to the dataset and analyse it for a given specific task. Once analysed the output can be exposed via an API or passed to another module for subsequent processing. In this sense the modules can be classified in terms of whether the goal is to analyse data or aid in client applications.

The analysis takes place depending on the current client requirements and needs. In this model the back-end acts as a broker for data which is passed to an analytics engine. The analytics engine also stores data internally in a database. However, the data is in processed form from the broker. There are a number of different data processing

Fig. 2. Timeline, or location based analysis combining a heterogeneity of data sources revealing a pattern

techniques that can be applied by the analytics engine. They are:

• Simple analysis of data (From a particular resource).

• Combining data from different resources (e.g. Timeline analysis on data from different sources, identification of patterns, behaviour.) • Incident timeline analysis (Which includes combined data from available resources at a given time of an event.)

In particular, the analysis of data can split into pre-incident, during-incident, and postincident analyses (see Fig. 2). Spatial analysis can also be applied on predefined urban spaces using multiple datasets as shown in Fig. 2. Higher level analysis, or customised user defined analysis should be facilitated, i.e. in terms of describing an analysis as a workflow/process that can be loaded and run by the system. The analysis and study of identified patterns/behaviour, which are external to the system, might require input from knowledge domain experts.

The Application Front-End

At the frontend level, the business logic for the specific application should be contained. This is specific to the application and/or visualisation and does not enable the degree of code reuse as exhibited for the previous components. No analysis of data takes place at this level, however, data processing for visualisation or conversion to another format might be required. The connection between the clients and platform is via an established API such as HTTP/REST. Many applications will require some processing of the raw data, while this is triggered via the API. The results are cached within the data-store for subsequent use. This is illustrated in the right-most component of Fig. 1. Some examples of the sorts of modules that can be provided are: Mash-up visualisations (where data from different datasets are normalised on similar coordinates); Custom data exporting; and Entering of user contributed data.

Case Studies

Two case studies using the KnowDS framework are presented in this section. The first case study is an application which extracts data from the open UK crime API and visualises it using spatial analytics with Ordinance Survey data. The second case study is a mobile application that enables users report on their perception of safety at urban spaces which they frequent. The application shows how user contributed data can be used along with other sources of public data to generate and discover new knowledge.

UK Crime Application

This is an application that was developed for the system, while it takes into account existing work on crime analysis in the literature. The main dataset of this application is the UK Police Crime data which is available under a public API 3 . The Police crime application examines crime data in 13 major UK cities based in the South of England, the Midlands, the North of England and Greater London. The processing tools of this application allow comparison and visualisation of different crime types at various levels of spatial granularities and zoom levels. Additionally, analytics are performed on the data to allow quantified on-demand statistical comparisons of specific urban crimes and type of crimes across UK cities. An overview of the system as implemented in the KnowDS framework is illustrated in Fig. 3. The main function of the adaptors in this case is to fetch data from the UK police API and store it in the database. Additionally, as the crime location data is stored in Easting/Northing conversion to WGS84 is required. Finally, the adaptor periodically downloads and updates the data (on a monthly cycle) accordingly. In order to process the data and compare at differing zoom levels, access to Ordinance Survey definitions is also required. These are ingested into the data-store via an additional adaptor.

Fig. 3. UK Crime Application Design

There are a number of distinct modules present in the system. Firstly, there is a tool that aggregates the normalised crime data for a specific crime-type and produces a "heat map" as a result. The output is archived within the data-store to reduce processing load. Secondly, there are raw crimes data which are exposed as a JSON object. This can be used to create maps on the client side. Thirdly, analytics are performed to cross-correlate various types of crime with each other. The results of the analysis are equally exposed via JSON. Finally, a simple aggregate of crimes at a given specific location (and level of zoom) is presented via a JSON object. The Police application front-end visualisation UI is a browser driven tool that can visualise various crime points and intelligent statistics on maps with cross-comparisons between different crime areas, e.g. city, neighbourhood, or postal code level. This is shown in Fig. 4. The data is generated dynamically from the framework and represents new investigative knowledge about urban and cross-urban crimes.

Mobile Application (YourSafe)

This is a native android application for reporting user safety perceptions. End users are the dataset suppliers and also consumers at the same time. YourSafe is built to allow users to report their perception of safety within urban spaces. The aim is to show that end users can become a sustainable source of information about geospatial perception of safety in urban spaces. At the same time such application can implicitly lead to making local urban spaces safer. Data protection, privacy and security is considered highly in this application under the European Data protection regulations. Device ID's are hashed and personal information is NOT stored in the system. An overall schematic of the design is shown in Fig. 5. As far as the data collection is concerned, there are a number of distinct modules which are implemented. Firstly, there is an adaptor which pulls in geographical locations from Ordinance Survey. Specifically, this is re-used from the police application. Additionally, UK crime data is re- used from the application as well. The final adaptor was built in the system in order to save reports into the data-store. Optionally, a binary object such as an image, can be sent as an attachment with the report. This adaptor converts the incoming report from the API to a space-time location and stores the report alongside the user. There is very little analysis that is performed for usage in the tool. The tool provides a simple visualisation of reports and crime data at a given location. Thus a pair of modules are written to perform a spatial query to return results via JSON. In addition there are tools related to user management. Specifically, creation and deletion of users lead to updates to the underlying data store. These are called via the rest API from an external client. The Android client is pictured in Fig. 6. The user can create a report either online or later on via the interface in Fig. 6 reports are a valuable source of new knowledge. Although the number of participants in the early trial was limited, around 60% of the reports concerned safety perception narratives, 25% on alerted incidents; and 15% were on joint reporting of incidents and perception of safety in urban zones.

Conclusions

This paper provides an overview of the KnowDS framework for City crime data analytics. It illustrated its usefulness on two distinct applications: a UK crime webapplication, and YourSafe mobile application. The essence of the design was to decouple the data gathering, data analysis, and data visualisation parts. The data gathering phase consists of a number of independent adapters which can ingest data into a data-store. Additionally, the history of the dataset is maintained and enabled to querying over historical data. This component of the framework is a data repository similar to a source code repository. The data analysis also consists of a number of modules which perform analyses on data. Processing is achieved by combining one or more of these modules. The last component is the specific application which varies depending on the application. The specific applications are very different in nature but share some common data (Police crime data). Using KnowDS allowed the mobile application to be built simply by concentrating on the differences between the two applications. Additionally, the framework demonstrated that knowledge can be mined from data content either via novel use of disparate datasets (UK Crime application); or user contributed data along existing data (YourSafe). Further development of the framework will continue in the future. More adaptors which allow more datasets to be explored will be put in place. Additionally, the range of data analytics tools will be deployed.

Fig. 4 .

 4 Fig. 4. (a) Illustration of the application showing crime data for a specific city (b) Comparison of two cities

Fig. 5 .Fig. 6 .

 56 Fig. 5. The design of the YourSafe mobile application

DESURBS: Designing Safer Urban Spaces: http://www.desurbs.eu

http://data.police.uk/

Acknowledgements

This work is co-funded by the European Commission under the 7 th Framework programme under the DESURBS project: Grant Number 261652.


Sergio Hernández

Philip Sallis

Distributed Minimum Temperature Prediction using Mixtures of Gaussian Processes

Minimum temperature predictions are required for agricultural producers in order to assess the magnitude of potential frost events. Several regression models can be used for the estimation problem at a single location but one common problem is the amount of required data for training, testing and validation. Nowadays, sensor networks can be used to gather environmental data from multiple locations. In order to alleviate the amount of data needed to model a single site, we can combine information from the different sources and then estimate the performance of the estimator using hold-out test sites. A mixture of Gaussian Processes (MGP) model is proposed for the distributed estimation problem and an efficient Hybrid Monte Carlo approach is also proposed for the estimation of the model parameters.

Introduction

Crop damages due to frost events is an important issue in many agricultural areas. There are several empirical models that attempt to predict or anticipate the occurrence of the minimum temperature at a given time and location. These methods deliver descriptive results, however calibration is required for the model parameters in order to account for the time of the year and the local conditions [START_REF] Snyder | Frost forecasting and monitoring[END_REF]. Artificial neural networks (ANN) models have been previously proposed as an alternative to the empirical methods for the estimation task [START_REF] Jain | Prediction of frost for fruit protection using artificial neural networks[END_REF]. For instance, ANN models were used to predict air temperature at hourly intervals from one to 12 hours ahead [START_REF] Smith | Improving air temperature prediction with artificial neural networks[END_REF] using meteorological data such as temperature, relative humidity, solar radiation, wind speed and direction, as well as seasonal variables to account for the day of the year.

