public static Tuple <SurveillancePlot, float> GetAggregateSurveillancePlotAndCorrelation(IEnumerable <Prediction> predictions, int plotWidth, int plotHeight, string seriesName, string plotTitle)
{
Dictionary <string, int> aggregateLocationTrueCount = new Dictionary <string, int>();
Dictionary <string, List <double> > aggregateLocationThreats = new Dictionary <string, List <double> >();
foreach (Prediction prediction in predictions)
{
IEnumerable <Incident> incidents = Incident.Get(prediction.PredictionStartTime, prediction.PredictionEndTime, prediction.PredictionArea, prediction.Model.IncidentTypes.ToArray());
Dictionary <string, int> locationTrueCount = SurveillancePlot.GetOverallLocationTrueCount(incidents, prediction);
foreach (string location in locationTrueCount.Keys)
{
aggregateLocationTrueCount.Add(prediction.Id + "-" + location, locationTrueCount[location]);
}
Dictionary <string, List <double> > locationThreats = SurveillancePlot.GetOverallLocationThreats(prediction);
foreach (string location in locationThreats.Keys)
{
aggregateLocationThreats.Add(prediction.Id + "-" + location, locationThreats[location]);
}
prediction.ReleaseAllLazyLoadedData();
}
Dictionary <string, List <PointF> > seriesPoints = new Dictionary <string, List <PointF> >();
seriesPoints.Add(seriesName, SurveillancePlot.GetSurveillancePlotPoints(aggregateLocationTrueCount, aggregateLocationThreats, true, true));
seriesPoints.Add(OptimalSeriesName, SurveillancePlot.GetOptimalSurveillancePlotPoints(aggregateLocationTrueCount, aggregateLocationThreats, true, true));
return(new Tuple <SurveillancePlot, float>(new SurveillancePlot(plotTitle, -1, seriesPoints, plotHeight, plotWidth, Plot.Format.JPEG, 2), GetThreatCorrelation(aggregateLocationThreats, aggregateLocationTrueCount)));
}
protected void Page_Load(object sender, EventArgs e)
{
Response.Cookies["loginreferrer"].Value = "/admin/incidents.aspx";
Response.Cookies["loginreferrer"].Expires = DateTime.Now.AddDays(30);
if (Request.Cookies["adminid"] != null && Request.Cookies["adminid"].Value != "")
{
intProfile = Int32.Parse(Request.Cookies["adminid"].Value);
}
else
{
Response.Redirect("/admin/login.aspx");
}
oIncident = new Incident(intProfile, dsn);
oVariable = new Variables(intEnvironment);
if (!IsPostBack)
{
LoadList();
}
if (Request.QueryString["id"] == null)
{
if (Request.QueryString["add"] == null)
{
LoopRepeater();
}
else
{
panAdd.Visible = true;
btnDelete.Enabled = false;
}
}
else
{
panAdd.Visible = true;
intID = Int32.Parse(Request.QueryString["id"]);
if (intID > 0 && !IsPostBack)
{
DataSet ds = oIncident.Get(intID);
hdnId.Value = intID.ToString();
txtError.Text = ds.Tables[0].Rows[0]["error"].ToString();
txtCompare.Text = ds.Tables[0].Rows[0]["compare"].ToString();
ddlRoute.SelectedValue = ds.Tables[0].Rows[0]["route"].ToString();
chkAutomatic.Checked = (ds.Tables[0].Rows[0]["automatic"].ToString() == "1");
txtMessage.Text = ds.Tables[0].Rows[0]["message"].ToString();
ddlPriority.SelectedValue = ds.Tables[0].Rows[0]["priority"].ToString();
radWorkstation.Checked = (ds.Tables[0].Rows[0]["workstation"].ToString() == "1");
radServer.Checked = !radWorkstation.Checked;
chkEnabled.Checked = (ds.Tables[0].Rows[0]["enabled"].ToString() == "1");
}
}
btnDelete.Attributes.Add("onclick", "return confirm('Are you sure you want to delete this item?');");
}
protected override void ExecuteWorkflowLogic()
{
var incidentCloseRequest = new CloseIncidentRequest()
{
Status = IncidentStatus.Get(Context.ExecutionContext),
IncidentResolution = new Entity("incidentresolution")
{
["subject"] = Subject.Get(Context.ExecutionContext),
["incidentid"] = Incident.Get(Context.ExecutionContext),
["description"] = Description.Get(Context.ExecutionContext),
["timespent"] = TimeSpent.Get(Context.ExecutionContext)
}
};
Context.UserService.Execute(incidentCloseRequest);
}
protected override void ExecuteWorkflowLogic(CodeActivityContext executionContext, IWorkflowContext context, IOrganizationService service)
{
