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Geographic information tools now a days become more powerful. It initially use to get the relation between the place and its information, in another meaning to make the relation between the spactial data and its attributes such as hot spots. The researches used the data to determine the hot spots areas and to solve the problems depending on the type of the hot spot. This paper discuss road networking geographic visualization and hot spots coming from accidents or working sites consuming the location and the best time to reach any target. An examples is given by different attributes. This examples can be used in real life to guide the decision maker on real time to solve the problems and to improve the services.
Germany has approximately 650,000 km of roads, as a densely populated country in the central location in Europe and with a developed economy, Germany has a dense and modern transportation infrastructure.
In Germany built the first high system, the extensive German autobahn network famously features sections with no speed limit in it, and for many other reasons the volume of traffic in germany, especially goods transportation is at a very high level, also much of the freight traffic shifted from rail to road, a further increase of traffic is expected in the future. However, posted limits are in place on many dangerous or congested stretches as well as where traffic noise or pollution poses a problem.
Several thousands of lifes could be saved in Germany by improving the response time of the emergency services. A study conducted in the UK has estimated that 12 % of accident victims sustaining serious skeletal trauma go on to have significant preventable disabilities.
Detailed information on injury severity is needed for a better understanding of the potential for reducing damages through post-accident care.
Specifications will be drawn up for satellite-positioning accident-warning systems and carry out demonstration projects involving the whole chain of emergency service provision (CIA World Fact Book, 2009) .
Areas of concentrated event are often referred to as hot spots. Researchers use the term in many different ways. Some refer to hot spot addresses (Eck and Weisburd, 1995; Sherman, Gartin, and Buerger, 1989), others refer to hot spot blocks (Taylor, Gottfredson, and Brower, 1984; Weisburd and Green, 1994), and others examine clusters of blocks (Block and Block, 1995). Like researchers, network analysts look for concentrations of individual events that might indicate a series of related accidents.
Hot spots also as example if we have an intersection ( Street nodes), and this intersection always having an accident on it, so in the maps is defined as point (spot point) and we call it then the HOTSPOT.
There are two types of hot spots, the first one is static, and it means the hot spots is known in the data before, even if it is in intersection or curve or at start or the end of any street at the network, and the decision will be taken from the previous knowledge from this point.
The second type of the hotspot is dynamic, means that we don t know the place of the accident or of the traffic jam happened from any reason in the network, it can be at any place and this paper will be concentrated on the dynamic HOTSPOTS and how to solve it concerning on the relation between the location and the time .
The primary data behind the accident ËœHOTSPOT is a feature class Ëœpoint , it contains all the information for the place if it is dynamic and this data is updated monthly and from this data we can automatically find the hot spots and generate it and draw it by make an application from Visual basic program and this application take the proccess in one step and give us the result but in dynamic HOTSPOT we have to define it from the screen or from the tables if we have the name directly in the tables to detect the nearest hospital and police car and to detect also all the possible other streets for the other cars.
Gis-HOTSPOT accident information systems can identify the relationships between spatial and non-spatial database (Attributes). Since 1990, there have been many studies about GIS technologies and its applications on traffic safety and accident analysis. Many agencies and researchers have reported the use of GIS analysis of accidents.
Affum and Taylor (1997) described the development of a Safety Evaluation Method for Local Area Traffic Management (SELATM), which is a GIS-based program for analyzing accident patterns over time and for the evaluation of the safety benefits of Land Area Traffic Management schemes. Hirasawa and Asano (2003) at Civil Engineering Research Institute developed a traffic accident analysis system for Hokkaido, Japan. This system manages accident data, road structure and road accessory facilities using GIS technology. It allows for the analysis of accident frequencies, accident rates and seasonal effects on accidents. Liang et al. (2005) developed a traffic accident analysis system using GIS at the University of Putra in Malaysia. Using this system, the user can identify high accident locations, obtain the accident location's ranking, visualize the road accident and location information, input and retrieve the accident database, perform statistical analysis on the selected accident location and so on within a short period of time. Erdogan.S,etal.(2007)
Several studies have been conducted to establish spatial patterns in vehicle or pedestrian accidents for the identification of critical locations (Jones et al., 1996). Thomas (1996) carried out a study for hot zones using spatial autocorrelation and kernel methods on road segments. Kim and Yamashita (2004), Levine et al. (1995) analyzed spatial patterns of pedestrian crashes in Honolulu, Hawaii using K-means clustering techniques. These spatial patterns show areas of high pedestrian crashes which have been explained in light of various demographic characteristics. Bello (2005) explored a stratified accident analysis in the city of Richardson. This research focuses on identifying the spatial patterns of traffic accidents to school age kids compared to other traffic accidents in the city, using kernel densities. Sabel et al. (2005) developed a method using kernel estimation cluster analysis techniques to automatically identify road traffic accident hot spots in Christchurch in New Zealand.
The research objectives are divided into two parts. The first part is concerning on calculate the hotspot staticly.The second part is dealing with the dynamic hotspot by drawing it, find a solution concerning the time.