More recently, an ensemble of twelve ANN models was used to achieve temperature predictions throughout an entire year [START_REF] Smith | Artificial neural networks for automated year-round temperature prediction[END_REF]. The ANN models were able to achieve low prediction error when using a large dataset for training consisting of 1.5 million records. To alleviate the issues related to the high dimensionality estimation problem, a support vector regression model was proposed for the prediction task using a reduced training set (300, 000 records) [START_REF] Chevalier | Support vector regression with reduced training sets for air temperature prediction: a comparison with artificial neural networks[END_REF].

In this paper we present a Bayesian framework for minimum temperature prediction using multiple sources of environmental data. A Mixture of Gaussian Process (MGP) model is then proposed as an alternative to the ANN model for dealing with a reduced amount of data. The idea behind the MGP approach is to use local Gaussian Process regression models on different subsets of the input data and a gating network that is responsible of assigning input data points to one of the local experts [START_REF] Tresp | Mixtures of Gaussian processes[END_REF].

The parameters of the MGP model includes a set of hyper-parameters for each Gaussian process model and the gating network. A Bayesian approach for estimating the model hyper-parameters from data is also presented [START_REF] Shi | Hierarchical gaussian process mixtures for regression[END_REF]. Nevertheless, even for the reduced amount of data, the running time of the algorithm can be prohibitively slow. Therefore, we propose an efficient Hybrid Monte Carlo implementation in a shared memory machine that may overcome some of the estimation issues in high dimensional problems.

As a result, our framework is able to deal with deal with a large number of instances and deliver several predictions at test locations.

Gaussian Processes Regression

Regression problems deal with datasets in the form D = {x, y} n i=1 , where x i is an input vector and y i is a continuous variable representing output data. In our case, the input data contains several environmental variables such as temperature, humidity and solar radiation measured at different times of the day (e.g. 18pm and 15pm) and the output data is the minimum temperature measured at any time of the following next day.

A Gaussian process regression model consists of a non-linear mapping between the input and the output data, such that:

y i = f (x i ) + i (1)
where i ∼ N (0, σ 2 ) represents a zero-mean noise term. More formally, a Gaussian process defines a prior over the function f (x) ∼ GP (0, κ(x, x )) and K is a positive-definite kernel or covariance matrix with elements [K ij ] = κ(x i , x j ). Now, we can write the marginal distribution p(y|x) as:

p(y|x) = p(y|f, x)p(f |x)df (2)
Integrating f in Equation 2 also leaves a normal distribution with covariance K x = K + σ 2 I. If we now observe new data x * , we can write the predictive distribution as follows:

p(f * |x * , D) = N (k T * K -1 x y, k * * -k T * K -1 x k * ) (3)
where k * denotes the vector of covariances between the test point and the training data. Similarly, k * * = κ(x * , x * ) is the covariance function of the test data.

Mixtures of Gaussian Processes

Wireless sensor networks make use of sensor nodes to collect environmental data from multiple locations [START_REF] Wang | Wireless sensors in agriculture and food industryrecent development and future perspective[END_REF][START_REF] Ruiz-Garcia | A review of wireless sensor technologies and applications in agriculture and food industry: State of the art and current trends[END_REF]. Instead of having one single dataset, our interest is to gather data from different farming sites and perform centralized learning using a horizontally partitioned dataset Compared to the cost of analyzing single datasets, the computational complexity of learning from a central dataset is increased [START_REF] Peteiro-Barral | A survey of methods for distributed machine learning[END_REF]. This is specially problematic in Gaussian processes calculations that uses inversion of matrices and linear solvers that require approximation methods for large datasets [START_REF] Neal | Regression and classification using Gaussian process priors[END_REF]. One of such approaches is the Mixture of Gaussian Processes (MGP), that builds several regression or classification models on different subsets of the data. In this case, the complexity of performing large-scale training is reduced since each model can be trained independently and because of the centralized nature of the data collection mechanism, there is no requirement on the number of models to be trained.

D = D 1 ∪ D 2 . . . ∪ D n .
In this case, we consider a set of models which are responsible of different groups of measurements S = {S 1 , . . . , S M } [START_REF] Shi | Hierarchical gaussian process mixtures for regression[END_REF]. A gating network parametrized by a discrete latent variable z is used to assign data points to any of the M local experts with prior probability π m , such that p(z i = m) = π m . The MGP prior can be now written as follows:

f (x) ∼ M i π i GP (0, κ i (x, x )) (4)
The parameters of the MGP model include a set of hyper-parameters for each model Θ = (θ 1 , . . . , θ M ) and the gating network π = (π 1 , . . . , π M ). In this paper we consider kernels based on the squared exponential function:

κ(x i , x j ) = σ 2 f exp(- 1 2 d ν 2 d (x id -x jd ) 2 ) + σ 2 y δ ij (5)
where θ = (σ f , σ y , ν 1 , . . . , ν D ) and D being the dimensionality of the input vector x. The posterior distribution of the model and the gating network parameters takes the form:

p(Θ, π|D) ∝ p(Θ, π)p(D|Θ, π) (6)
Unlike Equation 3, in the MGP model we cannot compute the marginal posterior analytically. Instead, we could use the E-M algorithm to iteratively maximize the complete data log-likelihood log p(D|Θ, π) [START_REF] Yang | An efficient EM approach to parameter learning of the mixture of gaussian processes[END_REF]. Instead, we sample from the marginal posterior distribution (see Equation 6) using a Hybrid Monte Carlo approach. The iterative procedure is given as follows:

1. Given the current kernel hyper-parameters Θ, sample the gating network parameters from the posterior distribution p(z i = m| Θ, D) 2. Sample π from a Dirichlet distribution p(π 1 , . . . , π M ) ∼ Dir(δ + c 1 , . . . , δ + c M ), where δ is a concentration parameter and c m is the number of times z i = m for all i. 3. Update Θ using hybrid Monte Carlo sampling.

Distributed Gibbs sampling for latent variable models were discussed in [START_REF] Asuncion | Distributed Gibbs sampling for latent variable models[END_REF]. In this case, we can partition the dataset according to the expert assignments, such that a different processor performs computations on different data subsets S m . GP computations require several linear algebra operations, however authors have noticed that good performance on multi-core machines can be achieved by using threaded BLAS libraries [START_REF] Nguyen | Fast allocation of gaussian process experts[END_REF][START_REF] Chandola | Implementing a gaussian process learning algorithm in mixed parallel environment[END_REF].

Experimental Results

Dataset

In this section we provide experimental results for the distributed minimum temperature prediction using the MGP approach. The data consists of agrometeorological variables collected using Wireless Sensor Networks from 5 different locations in the region of Maule in south central Chile 3 . The data was sampled every 10 minutes but only the mean temperature, mean humidity and mean solar radiation values at 15pm and 18pm were used to predict the minimum temperature for next the day.

Model

The priors used for the gating network and the kernel hyper-parameters follow the settings suggested in [START_REF] Neal | Regression and classification using Gaussian process priors[END_REF][START_REF] Shi | Hierarchical gaussian process mixtures for regression[END_REF]. In order to get the initial values of Θ, we partition the data using k-means and ran 100 iterations of Hybrid Monte Carlo with 10 leapfrog steps. The Hybrid Monte Carlo sampler was done in C + + and the data partitioning and parallel execution was performed in R. In order to ensure that valid random numbers were generated in parallel, the TNRG random number generator was used. This approach combines the flexibility of the R environment for data processing with the fast execution of C + + shared libraries [START_REF] Eddelbuettel | Seamless R and C++ Integration with Rcpp[END_REF].

A number of 1000 iterations and 3 leapfrog sets were used to train the MGP model. Figure 2 compares the predicted and observed minimum temperatures at the training sites.

As shown in Figure 2, each model produces different predictions. Figure 3 shows the predicted minimum temperatures at the test site. Figure 3 shows the predicted minimum temperatures at a single test site.