var incidentCloseRequest = new CloseIncidentRequest()
{
Status = IncidentStatus.Get(executionContext),
IncidentResolution = new Entity("incidentresolution")
{
["subject"] = Subject.Get(executionContext),
["incidentid"] = Incident.Get(executionContext),
["actualend"] = CloseDate.Get(executionContext),
["description"] = Description.Get(executionContext)
}
};
service.Execute(incidentCloseRequest);
}
protected override void Run(Prediction prediction)
{
List <PostGIS.Point> predictionPoints = new List <PostGIS.Point>();
Area predictionArea = prediction.PredictionArea;
double areaMinX = predictionArea.BoundingBox.MinX;
double areaMaxX = predictionArea.BoundingBox.MaxX;
double areaMinY = predictionArea.BoundingBox.MinY;
double areaMaxY = predictionArea.BoundingBox.MaxY;
for (double x = areaMinX + prediction.PredictionPointSpacing / 2d; x <= areaMaxX; x += prediction.PredictionPointSpacing) // place points in the middle of the square boxes that cover the region - we get display errors from pixel rounding if the points are exactly on the boundaries
{
for (double y = areaMinY + prediction.PredictionPointSpacing / 2d; y <= areaMaxY; y += prediction.PredictionPointSpacing)
{
predictionPoints.Add(new PostGIS.Point(x, y, predictionArea.Shapefile.SRID));
}
}
List <PostGIS.Point> incidentPoints = new List <PostGIS.Point>(Incident.Get(TrainingStart, TrainingEnd, predictionArea, IncidentTypes.ToArray()).Select(i => i.Location));
predictionPoints.AddRange(incidentPoints);
Console.Out.WriteLine("Filtering prediction points to prediction area");
predictionPoints = predictionArea.Intersects(predictionPoints, prediction.PredictionPointSpacing / 2f).Select(i => predictionPoints[i]).ToList();
NpgsqlConnection connection = DB.Connection.OpenConnection;
try
{
Console.Out.WriteLine("Inserting points into prediction");
Point.CreateTable(prediction, predictionArea.Shapefile.SRID);
List <int> predictionPointIds = Point.Insert(connection, predictionPoints.Select(p => new Tuple <PostGIS.Point, string, DateTime>(p, PointPrediction.NullLabel, DateTime.MinValue)), prediction, predictionArea, false);
Console.Out.WriteLine("Running overall KDE for " + IncidentTypes.Count + " incident type(s)");
List <float> density = GetDensityEstimate(incidentPoints, _trainingSampleSize, false, 0, 0, predictionPoints, _normalize);
Dictionary <int, float> pointIdOverallDensity = new Dictionary <int, float>(predictionPointIds.Count);
int pointNum = 0;
foreach (int predictionPointId in predictionPointIds)
{
pointIdOverallDensity.Add(predictionPointId, density[pointNum++]);
}
Dictionary <int, Dictionary <string, double> > pointIdIncidentDensity = new Dictionary <int, Dictionary <string, double> >(pointIdOverallDensity.Count);
if (IncidentTypes.Count == 1)
{
string incident = IncidentTypes.First();
foreach (int pointId in pointIdOverallDensity.Keys)
{
Dictionary <string, double> incidentDensity = new Dictionary <string, double>();
incidentDensity.Add(incident, pointIdOverallDensity[pointId]);
pointIdIncidentDensity.Add(pointId, incidentDensity);
}
}
else
{
foreach (string incidentType in IncidentTypes)
{
Console.Out.WriteLine("Running KDE for incident \"" + incidentType + "\"");
incidentPoints = new List <PostGIS.Point>(Incident.Get(TrainingStart, TrainingEnd, predictionArea, incidentType).Select(i => i.Location));
density = GetDensityEstimate(incidentPoints, _trainingSampleSize, false, 0, 0, predictionPoints, _normalize);
if (density.Count > 0)
{
pointNum = 0;
foreach (int predictionPointId in predictionPointIds)
{
pointIdIncidentDensity.EnsureContainsKey(predictionPointId, typeof(Dictionary <string, double>));
pointIdIncidentDensity[predictionPointId].Add(incidentType, density[pointNum++]);
}
}
}
}
PointPrediction.CreateTable(prediction);
PointPrediction.Insert(GetPointPredictionValues(pointIdOverallDensity, pointIdIncidentDensity), prediction, false);
Smooth(prediction);
}
finally
{
DB.Connection.Return(connection);
}
}
public static void Evaluate(Prediction prediction, int plotWidth, int plotHeight)