The research is carried out to achieve the following:
The first part will be done to deal with the accidents data and road networks by using ËœHOTSPOT analysis tool from ARCGIS to give the best accuracy for defining the hotspot areas, and to provide a software for study purpose to the University of Applied Science, Stuttgart and for the future use by police departments, hospitals, fire departments and of course people, and this will put a base for helping other countries that doesn t have it .
The aim of doing the second part is to find the best solution if an accident happen and the difficulty of this part is no one know in which place the problem will happen in the network, so the reaction must be so fast from all the police and the ambulance and all the services needed in the situation.
5.1. Static HotSpot
By using ËœHotSpot analysis tools which calculates the Getis-Ord for each feature in a dataset. This to calculate and know the Z score which is defining where features with either high or low values cluster spatially.
The tool is working by looking at each feature within the context of neighboring features.
This method will be done by using ArcGIS customization.
Fig(1) The Getis-ord local statistic formula(ArcGIS Online, USA)
5.2. Dynamic HotSpot
By using ËœNetwork Analysis for Finding the closest hospital to an accident, the closest police cars to a crime scene, and the closest store to a customer's address are all examples of closest facility problems. When finding closest facilities, you can specify how many to find and whether the direction of travel is toward or away from them. Once you've found the closest facilities, you can display the best route to or from them, return the travel cost for each route, and display directions to each facility. Additionally, you can specify an impedance cutoff beyond which ArcGIS Network Analyst should not search for a facility. For instance, you can set up a closest facility problem to search for hospitals within 15 minutes' drive time of the site of an accident. Any hospitals that take longer than 15 minutes to reach will not be included in the results. ArcGIS Online (ESRI, Inc., Redlands, California, USA) 
Fig(2) closet facility solver (ArcGIS Online, USA) 
Customization will be done in this research and the use of VBA programme for
creating custom commands with UIControls.
We can create a new button, tool, combo box, or edit box (collectively named UIControls), then attach code to the control's events. (ArcGIS Online, USA) 
Customize the interface of the ArcMap is a very usefull way to have a friendly inteface which it will be easy to be used from normal persons , and they have not to study the ArcGis to find the solution .
A main form will be done contains of the main bottons that can have the main prosses , as calculate the hotspot places or put rules on the network or find the best routes.
Fig(3) Cutomize at the ArcMap Interface
6-Data and software
At the static and dynamic HOTSPOT part the network will created using network datasets and this will be done in four main steps.
There are numerous options available when creating a network dataset. A network dataset can be built from feature classes in a feature dataset of a personal or enterprise GeoDatabase. Since a feature dataset can store multiple feature classes, the network dataset can support multiple sources and model a multimodal network. The shapefile-based network dataset provides ArcView GIS users the opportunity for rapid migration of their data. The shapefile network dataset is created from a polyline shapefile containing the network source (for instance, a street network) and, optionally, a shapefile turn feature class. Such a network dataset cannot support multiple edge sources and cannot be used to model multimodal networks. ArcGIS Online (ESRI, Inc., Redlands, California, USA) 
ArcGis will be used with different tools as (Network tools, spatial analysis), VBA will be used for customization of the ArcGis desktop, also knowing and understanding ArcObjects for creating .dll.
Using of ArcCatalog and ArcToolbox to complete the rest of the work and to handle the feature datasets and the feature class created (e.g.Network analyst tools).
Creating Forms and interface
Network Analyst tool
Assigning values & Establishing Connectivity
Build the Network
Sources for roles
Prepare the feature dataset
Creating Network Dataset
Result for HotSpots Concerning Time
Fig( 4) shows a detailed overview of the proposed investigation and the whole work flow.
After collecting the main data of the network that we want to work on it, we start to create network dataset, and this consists of four main steps. First step is to prepare the feature dataset and sources, this means all feature classes should be present in one feature dataset.
Second step is to prepare the roles inside the network dataset to make sure that the source have fields that represent the network impedance values (distance, travel time). Third step is to create the network dataset using the new network dataset wizard and this will go through naming the network dataset, identifying the network sources, setting up the connectivity and the last step is to build the network dataset. After having the network, there are two ways to work, the first way if we have a dynamic hotspot, in this case using the network analyst tool is the best solution, it have a network location tool that helps to define any point at the network by clicking on this point. We can edit or move any point at the network and solve the choosen critiera by finding the shortest way or by fastest time, at last it is to define the directions of the street in the intire network. The other way is the static hotspot and that means that we already have the data but we want to create the hotspot and this is done by hotspot analysis which calculates the Getis-Ord(Gi*) statistics for each feature in a dataset. It looks at each feature within the context of neighboring features. A feature with a high value is interesting but may not be a statistically significant hot spot, and to be so, the feature will have a high value and be surrounded by other features with high values as well. In the static hotspot a customization will be done in the ArcGis interface using VBA programming to create forms and design to generate the hotspots without doing many process and to have the result map then.
Over all the research will be concentrated on having the hotspot places and solve the problems by taking the time as a main effort to have the best result.