Table 2 summarizes results using the Root Mean Square (RMS) error for the 3 test sites. It is important to notice that the ensemble RMS error is lower for all test locations. q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q donoso_1 donoso_3 gillmore_2

niceblue_1 niceblue_2 -2 -1 0 1 2 3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3
Observed Minimum Temperatures (C) Calculated Minimum Temperatures (C) cluster q 1 2 Fig. 2: Comparison between observed and calculated next day minimum temperatures at training locations q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q donoso_1 -10 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q niceblue_3 -5 In this paper we combine data from multiple and distributed sources and we are able to predict at previously unseen test locations. A mixture of Gaussian

Fig. 1 :

 1 Fig.1: Distributed environmental data from wireless sensor networks. Data is collected from multiples sites, each one having multiple sensor nodes. Every node reports the same information collected at different spots.

2 Fig. 3 :

 23 Fig. 3: Minimum temperatures at a training and test locations

Table 1 :

 1 Table 1 describes the variables used in the predictive model. In order to create the training and test patterns, each node is used independently. Therefore, in the training data set we incorporate sensor nodes from different locations. Table 1 also shows the locations of the sensor nodes used in the training and test datasets. Variables used for minimum temperature prediction and sensor nodes locations

	x1 Temp15 Mean Temperature at 15hrs
	x2 Hum15 Mean Humidity at 15hrs	Lat	Lon	Site ID
	x3 Rad15 Mean Solar Radiation at	-35.463882 -71.612818 donoso 1
	15hrs	-35.466391 -71.617502 donoso 2
	x4 Temp18 Mean Temperature 18hrs	-35.588004 -71.910842 gillmore 2
	x5 Hum18 Mean Humidity 18hrs	-35.857414 -71.602135 niceblue 1
	x6 Rad18 Mean Solar Radiation at	-35.858761 -71.601540 niceblue 2
	18hrs	-35.858845 -71.601852 niceblue 3
	y M inN ext Next day minimum temper-	-35.013470 -71.432918 canepa 1
	ature

Table 2 :

 2 RMS values for the MGP model at test locations process model is introduced as a method to handle the distributed learning problem. The model has the appealing property of being able to handle multimodal regression or classification tasks. This is a new approach for distributed learning, since most methods used in the literature use locally trained models and then combine the output. In our case, we overcome the problem of having the same number of models and training locations by imposing the number of mixture components. However, this is also one of the limitations of this approach since we need to choose beforehand the number of components. Future work will involve automatic methods for model selection and model averaging procedures for the MGP model.

	Site ID RMS Cluster 1 RMS Cluster 2 RMS Ensemble Mean
	niceblue 3	0.4314506	0.4521971	0.3712023
	canepa 1	0.5649653	0.6597936	0.5737617
	canepa 2	0.7197888	0.6106701	0.5600180
	canepa 3	0.5906937	0.6441202	0.5677543

http://www.agrosense.cl


Marie Pavláková Dočekalová
email: docekalova@fbm.vutbr.cz

Alena Kocmanová
email: kocmanova@fbm.vutbr.cz

Jana Hornungová
email: hornungova@fbm.vutbr.cz

Measuring and Benchmarking Corporate Environmental Performance

Keywords: corporate environmental performance, benchmarking, data envelopment analysis

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Reporting corporate environmental performance is now commonplace among large enterprises. Twenty years ago, it was performed by just a few pioneers, and in the Czech Republic, for example, it can be said with little exaggeration that no companies informed their stakeholders about their environmental performance. In April 2014, the European Parliament adopted a directive that introduces the obligation that companies with more than 500 employees report on their social responsibility and, therefore, the impact of their business activities on the environment. Corporate environmental performance directly affects many corporate stakeholders -employees, consumers, the community, government bodies, etc. These stakeholders create a demand for clear and relevant information to support their decision-making. Traditional performance evaluation systems do not reflect the requirements of corporate stakeholders. The aim of this paper is to integrate the measuring of corporate environmental performance into performance measurement systems by proposing a framework for an environmental performance benchmarking model.

A Theoretical Approach to Environmental Performance

Corporate environmental performance is defined as the impact of the company on the environment. A company's influence on the environment is assessed in terms of harmful activities affecting the environment [START_REF] Lankoski | Environmental and Economic Performance. The Basic Links[END_REF]. ISO 14031 defines environmental performance evaluation as a "process to facilitate management decisions regarding an organization's environmental performance by selecting indicators, collecting and analyzing data, assessing information about environmental performance, reporting and communicating, and periodically reviewing and improving this process" [START_REF]Environmental management -Environmental performance evaluation -Guidelines[END_REF].

There has been a great debate about the relationship between corporate environmental performance and economic performance. Both positive and negative statements can be found in scientific publications. The authors in [START_REF] Russo | A resource-based perspective on corporate environmental performance and profitability[END_REF] concluded that environmental and economic corporate performance are positively linked and that industry growth moderates the relationship, with the returns in environmental performance being higher in high-growth industries. The authors in [START_REF] Kirchhoff | Green business and blue angels: a model of voluntary overcompliance with asymmetric information[END_REF][START_REF] Feddersen | Saints and markets: activists and the supply of credence goods[END_REF][START_REF] Fisman | A Model of Corporate Philanthropy[END_REF] state that economic benefits are attained by the use of brands and advertisements providing information about the sustainability of products, thereby supporting product differentiation. On the other hand, a meta-analysis focusing on 37 empirical studies examining the relationship between environmental and economic performance shows that the results remain ambiguous despite three decades of theoretical and empirical research [START_REF] Horváthová | Does environmental performance affect financial performance? A meta-analysis[END_REF].

Environmental Benchmarking

Benchmarking represents a way of finding quality and achieving success on the basis of organic growth. The approach taken by companies to environmental benchmarking is influenced by the regulatory context, company strategy, product type, corporate and national culture, resource costs and stakeholder demands [START_REF] Rothenberg | Lessons from benchmarking environmental performance at automobile assembly plants[END_REF]. According to [START_REF] Rothenberg | Lessons from benchmarking environmental performance at automobile assembly plants[END_REF], environmental benchmarking can be split into four categories: regulatory, gross emissions, efficiency and life cycle. The principle of benchmarking is included in the ISO 14000 standards relating to environmental management. The process of implementing environmental benchmarking based on the ISO 14001 EMS standard is described in, for example, [START_REF] Matthews | Environmental management systems for internal corporate environmental benchmarking[END_REF]. Corporate environmental performance is benchmarked against:

• the organization's environmental policy, • environmental objectives, • environmental targets, or • other environmental performance requirements [START_REF]Environmental management systems -Requirements with guidance for use[END_REF].

The Best Available Techniques (BAT) can also be used as a benchmark for environmental benchmarking [START_REF] Tokos | An integrated sustainability performance assessment and benchmarking of breweries[END_REF]. At the EU level, BAT for selected industrial and agricultural activities are defined and quantified in Best Available Techniques Reference Documents (BREF).

The use of the following factors is an extremely common approach in setting benchmarks:

• the average value in an industry,

• the best-performing company in an industry,

• the value of the previous period.

Performance can also be measured against the production frontier. Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) are methods that can be used for the purposes of estimating the production frontier. SFA has been defined by the authors [START_REF] Aigner | Formulation and estimation of stochastic frontier production function models[END_REF][START_REF] Meeusen | Efficiency estimation from Cobb-Douglas production functions with composed error[END_REF]. SFA is based on the estimation of a stochastic frontier production function by econometric methods. DEA has been defined by the authors [START_REF] Charnes | Measuring the efficiency of decision-making units[END_REF] and is based on the estimation of a production function by techniques of linear programming. The DEA method was used for performance evaluation and benchmarking by, for example, [START_REF] Zhu | Quantitative Models for Performance Evaluation and Benchmarking: DEA with Spreadsheets[END_REF][START_REF] Post | Performance benchmarking using interactive data envelopment analysis[END_REF].

Methodology

An environmental performance benchmarking model was created and tested on real corporate data. The given data was taken from large Czech manufacturing companies for the year 2012. This industry was chosen because manufacturing companies exert a significant impact on the environment. The sample is described in Table 1. The first step in creating a corporate performance measurement system is identifying relevant key performance indicators (KPIs). Environmental KPIs are often nonfinancial metrics. Mathematical and statistical methods were used to determine the environmental performance indicators. For a detailed description of the construction of environmental KPIs see [START_REF] Kocmanová | The Construction of Environmental Indicators for Determination of Performance of ESG Indicators to Support Decision-Making of investors[END_REF][START_REF] Kocmanová | Environmental, Social and Governance (ESG) Key Performance Indicators for Sustainable Reporting[END_REF][START_REF] Hřebíček | Corporate Key Performance Indicators for Environmental Management and Reporting[END_REF].

