This paper proposes an original solution to present statistical data using the facilities provided by the Geographical Information Systems, to improve the means of statistical data figure and distribution inside the territorial profile. The proposed solution allows to represent more statistical data sets, on the same map, using two different methods of data representation: using a color ramp to represent the territorial distribution of an aggregate indicator and a set of charts overlapped to represent the proportions of the variables that form the indicator.Geographical Information Systems, Spatial Visualization, ArcObjects, Cartograms

Dardala, Marian

Reveiu, Adriana

English

Research Papers in Economics

1 
 
GEOGRAPHICAL INFORMATION SYSTEMS TECHNOLOGIES FOR SPATIAL 
VISUALIZATION OF STATISTICAL DATA 
 
Marian DÂRDALĂ 
Academy of Economic Studies, Bucharest 
dardala@ase.ro 
Adriana REVEIU 
Academy of Economic Studies, Bucharest 
reveiua@ase.ro 
 
 
Abstract.  This  paper  proposes  an  original  solution  to  present  statistical  data  using  the 
facilities  provided  by  the  Geographical  Information  Systems,  to  improve  the  means  of 
statistical data figure and distribution inside the territorial profile. The proposed solution 
allows to represent more statistical data sets, on the same map, using two different methods of 
data  representation:  using  a  color  ramp  to  represent  the  territorial  distribution  of  an 
aggregate  indicator  and  a  set  of  charts  overlapped  to  represent  the  proportions  of  the 
variables that form the indicator. 
 
Key  words:  Geographical  Information  Systems,  Spatial  Visualization,  ArcObjects, 
Cartograms 
JEL Classification: R12 (Size and Spatial Distributions of Regional Economic Activity) 
REL Classification: 16D 
 
1. Introduction 
In recent years, Geographical Information Systems (GIS) have become an important tool for 
statistical data representation.   
The huge amount of spatial data generated by GIS expansion, the increasing number of GIS 
applications available, the computerization of a large amount of information sources, and the 
availability of digital map has increased the opportunity and need for the usage of methods for 
spatial visualization of statistical data, for both research and applied purposes. Data graphical 
representation in GIS requests to use different symbologies to view more information in the 
same time on the map. The spatial perspective of data offer the advantage to locate the studied 
phenomena and the symbology offers quantitative details that are visual reflected in various 
forms. 
 
2. Techniques for Statistical Data Visualization in Geographical Information Systems 
Cartograms  are  unique  representations  of  geographical  space.  A  cartogram  is  a  type  of 
graphic tool that describes attributes of geographic objects. Mapping requirements include the 
preservation of shape, orientation contiguity, and data that have suitable variation. Successful 
communication depends on how well the map reader recognizes the shapes of the internal 
enumeration  units,  the  accuracy  of  estimating  these  areas,  and  effective  legend  design. 
Because a cartogram does not depict geographic space, but rather changes the size of objects 
depending on a certain attribute, a cartogram is not a true map. Cartograms vary on their 
degree in which geographic space is changed: some appear very similar to a map; however 
some don’t look like a map. (Borden, 1996) 
There  are  three  main  types  of  cartograms,  each  having  a  very  different  way  of  showing 
attributes  of  geographic  objects:  non-contiguous,  contiguous  and  Dorling  cartograms. 
A  non-contiguous  cartogram  is  the  simplest  and  easiest  type  of  cartogram.  In  a  non-
contiguous cartogram, the geographic objects do not have to maintain connectivity with their 2 
 
adjacent  objects.  This  connectivity  is  called  topology.  By  freeing  the  objects  from  their 
vicinity, the objects can grow or get smaller in size and still maintain their shape.  
In a contiguous cartogram is maintained the true topology, the objects remain connected 
with each other, but this causes great distortion in shape. In this case, the cartographer must 
make the objects of the appropriate size, to represent the attribute value, but he or she must 
also maintain the shape of objects as best as possible, so that the cartogram can be easily 
interpreted.  
A Dorling cartogram maintains neither shape topology nor object centroids, though it has 
proven to be a very effective cartogram method. This type of cartogram was named after its 
inventor, Danny Dorling of the University of Leeds. To create a Dorling cartogram, instead of 
expanding or distorting the objects themselves, the cartographer will replace the objects with 
a uniform shape, usually a circle of the appropriate size.  
 
3. Informatics Technologies for Representing Symbologies in Geographical Information 
Systems 
A geographic information system works with spatial data. These data are used to represent 
different elements of geographical reality, the types of vectors (spatial characteristics) and 
their location on the earth. The main vectorial spatial data types are: point, line and polygon. 
In addition to the vectorial spatial data, geographical information systems operate with raster 
data. They are described by a grid of color (bit maps), covering a well defined area on the 
land area and are used to suggest more realism to the studied geographical space. Satellite 
images, terrain elevation model (DEM), orthophotoplan, and scanned maps are represented 
using raster formats. 
The geo-relational data model involves the data storage using independent formats based on 
the division of attributes into two entities, and store them separately. Thus, a spatial data is 
made up of a spatial attribute, which is stored separately and non-spatial attributes. The link is 
made through an identification field (ID), which aims to combine consistently the two types 
of attributes. Spatial data storage using geobase data model supposes to store both the spatial 
attributes and the non-spatial attribute, together in one table. The field containing the space 
attribute is a geometric, a vector field. 
The spatial features are implemented and processed through all classes of objects even they 
reside  in  tables,  in  a  relational  database.  Thus,  they  contain  properties  and  methods  that 
operate on them. 
 