{
List <Incident> newIncidents = new List <Incident>();
DateTime newIncidentsStart = prediction.PredictionStartTime;
if (prediction.MostRecentlyEvaluatedIncidentTime >= newIncidentsStart)
{
newIncidentsStart = prediction.MostRecentlyEvaluatedIncidentTime + new TimeSpan(0, 0, 0, 0, 1);
}
foreach (Incident i in Incident.Get(newIncidentsStart, prediction.PredictionEndTime, prediction.PredictionArea, prediction.Model.IncidentTypes.ToArray()))
{
newIncidents.Add(i);
}
if (newIncidents.Count == 0)
{
return;
}
List <Incident> oldIncidents = new List <Incident>();
DateTime oldIncidentsEnd = newIncidentsStart - new TimeSpan(0, 0, 0, 0, 1);
if (oldIncidentsEnd >= prediction.PredictionStartTime)
{
foreach (Incident i in Incident.Get(prediction.PredictionStartTime, oldIncidentsEnd, prediction.PredictionArea, prediction.Model.IncidentTypes.ToArray()))
{
oldIncidents.Add(i);
}
}
IEnumerable <Incident> incidents = oldIncidents.Union(newIncidents);
DiscreteChoiceModel model = prediction.Model;
long sliceTicks = -1;
if (model is TimeSliceDCM)
{
sliceTicks = (model as TimeSliceDCM).TimeSliceTicks;
}
prediction.AssessmentPlots.Clear();
prediction.SliceThreatCorrelation.Clear();
prediction.OverallCrimeThreatCorrelation = -1;
int sliceNum = 1;
Dictionary <long, Dictionary <string, int> > sliceLocationTrueCount = SurveillancePlot.GetSliceLocationTrueCount(incidents, prediction);
Dictionary <long, Dictionary <string, List <double> > > sliceLocationThreats = SurveillancePlot.GetSliceLocationThreats(prediction);
foreach (long slice in sliceLocationTrueCount.Keys.OrderBy(slice => slice))
{
if (sliceLocationThreats.ContainsKey(slice)) // no prediction points might have been generated in the case of a feature-based classifier with no features
{
string slicePlotTitle = prediction.Name;
if (sliceTicks > 0)
{
DateTime sliceStart = new DateTime(slice * sliceTicks);
DateTime sliceEnd = sliceStart + new TimeSpan(sliceTicks - 1);
slicePlotTitle += Environment.NewLine +
sliceStart.ToShortDateString() + " " + sliceStart.ToShortTimeString() + " - " +
Environment.NewLine +
sliceEnd.ToShortDateString() + " " + sliceEnd.ToShortTimeString();
}
Dictionary <string, List <PointF> > seriesPoints = new Dictionary <string, List <PointF> >();
string predictionSeriesName = sliceLocationTrueCount.Count > 1 ? "Slice " + sliceNum++ : "Prediction";
seriesPoints.Add(predictionSeriesName, SurveillancePlot.GetSurveillancePlotPoints(sliceLocationTrueCount[slice], sliceLocationThreats[slice], true, true));
seriesPoints.Add(OptimalSeriesName, SurveillancePlot.GetOptimalSurveillancePlotPoints(sliceLocationTrueCount[slice], sliceLocationThreats[slice], true, true));
prediction.AssessmentPlots.Add(new SurveillancePlot(slicePlotTitle, slice, seriesPoints, plotHeight, plotWidth, Plot.Format.JPEG, 2));
prediction.SliceThreatCorrelation.Add(slice, GetThreatCorrelation(sliceLocationThreats[slice], sliceLocationTrueCount[slice]));
}
}
if (sliceLocationTrueCount.Count > 1)
{
Dictionary <string, int> overallLocationTrueCount = SurveillancePlot.GetOverallLocationTrueCount(incidents, prediction);
Dictionary <string, List <double> > overallLocationThreats = SurveillancePlot.GetOverallLocationThreats(prediction);
Dictionary <string, List <PointF> > seriesPoints = new Dictionary <string, List <PointF> >();
seriesPoints.Add("Overall", SurveillancePlot.GetSurveillancePlotPoints(overallLocationTrueCount, overallLocationThreats, true, true));
seriesPoints.Add(OptimalSeriesName, SurveillancePlot.GetOptimalSurveillancePlotPoints(overallLocationTrueCount, overallLocationThreats, true, true));
prediction.AssessmentPlots.Add(new SurveillancePlot(prediction.Name, -1, seriesPoints, plotHeight, plotWidth, Plot.Format.JPEG, 2));
prediction.OverallCrimeThreatCorrelation = GetThreatCorrelation(overallLocationThreats, overallLocationTrueCount);
}
prediction.MostRecentlyEvaluatedIncidentTime = incidents.Max(i => i.Time);
prediction.ReleaseAllLazyLoadedData();
}