Criterion

Originally, the set of environmental KPIs consisted of seven variables: Environmental Non-investment Costs, Air Emissions, Emissions of Greenhouse Gases, Energy Consumption, Water Consumption, Waste Production and Hazardous Waste Production.

The first step in data processing was a quality check carried out in order to find out whether there were any erroneous or missing data. The following statistic measures were computed in order to obtain a basic knowledge of performance indicators: mean, range, standard deviation and variance. A correlation analysis was performed in order to understand the relationship between the variables. High values of pair correlation coefficients, i.e. |r| > 0.8, suggest multicollinearity. The variance inflation factor (VIF) was also used to detect multicollinearity. The VIF can be detected from an inversion matrix of the correlation matrix. Redundant KPIs were removed from the model. The reduced set of environmental KPIs then contained: Emissions of Greenhouse Gases, Water Consumption and Waste Production.

In this paper corporate performance is benchmarked against the production frontier -efficient frontier, which is constructed by efficient companies. The production frontier is estimated by Data Envelopment Analysis (DEA). The DEA method potential consists of identifying the best practice in the industry. The DEA model is based on assessing the quantity of consumed inputs by the produced outputs and estimation of the production possibility frontier. Three environmental KPIs (Emissions of Greenhouse Gases, Water Consumption and Waste Production) are considered as inputs. The goal of every business is to minimize values of these KPIs. Gross Value Added (GVA) is considered an output. GVA is the difference between the total production of goods and services and consumption (the value of goods and services consumed in production) -this approach is taken from the Eco Management and Audit Scheme (EMAS III). An input-oriented model with constant returns to scale (CCR model) is computed. The mathematical background of the model is described in [START_REF] Charnes | Measuring the efficiency of decision-making units[END_REF].

The mathematical model of the primary input-oriented CCR model: , max

i i x v y u ∑ ∑ ≤ , 1 = ∑ jq m j j x v ε ≥ i u , ε ≥ j v . ( 2
)
Where z is the coefficient of the technical efficiency of a company C q . u i and v j are individual weights assigned so as to maximize efficiency and ε is an infinitesimal constant that ensures that all weights of inputs and outputs are positive.

The professional optimization program Frontier Analyst from the company Banxia Software, which is very user friendly, was used for the model. The simplest method from the viewpoint of demands on software is the computation of DEA models with the use of MS Excel or using programmed applications such as DEA-Excel by Professor Jablonský and DEA Excel Solver by Professor Zhu. The specialized optimization software Lingo developed by the company Lindo Systems uses a special modelling language for notation. The model can also be imported into the program in MPS for-mat. MPL for Windows from the company Maximal Software is an open optimization system.

Results and Discussion

Figure 1 shows an environmental performance measurement system based on a benchmarking model. An environmental performance measurement system must be part of a corporate performance measurement system. In order to provide the whole picture about corporate performance, the corporate performance measurement system must also consider the social dimension in addition to the environmental and economic dimension. A balance between these three factors of corporate performance creates a triple-bottom-line and brings sustainability to organizations [START_REF] Elkington | Cannibals with Forks: The Triple Bottom Line of the 21st Century[END_REF][START_REF] Kocmanová | Construction of the economic indicators of performance in relation to environmental, social and corporate governance (ESG) factors[END_REF][START_REF] Chvátalová | Corporate Sustainability Reporting and Measuring Corporate Performance[END_REF]. • relative corporate performance and benchmarks,

• the best practice in the group of analyzed companies.

Table 2 presents the relative performance of all companies in the group given by the coefficient of technical efficiency derived from the DEA model. Performance is relative because it expresses the efficiency of the company within the studied group of companies. If it is equal to 100 %, this means that there is no company in the group that has a better performance. If the value is less than 100 %, there is at least one other company which is more effective. Both absolute and relative potential improvements are part of the output of the DEA model calculations. Table 2 gives relative potential improvements. The best-performing companies are "A", "G" and "Q". These efficient companies are referred to as "peer companies" and determine the efficiency Let us analyze the performance of company "H" further. This company is not effective from the viewpoint of environmental KPIs per Gross Value Added. Table 3 gives the benchmarks for the environmental KPIs that will make sure that the company "H" becomes effective at the current level of Gross Value Added which is 378,433 thousand CZK. Individual environmental KPIs together with their benchmarks can be visually integrated in an AMOEBA graph. In Figure 2, the benchmark is considered 100 % and the actual performance of company "H" is relative to the benchmark. Users can see at a glance that Water Consumption significantly exceeds its benchmark and is therefore the weakest point in the environmental performance of this particular company.

KPIs

Fig. 2. Environmental performance of company "H"

The graphic presentation of environmental KPIs in the context of their benchmarks makes the model outputs clear and easy to understand because it is obvious at a glance which KPIs effect environmental performance positively and negatively. The model outputs allow users to identify weaknesses in performance and to quantify performance gaps, for which reason the environmental performance benchmarking model can serve its users as a basis for decision-making and performance management. Information about environmental performance should be an integral part of corporate reporting [START_REF] Hřebíček | Voluntary Reporting in the Czech Republic and Indicators of Sustainable Development[END_REF][START_REF] Hřebíček | Voluntary Company Assessment Report on the Linkages between Environment[END_REF].

Conclusions

A framework for an environmental performance benchmarking model was proposed in this paper. Data Envelopment Analysis is the benchmarking method used in the model. This approach has the following advantages: the performance of the company is expressed in one number given by the coefficient of technical efficiency, the model quantifies necessary improvements -performance gaps and also identifies the "best practice" in the particular industry. Information then becomes an important source material for quality decisions by the management. The weakness of the model is that performance is relative, and adding another company to the analyzed group will change the environmental performance.

Fig. 1 .

 1 Fig. 1. Environmental performance measurement system Metrics (environmental KPIs) and their values (data) are entered into the environmental performance benchmarking model. The outputs of the model are identification of:

Table 1 .

 1 Research sample(N -number of companies, % -percentage in the sample)

	N	%

Table 2 .

 2 Corporate Performance -results of the environmental performance benchmarking model

	The Environment
	Corporate Performance Measurement System
	Environmental Performance Measurement System
	Metrics & Data	Benchmarking method	Relative performance identification

Table 3 .

 3 Environmental performance of company "H"

		Emissions of Greenhouse Gases (t)	Water Con-sumption (m 3 )	Waste Pro-duction (t)
	Actual performance	0.01	4116.00	308.57
	Benchmark	0.01	1873.07	214.04

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011

Acknowledgements

This paper is supported by grant no. 14-23079S Measuring Corporate Sustainability in Selected Sectors from the Czech Science Foundation.


Lu Xia

Qingchun Yu

Youhua Chen

Maohua Li

Guofu Xue

Deji Chen

GeneralBlock: A C++ Program for Identifying and Analyzing Rock Blocks Formed by Finite-sized Fractures

Keywords: GeneralBlock, finite-sized fracture, rock block identification and analysis adfa, p. 1, 2011

GeneralBlock is a software tool for identifying and analyzing rock blocks formed by finite-sized fractures. It was developed in C++ with a friendly user interface, and can analyze the blocks of a complex-shaped modeling domain, such as slopes, tunnels, underground caverns, or their combinations. The heterogeneity of materials was taken fully into account. Both the rocks and the fractures can be heterogeneous. The program can either accept deterministic fractures obtained from a field survey, or generate random fractures by stochastic modeling. The program identifies all of the blocks formed by the excavations and the fractures, classifies the blocks, and outputs a result table that shows the type, volume, factor of safety, sliding fractures, sliding force, friction force, cohesion force, and so on for each block. It also displays threedimensional (3D) graphics of the blocks. With GeneralBlock, rock anchors and anchor cables can be designed with the visual assistance of 3D graphics of blocks and the excavation. The anchor, cables, and blocks are shown within the same window of 3D graphics. The spatial relationship between the blocks and the anchors and cables is thus very clear.

Introduction

A computer program for identifying and analyzing rock blocks formed by finite-sized fractures is a useful tool in many problems involving fractured rocks. If a rock is slightly fractured, the blocks are infrequent in the rock mass. In this case the number, dimensions and locations of the blocks are a major concern. The rock blocks, which are commonly buried in the rock mass under natural conditions, may be exposed by artificial excavations and may fall into the openings formed by the excavation. Many authors have discussed the possibilities of the occurrence of unstable blocks caused by the intersection between fractures and excavations [START_REF] Warburton | Vector stability analysis of an arbitrary polyhedral rock block with any number of free faces[END_REF][START_REF] Goodman | Block theory and its application to rock engineering[END_REF][START_REF] Lin | Static analysis of the stability of three-dimensional blocky systems around excavation in rock[END_REF][START_REF] Hoek | Support of underground excavation in hard rock[END_REF]. On the other hand, if a rock is heavily fractured, the rock may be visualized as an assemblage of isolated rock blocks. In this case, the mechanical behavior of the rock is often simulated as the behavior of a system of rock blocks [START_REF] Cundall | Formulation of a three-dimensional distinct element model-part 1: A scheme to detect and represent contacts in a system composed of many polyhedral blocks[END_REF][START_REF] Shi | Discontinuous deformation analysis: A new numerical model for the statics and dynamics of block systems[END_REF][START_REF] Wu | A development of the discontinuous deformation analysis for rock fall analysis[END_REF].

Because the geometry of the blocks exerts a strong influence on the mechanical properties of the rocks, and the geometry of the blocks is determined by the geometry of the fractures, many authors have studied the relationship between the geometries of fractures and rock blocks. Basically, their approaches may be divided into two types. The major difference between the two methods is whether or not the shape of the fractures has a definition prior to block identification. In the first method, the shapes of the fractures are defined before block identification; this approach was used by many authors [START_REF] Lin | Geometrical identification of threedimensional rock block system using topological techniques[END_REF][START_REF] Ikegawa | A novel automatic identification system for threedimensional multi-block systems[END_REF][START_REF] Jing | Block construction for three-dimensional discrete element models of fractured rocks[END_REF][START_REF] Ohnishi | 3D block analyses for fractured rock[END_REF][START_REF] Lu | Systematic identification of polyhedral blocks with arbitrary joints and faults[END_REF], and is adopted in GeneralBlock. In this method, the fractures can cross each other and terminate in intact rock; an individual fracture can be contributive, noncontributive, or partly contributive. The second method uses a block generation language [START_REF] Heliot | Generating a blocky rock mass[END_REF][START_REF] Empereur-Mot | OBSIFRAC: database-supported software for 3D modeling of rock mass fragmentation[END_REF], whereby the fractures always form fully connected networks and all of the fractures are completely contributive.

GeneralBlock is written in Visual C++ 6.0 using the OpenGL library. In the program, the modeling domain or the excavation may be of arbitrarily complex shape. The rock and fractures may be heterogeneous. The fractures may be either deterministic fractures obtained from a field survey or random fractures generated by stochastic modeling. The program identifies all blocks formed by the excavations and the fractures, classifies the blocks, and outputs a result table that shows the type, volume, factor of safety, sliding fractures, sliding force, friction force, cohesion force, and so on for each block. It also outputs three-dimensional (3D) graphics of each block. With GeneralBlock, anchors and cables may be added, and are shown with the blocks within the same window of 3D graphics, making the spatial relationship between the blocks, anchors, and cables very clear. The user can therefore edit rock anchors and anchor cables with the visual assistance of 3D graphics of blocks and excavation faces.

Algorithm

Some authors have discussed the issues in block identification. In initial studies, the fractures were assumed to be infinitely large, and hence, the blocks were limited to convex shapes. Lin et al. [START_REF] Lin | Geometrical identification of threedimensional rock block system using topological techniques[END_REF] and Ikegawa and Hudson [START_REF] Ikegawa | A novel automatic identification system for threedimensional multi-block systems[END_REF] presented their block identification approaches for finite extended fractures based on topological concepts. In these approaches, the intersections of the fractures are calculated first to define the sets of vertices, edges, and faces. Then, the sets were regularized to discard any isolated and dangling vertices, edges, and faces. Finally, the blocks were identified through boundary-chain operations of the closed surfaces and the Euler-Poincare formula for a polyhedron. Similar methods have also been discussed in detail [START_REF] Jing | Block construction for three-dimensional discrete element models of fractured rocks[END_REF][12] [START_REF] Yu | Analyses for fluid flow and solute transport in discrete fracture network[END_REF]. GeneralBlock was developed based on the procedure presented by Yu et al. [START_REF] Yu | A generalized procedure to identify three-dimensional rock blocks around complex excavations[END_REF]. It decomposes a complex block into a finite number of convex element blocks during the identification process. The adoption of the concept of an element block makes it possible to represent a complex block using an assemblage of simple convex blocks, and thus, to transform the difficult calculations involved in identifying the blocks around complex excavations into simple calculations using convex geometry. In the procedure, the complexity of the modeling domains and the heterogeneity of a material are taken fully into account.

In the procedure, a complex modeling domain is initially divided into convex subdomains, and each subdomain is further decomposed into convex blocks with infinite fractures. Then, the fractures are restored to finite dimensions, and the domain is reassembled by combining the subdomains into a full domain. Thus, the procedure is comprised of the following major steps:

1. Subdividing the modeling domain into a finite number of convex subdomains; 2. Removing the non-contributive fractures; 3. Decomposing the subdomains into element blocks with the fractures, which are temporarily taken as infinitely large at this stage; 4. Restoring the infinite fractures to finite; 5. Assembling the modeling domain.

User Interface

The solution of a block identification and analysis problem with GeneralBlock usually includes the following major steps.

1. Open a new project. This creates a new directory in which to store the data for the problem. All ensuing data for this problem will be stored in this directory. 2. Define the modeling domain.

Defining the Model Domain

The first step in block identification and analysis is defining the model domain. A major process in the definition is to decompose the problem domain into a number of convex subdomains. The subdivision of a problem domain is similar to the subdivision of a model domain into elements in the FEM, and the geometry and the associated data structure of a subdomain are also similar to those of an element in the FEM technique. The subdomains, surface polygons, and vertices must be numbered uniquely, and no overlap among the subdomains or surface polygons is allowed.

Manually decomposing a problem domain into subdomains is not difficult, even for a problem domain with a very complex shape. However, it is not simple to code a computer program to automatically decompose different-shaped rock masses. Thus, GeneralBlock provides graphical user interfaces for the subdivision of some common rock structures, including slopes, tunnels, and underground caverns. For these common structures, the user only enters a few data values through a graphical window to define the rock masses. The subdivision of the domain is performed automatically by GeneralBlock, and the user is not required to be aware of it.

The Window Defining the Model Domain.

GeneralBlock provides graphical interfaces for three kinds of structures: slope, tunnel, and underground cavern. Accurately speaking, a tunnel is a kind of underground cavern. As an example, figure 1 shows the actual underground powerhouse cavern 311.3 m long and 71 m high in the Three Gorges Project. In the lower part of the powerhouse, the distance between the two vertical sidewalls is 31 m, and in the upper part the span of the crown is 32.6 m.

Fig. 1.

The window defining the model domain (the example of the underground powerhouse cavern in the Three Gorges Project defined by GeneralBlock)

The Format of "model_domain.dat"

The data defining the model domain and sub-domains are all stored in an ASCII file named "model_domain.dat". This file can be edited by a text editor. The user can prepare it and have GeneralBlock read it to perform block analyses.

The rock masses and the excavations of study domains may have various shapes, e.g., slopes, tunnels, and their combinations. However, all of the data arising from domain definitions and subdivisions are stored in this file, and all of them always have the same format. This file includes the following data.

1. Direction of x-axis (in deg), excavation type (slope = 0; tunnel = 1; cavern = 3; complex = 4); 2. Number of the nodes to define the domain, number of the faces to define the domain;

3. Number of the nodes to define each of the faces;

4. An index for each of the faces;

5. An index to indicate the mechanic property of each face (common = 0; fix = 1; free = 2); 6. A vertex list for each of the face; 7. Three-dimensional coordinates for each vertex; 8. The number of sub-domains, rock density of each sub-domain; 9. The number of faces for each sub-domain; 10. Face list for each sub-domain.

Entering and Stochastic Modeling of Fractures

For GeneralBlock, the fractures are divided into two types: deterministic fractures and stochastic fractures. Deterministic fractures usually arise from excavation mappings; their geometric and mechanical parameters are determined through various measurements. Stochastic fractures are usually obtained through random modeling; their parameters are generated from a stochastic model. Deterministic fractures are often larger in scale than stochastic ones. Although deterministic and stochastic fractures may arise from different sources, they have the same defining method: each fracture is defined by nine parameters, including the x, y, and z coordinates of the disc center, dip direction, dip angle, radius, aperture, cohesion coefficient, and friction angle. In block analysis, the two kinds of fractures are treated in the same way. After inputting deterministic fractures or simulating stochastic fractures, filtering fractures is necessary. The objective of fracture filtering is to find and eliminate the fractures that are evidently non-contributive to block formation. And fractures below the size specified by user may be neglected in block identification. For most practical engineering, the blocks close to the excavations are usually of more concern than more distant ones. Thus, it is often the case that only the fractures near the excavations are taken into account. In GeneralBlock, the distance of a fracture from an excavation is an integer. If the fracture intersects directly with the excavation, the distance is zero; if the fracture does not intersect directly with the excavation but is connected indirectly to the excavation through one fracture, the distance is one; the rest may be deduced by analogy.

The remaining fractures after fracture filtering are stored in the file "contribu-tive_fracture_xyzabr.dat". GeneralBlock reads all fractures in this file when performing block identification.

Calculating and showing the traces of fractures should be done next. A trace is an intersection line between a fracture and a rock surface, e.g., an excavation or a natural exposure. Calculating the traces of fractures on specified excavations and displaying them through 3D graphics is a useful function of GeneralBlock. In practical rock engineering, when a fault is encountered by a borehole or an exploration tunnel, the engineering geologist is often confronted with the problem of predicting where the fault would be reencountered by other excavations. Figure 2 shows the example traces shown in this figure are the traces of main faults in vaults on excavation of underground powerhouse in the Three Gorges Project.

Identifying Blocks and Displaying Results

The calculation of block identification completes the following tasks: block identification, volume calculation, block classification, removability analysis, sliding fracture determination, and force calculation (sliding force, friction, cohesion, and support).After the completion of block identification, the user can investigate the results. Figure 3 shows the window displaying the results of block analysis. All of the blocks are listed in the table and sequenced according to their volumes. The type, volume, safety factor, sliding fracture, sliding force, friction, cohesion, and the support forces provided by rock bolts and anchor cables of each block are displayed in the table. All data of block results save in the file "Result_table.dat".

Conclusion

GeneralBlock has been developed for identifying and analyzing the rock blocks formed by complex excavations and finite-sized fractures. It is a useful software tool in many ways for the study of fractured rocks. It may be used to identify and analyze the stability of rock blocks arising from rock excavation. It may also be used to investigate the size and geometry of rock blocks, two important factors affecting the mechanics of fractured rocks.

GeneralBlock was developed in C++ with a friendly user interface. In the program, the modeling domain or the excavation can be very complex in shape, such as slopes, tunnels, underground caverns, or their combinations. The heterogeneity of the materials was taken fully into account. Both the rocks and the fractures can be heterogeneous. The fractures can be either deterministic fractures obtained from a field survey, or random fractures generated by stochastic modeling. The program: identifies all of the blocks formed by the excavations and the fractures; classifies the blocks and outputs a result table that shows the type, volume, factor of safety, sliding fractures, sliding force, friction force, cohesion force, and so on, of each block; and outputs threedimensional (3D) graphics of each block. With GeneralBlock, rock anchor and anchor cable design can be performed with the visual assistance of 3D graphics of blocks and excavation faces. The anchors, cables, and blocks are shown within the same window of 3D graphics. Consequently, the spatial relationships between the blocks and the anchors and cables are very easy to understand. Fig. 3. The window displaying the results of block analysis (3D graphics of blocks in vaults of underground powerhouse in the Three Gorges Project using GeneralBlock)

3 .

 3 Input deterministic fractures and/or generate stochastic fractures. 4. Calculate and observe the traces of the fractures. 5. Filter fractures. 6. Identify blocks and analyze their stability. 7. Display analysis results. 8. Design rock anchors and anchor cables.

Fig. 2 .

 2 Fig. 2. The window for calculating and displaying the trace of fractures (traces of main faults in vaults on excavation of underground powerhouse in the Three Gorges Project)

Acknowledgements

This study was financially supported by the National Natural Science Foundation of China (Grant No.40372134, No.40772208, No.41272387) and the Fundamental Research Funds for the Central Universities (Grant No.2652011305).


Georg Neubauer

Hermann Huber

Armin Vogl
email: armin.vogl@bmi.gv.at

Bettina Jager

Alexander Preinerstorfer

Stefan Schirnhofer

Gerald Schimak
email: gerald.schimak@ait.ac.at

Hermann Neubauer

Bettina Huber

Alexander Jager

Stefan Preinerstorfer

Schirnhofer

On the Volume of Geo-referenced Tweets and their Relationship to Events Relevant for Migration Tracking

Keywords: migration, tweets, geolocation, early indicator, push factor

Migration is a major challenge for the European Union, resulting in early preparedness being an imperative for target states and their stakeholders such as border police forces. This preparedness is necessary for multiple reasons, including the provision of adequate search and rescue measures. To support preparedness, there is a need for early indicators for detection of developing migratory push-factors related to imminent migration flows. To address this need, we have investigated the daily number of geo-referenced Tweets in three regions of Ukraine and the whole of Japan from August 2014 until October 2014. This analysis was done by using the data handling tool Ubicity. Additionally, we have identified days when relevant natural, civil or political events took place in order to identify possible event triggered changes of the daily number of Tweets. In all the examined Ukrainian regions a considerable increase in the number of daily Tweets was observed for the election day of a new parliament. Furthermore, we identified a significant decrease in the number of daily Tweets for the Crimea for the whole examined period which could be related to the political changes that took place. The natural disasters identified in Japan do not show a clear relationship with the changes in the degree of use of the social media tool Twitter. The results are a good basis to use communication patterns as future key indicator for migration analysis.

Introduction

In recent years the importance of migration has increased considerably in EU member states, and is seen as one of the major challenges by the European Union. A number of events have led to issues of migration becoming more important in Europe, including the "Arab spring" crisis situations in North-Africa, natural disasters such as draughts or large scale flooding, and wars and warlike situations in the Middle-East, Sub-Sahara and South Asia. These events have caused an overwhelming migration wave with a steadily growing number of refugees who are trying to reach European countries. European societies are struggling with the socioeconomic impact of these crises. Consequently, these societies are searching for political solutions which can cope with the humanitarian responsibilities, as well as the protection of their demographic and economic structures.

It is important for possible destination countries to be well-prepared for such large influxes of refugees so they can grant migrants a liveable and worthy reception. As an example, the southern border of Italy is facing a soaring migratory wave, as more than 100.000 illegal migrants had to be rescued in open-sea during 2014 and brought to the Italian coast. This mass immigration caused a socioeconomic crisis in this region. Human disasters, such as the drowning of hundreds of human beings, could be avoided if better information is available to alert search and rescue teams in timely manner. Currently, European authorities seem ill-prepared to cope with this overwhelming situation and therefore new information sources have to be found in order to better observe possible migration-causing incidents. Early and reliable indicators on migration movements are therefore imperative for multiple stakeholders such as border police forces or first responders in the field. So far, data on volume and flow of migration is mainly inconsistent, outdated or does not exist at all. This paper investigates the suitability of using the volume of geo-referenced Tweets versus time as potential early indicators of migration movements, whilst taking into account both long term impacting situations and specific high-profile events.

Related Work

In recent years, extensive research was done in identifying events by studying human communication behavior on social media. In this context the micro-blogging platform Twitter has been the focus of much research. The benefit of gathering information about events from social media services, such as Twitter, relates to the fact that people turn to these platforms in the face of exceptional circumstances [START_REF] Starbird | Crowdwork, Crisis and Convergence: How the Connected Crowd Organizes Information during Mass Disruption Events[END_REF]. This study stresses that irrespective of whether these events are emergencies, natural disasters or political protests, a significant factor for changes in communication patterns is a disruption to normal routines. Besides a certain content of Twitter messages, [START_REF] Stefanidis | Harvesting ambient geospatial information from social media feeds[END_REF] pointed out, that volunteered information with geographic footprints is displaying people's current position, which can facilitate a wide range of possibilities to support situational awareness. The tracking of recent trends in migration patterns by using geo-located data gathered from Twitter has been discussed in [START_REF] Zagheni | Inferring international and internal migration patterns from Twitter data[END_REF] as a specific type of application.

Based on the monitoring of real-time migration flows, statements can be made on migration trends, i.e. increasing or decreasing mobility from a certain country to OECD countries. Political and civil events are frequently chosen as research use cases. An analysis of Twitter data in the reference period of five months exposed a relation between communications behaviour and the occurrence of exceptional events during the troubled time. As a main result of [START_REF] Neubauer | Detecting Events in Egypt Based on Geo-Referenced Tweets[END_REF], highly publicised events in Egypt have been reflected in the amount of communication via Twitter in a certain spatial area. Furthermore, the paper states that the amount of communication in different Egyptian cities is comparable.

Another study focusing on the Middle East was made by [START_REF] Khanwalkar | Content-Based Geo-Location Detection for Placing Tweets Pertaining To Trending News on Map[END_REF] and showed the potential for the detection of trending topics on the basis of content-based geo-location of Arabic and English language Tweets. An extensive range of applications has been presented by [START_REF] Ifrim | Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering[END_REF], who have employed techniques for the SNOW Data Challenge 2014. By combining aggressive filtering of Tweets with hierarchical clustering of Tweets, events around the US presidential elections in 2012 and the recent events in Ukraine, etc. have been detected.

Developments in the Aftermath of the Ukrainian Conflict

In the course of the escalating conflict between Russia and Ukraine in the early 2014, a Ukrainian crisis emerged. Accompanied by demonstrations at the Independence Square in Kiev a strong commitment to Europe had been expressed by Ukrainian protesters, which has acquired visibility during the Euromaidan protest wave. Although the situation in the Crimea has seemed to calm down since the Crimean status referendum in March 2014, [START_REF]Report on the human rights situation in Ukraine[END_REF] pointed out, that the annexation of the Crimea has caused radical changes, especially for ethnic Ukrainians, Crimean Tatars and representatives of minority groups generally. An increasing number of incidents have been recorded in the east and Crimea, which have been identified as an important factor for emigration from the Crimea. A research of the conflict events in the archive of Center for Strategic and International Studies (CSIS) 1 delivers therefore a huge number of hits. Table 1 illustrates the most relevant events for each category summarized on a monthly basis. The categories "civil events" and "political events" comprise the majority of results. In particular the combats in the eastern Ukraine have been highly publicized. The majority of events can be clearly attributed to one of the pre-defined categories. Solely, the category "natural events" remains vacant, which can be explained by the overwhelming media response to human induced events or the fact, that there were no natural induced disasters in the period from August 2014 to October 2014.

Environmental Events in Japan

Japan is one of the countries most affected by natural disasters. Due to its geographic location in the pacific in the geologic fracture zone of four tectonic plates, earthquakes happen frequently in the Japanese archipelago. Since Japan has over 100 active volcanoes eruptions are also occurring occasionally in Japan. Other relevant disasters caused by natural events in Japan are extreme temperatures, floods, storms (e.g. tropical cyclones) and mass movements [8]. Japan leads in the list of economic damage due to earthquakes in the world from 1900 up to the present day with the huge number of 360 billion US $ [9]. In the actual World Risk Report [START_REF]World Risk Report 2014[END_REF] Japan is in the top five countries with the highest urban risk. Urban risk is defined as the product of urban vulnerability and urban hazards. Several natural events occurred in Japan during the observation period from August 2014 up to October 2014, which can be seen in Table 2. These natural events were extracted using the search engine GLIDEnumber [START_REF] Glidenumber | [END_REF].

Data Acquisition

In contrast to many other platforms, Twitters data access policy is quite liberal. The programming interface provides access to 1% of the twitter traffic on a just-in-time basis. Users are free to downstream either a random sample or provide data filters in order to decrease data volume and ensure that Tweets match various criteria like the presence of certain hashtags. However, if the data volume after the application of filters still exceeds the 1% threshold, twitter randomly truncates the subset to meet the data limitation policy. In order to examine spatiotemporal frequencies, we applied a geo-location filter to only download Tweets that feature geographic positioning attributes in terms of latitude and longitude. In this way, Twitter provides roughly 7 million geo-referenced Tweets a day, originating from mobile devices equipped with GPS sensors.

During the three month evaluation period we downloaded 698 million Tweets (261GB). AIT has developed a data analysis tool called Ubicity in order to extract, aggregate and analyse social media data. It routes the data between all kinds of data stores and analysis components like search engines, graph databases or mobile phones. We used its geo-polygon query capability to extract the tweets emitted in certain areas of the Ukraine and Japan.

Data Analysis

We approximated the national borders of the Ukraine and Japan and retrieved all of the collected messages tweeted within the geo-polygons. In the case of Ukraine, we subdivided the territory into three subareas with respect to the distribution of the Russian language according to a national census in 2001 [START_REF]Available: The main advantage is the independence in terms of language understanding. Advanced analysis might also take term frequency and co-occurrence as well as hashtags and network relations into account[END_REF]. Polygon (a) in Fig. 1 depicts the area in which less than 10% of the population identified Russian as their native language. Polygons (b) and (c) depict areas with a rate higher than 50%. Area (b) encompasses the administrative districts Donetsk and Luhansk; (c) encompasses the Crimea peninsula. We selected the three Ukrainian areas because of their geopolitical importance in the conflict between the Ukrainian government and the Pro-Russian separatists. In the period of investigation, we leached from a total number of 698 million Tweets (261GB), 1.1 million Tweets in Ukrainian areas and 25.9 million in the Japanese polygon. The geo-polygon query consists of multiple latitude/longitude pairs to approximate the borders of the respective country. As our twitter stream was partly interrupted due to maintenance work, we identified those interruptions of at least one hour and excluded affected days from our investigation. Our analysis is based on the spatiotemporal dimension only. The main advantage of this approach is that it is language and text understanding independent. Advanced analysis might also take term frequency and co-occurrence as well as hashtags and network relations into account. Depending on the use case we identified tight filters as a promising solution. Keeping the data rates low (below 1% of the overall traffic) avoids spurious frequency changes in the first place.

Results

Statistical tests highlight, that the communication behavior differs significantly between Japan, the Crimea and the regions in the Ukraine (Table 3). In comparison to the Japanese Tweets, the number of Tweets is lower by a factor of rounded 30 in the region of the West of Ukraine (see the mean values). However these numbers do not take the difference in the number of inhabitants between the regions from Ukraine and Japan into account.

Results Obtained for the Crimea, the East and the West of Ukraine

As illustrated in Fig. 1 (left picture), the analysis of Tweets in the Crimea, the East and the West of Ukraine considers the three regions separately. Fig. 2 displays that the course of the curve shows a permanently downward trend from the beginning of the reference period to the end. Apart from the peaks in the second half of August, no comparable extent of Tweets could be reached in later stages.

Fig. 2. Number of Tweets from August 2014 to October 2014 in Crimea

The flat curve shape of the number of Tweets (not shown here) indicates a moderate increase over the period of three months in the East of Ukraine. Determined by daily peaks, the events around the foray of Ukrainian forces into Lugansk and Donetsk are reflected in the communication identified in the eastern region of the Ukraine. Aside from the middle section of the curve in Fig. 3, a permanent decrease of the number of Tweets per day in the West Ukraine can be recognized. The line is slightly curved and the most disruptive events are not clearly reflected therein. Mirrored by monthly top rates a marked increase of communication can be noticed at the beginning of September 2014. In this time frame, NATO became more visible in Ukrainian conflict and in general, the largest number of political events had been observed regarding, foreign sanctions and economic degradation.

Results

Obtained for Japan

The timeline of the daily number of geo-located Tweets in Japan describes a linear decrease over the whole period (Figure not shown). An interesting result is that the communication behavior around all the tropical cyclones is located over the trend line, but after the landslide no such trend is recognizable. This might be explained by the missing data from our dataset, which was caused by system maintenance. In this time period no political events with an increase of Twitter communication were found. On a culture-bounded basis, Japan naturally exhibits a higher number of Tweets when compared to the other observed areas -the Crimea, East and West Ukraine.

Discussion

Discussion of Communication Patterns Related to Natural Events

Analysis of the Tweets from Japan showed no clear patterns. Five major natural disasters and three political events were identified in the period of investigation. Looking at the two cyclones on August 8 th and October 12 th 2014, an increase of about 10% in number of Tweets was observed the day after the event. However, a decrease of about 16% was observed for the cyclone occurring on October 6 th . The observed changes lay within typical variations of the daily number of Tweets. A landslide took place on August 20 th causing 36 casualties. This event did not lead to an increase of messages on the day of the event nor in the days afterwards. This is in contrast to the results from [START_REF] Neubauer | Detecting Events in Egypt Based on Geo-Referenced Tweets[END_REF], in which a snowstorm in Egypt was associated with the highest number of Tweets in the observed period. The reason for this difference might be the uniqueness of the Egyptian snowstorm, whereas cyclones are rather common events in Japan. The identified political events did not occur with an obvious increased number of Tweets.

Discussion of Communication Patterns related to Man -Made Events

We examined three regions in Ukraine in order to identify possible differences in communication patterns between these regions that were caused by different human induced factors. We identified for almost every day a political or civil event, however no natural disaster was documented. It is interesting to note that in all examined regions, the number of Tweets augmented on October 26 th , the day the election of a new parliament took place.

The Crimea shows a remarkable decrease in the daily number of Tweets during the three investigated months. The median of daily Tweets in August is 2,279, whereas the median in the month of October went down to 647, corresponding to 28 % of the Tweets of August. Apart from the peaks in the second half of August, no comparable extent of Tweets was identified afterwards. There are a number of possible reasons for this decrease. For instance, new types of social media might have been introduced by the new potentates. Another possibility would be the restriction of communication services in general or a new taxing policy making use of social media not affordable to a considerable part of the population. Finally, it is possible that a large proportion of former Twitter users just left the region.

On August 17 th the highest number of geo-referenced daily Tweets was observed, this day a push of Ukrainian forces into Lugansk and Donetsk was reported. In the eastern region of Ukraine, the highest number of daily Tweets can be seen for September 20 th , the day a large explosion took place in Donetsk. Compared to the median of all daily Tweets this corresponds to an increase of about 32% in the total number of Tweets.

In contrast to the investigations on Egypt performed by [START_REF] Neubauer | Detecting Events in Egypt Based on Geo-Referenced Tweets[END_REF], in which the number of Tweets across the whole Egypt and the cities of Cairo and Alexandria correlated highly, correlations were low for the regions of Ukraine, e.g., 0.16 in case of the West of Ukraine and Crimea. Communication patterns using Twitter seem to differ considerably between the examined regions in Ukraine.

Looking at the methodology applied to extract Tweets several aspects need to be discussed in detail. The authors of [START_REF] Morstatter | Is the Sample Good Enough? Comparing Data from Twitter's Streaming API and Twitter's Firehose[END_REF] indicate that the 1% streaming limitation is not constant over time. Hence, long-term frequency analysis that is based on the public twitter programming interface requires advanced data normalization approaches to avoid misinterpretation. Our approach to focus on the amount of Tweets in a predefined area builds on the concept, which is based on the assumption that monitoring of the dimension of the crowd at a certain hotspot is more meaningful than the content of the communication. Without pursuing a thematic priority the approach of extracting geo-references from Tweets facilitate novel insights in human behavior and opens multiple application, as mentioned in [START_REF] Ifrim | Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering[END_REF], [START_REF] Abdelhaq | EvenTweet: Online Localized Event Detection from Twitter[END_REF] [15] and [START_REF] Schaust | Avalanche: Prepare, Manage, and Understand Crisis Situations Using Social Media Analytics[END_REF]. Especially, the study of [START_REF] Schaust | Avalanche: Prepare, Manage, and Understand Crisis Situations Using Social Media Analytics[END_REF] have provided an outlook on how Social Media technology can be used as a support module for real time alerting systems for any type of event.

Conclusion

It is the purpose of our ongoing investigations to develop a method for the identification of early indicators of migration movements. The method has to optimize required man power of the stakeholders (e.g. border police forces) as well as their time investment, it has to serve to respond to developing or already ongoing migration movements in very early stages in order to mitigate the effects of such developments. The method consists of several steps:

1. Identification of typical communication patterns such as elevated number of daily Tweets associated with natural as well as man-made events having the potential to become push factors for migration based on analysis of historical data 2. Continuously observation of the quantity of social media communication for unstable regions without analyzing the content and comparison the developing patterns of communication with archival patterns being typical for migration triggering events. Identification of developing situations affecting the well-being of migration endangered populations leading to the imminent readiness for emigration by observing the individual base relevance of the region 3. In case of identifying communication patterns indicative for migration developments such as exceptional frequency of Tweets or non-typical increases or decreases of daily number of Tweets more detailed investigations such as text mining can be performed to obtain a more precise operational picture. Moreover, profound knowledge on the ongoing developments in specific critical regions is required to interpret changes in communication patterns correctly So far, we examine the relation between communication patterns and historical data and restrict our work to item 1 of the above described approach. We intend to find specific types of events such as riots or natural disasters, that are associated with typical "communication curves". In some cases several developments in communication intensity may become potential indicators of migration movements. For instance, the very large decrease in transmitted Tweets in Crimea is likely to be a sign for one or several civil and political changes occurring in a period of several months.

Fig. 1 .

 1 Fig. 1. The Geo-Polygons approximate the national borders of parts of the Ukraine and Japan. We analyzed all Tweets provided by the Twitter public streaming interface submitted within these areas -(a) West Ukraine, (b) East Ukraine and (c) Crimea

Fig. 3 .

 3 Fig. 3. Number of Tweets from August 2014 to October 2014 in West Ukraine

Table 1 .

 1 Events in Ukraine in the second reference period (August 2014 -October 2014)

	Month	No. of Events	Exemplary Event
	August 2014	32	Over 300 Ukrainian soldiers flee across border to Russia European leaders threaten Russia with further sanctions Suspicion surrounds Russian convoy carrying aid to Ukraine
	September 2014	28	Ceasefire strained as fighting near Donetsk kills nine Party of regions plans election boycott as fighting continues Drop in Russian gas supplies to Europe
	October 2014	27	Separatists are violating ceasefire Ukraine elects new parliament Russia and Ukraine remain at impasse over gas

1 http://csis.org/ukraine/index.htm#130 1.

[START_REF] Zagheni | Inferring international and internal migration patterns from Twitter data[END_REF]

Table 2 .

 2 Natural events in Japan in the period (August 2014 -October 2014) extracted from

			GLIDEnumber V2.0 [11]
	Date	Type of Event	Event
	08/08/2014 Tropical Cyclone	Typhoon Halong killed one person in Japan and injured 33,
			as authorities ordered 1.6 million people out of the path of
			the storm that battered the west of the country
	20/08/2014 Landslide	At least 36 people were killed in Japan on 20 August 2014,
			when landslides triggered by torrential rain
	27/09/2014 Volcanic Eruption Mt. Ontake, a central Japan volcano popular with tourists
			particularly in the fall, erupted without warning just before
			noon 27 September 2014
	06/10/2014 Tropical Cyclone	At least one person was dead and six were missing on 6
			October as a strong typhoon whipped through the Tokyo
			metropolitan area after making landfall further south
	12/10/2014 Tropical Cyclone	Typhoon Vongfong, the strongest storm to hit Japan this
			year, has made landfall on the country's main islands
	2	Method

Table 3 .

 3 Statistical parameters for Tweets gathered in Japan, the Crimea and the East and the West of Ukraine

	Statistical Parameter	Japan	Crimea	East Ukraine	West Ukraine
	Mean	375316.8	1314.8	1416.3	13868.8
	Median	375090.0	1105.0	1407.0	13774.0
	Maximum	498847.0	2882.0	1855.0	17383.0
	Minimum	212669.0	306.0	1094.0	10791.0
	1 st Quartile	328671.0	706.5	1309.0	13251.0
	2 nd Quartile	375090.0	1105.0	1407.0	13774.0
	3 rd Quartile	419217.0	1954.3	1498.0	14516.5

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011