3.1. ArcObjects .NET SDK Framework for Geographical Information Systems Development 
ArcObjects represents a framework based on COM (Component Object Model) used to build 
ArcGIS software product, developed by ESRI (Environmental Systems Research Institute). 
The three versions of ArcGIS software, ArcView, ArcEditor, and ArcInfo share the same 
applications:  ArcCatalog  and  ArcMap.  Geobase  data  model  and  ArcObjects  are  the 
foundation of the two desktop applications. 
ArcObjects  is composed  from classes and objects. ArcObjects uses three types of classes 
(Chang, 2008): 
-  Coclasses  – used to create new objects, like RgbColor class having as the goal to 
define and to handle colors, defined in RGB model. 
-  Abstract classes -cannot be used to generate new objects, but are designed to build 
hierarchy  of  classes  starting  with  an  entity  having  a  general  nature.  For  example, 
classes that implement different color models are derived from an abstract class called 
Color; 3 
 
-  Classes – are not used directly to generate new objects, but they are build from other 
objects. For example, to access selected features on the map at a time, a MapSelection 
object can be build from the map object. 
Using ArcOjects, we can develop applications in several ways: 
  GIS applications can be developed using the independent component ArcEngine and a 
software development environment like Visual Studio.NET; 
  It  is  possible  to  develop  extensions,  which  could  be  attached  to  the  ArcGIS 
applications  and  which  contains  interaction  elements  for  different  customized  GIS 
processing; 
  It is possible to develop Web applications. 
 
3.2 Symbologies for Statistical Data Representation 
One of the main applications of GIS is related to the possibility to display various statistical 
information on the maps. So that, the users could have a geographical perspective on the 
represented  phenomenon,  too.  We  must  build  cartograms  to  display  the  results  of  the 
economic and social phenomena.  
Another kind of symbology refers to the displaying of graphs on the spatial data, to capture 
the proportions of variables represented as statistical data. 
There are several techniques implemented in ArcGIS, used to classify some items according 
with the value of one indicator (attribute). To achieve this, it’s a must to identify disjoint 
intervals of values and the indicator used to form groups. For example, the simplest way to 
create ranges of values is the division of the values used to make the classification, into equal 
intervals. The NaturalBreaks method, used to split the ranges, is based on the shaping of the 
groups of values, so that to have a minimum dispersion inside the group and a maximum 
dispersion between groups. 
In ESRI’s software, colors could be defined individually. In this case there are special classes 
for various color models,  like RgbColor class used for RGB model, and as collections of 
colors, managed by color ramp classes, having the name ColorRamp. Collections of colors 
can be generated by the programmer or can be used the default collections that are defined by 
ESRI’s style gallery. 
In the following paragraph we present the exemplifications of the usage of these symbologies. 
 
4. Results 
From the relational  model perspective, we create a personal geodatabase  bd_ro.mdb, with 
judete table containing Romania’s counties as spatial data and populatie table containing the 
data about the population of the counties, as follows:  
-  population in large towns (with 150000 inhabitants or more);  
-  population in towns (having between 10000 and 150000 inhabitants);  
-  rural population (with less than 10000 inhabitants).  
The two tables contain a common field used to create the junction between them; it is the 
symbol of the county. 
The first part of the statistical data representation supposes to color the counties on the map in 
a gradient color, based on their total population. Thus, by pressing the button from the toolbar 
built for this purpose, the map is colored based on the total population and the appropriate 
legend will be displayed too. 
The source code contains several parts according to the steps that solves. In this paper we 
present only the specific steps: 
  To identify the desired thematic layer - the counties layer: 
int nl, i, nrg=3; // nl-variable used to store the number of layers  
// i –counter variable, nrg – number of groups (ranges) used to divide the counties, function on  4 
 
// the population number  
IMxDocument doc = ArcMap.Document; // get the map document 
IMap harta = doc.FocusMap; // get the map 
// declare the interface for the layer – each contains properties and methods needed in different  
// contexts  
ILayer lyr = null;  
IFeatureLayer ifl = null; 
IFeatureClass ifc = null; 
// getting the number of layers from the map  
nl = harta.LayerCount; 
// iteration of the layers to identify the desired county 
for (i = 0; i < nl; i++) 
{lyr = harta.get_Layer(i); 
    if (lyr.Name == "judete") 
    { // the usage of IFeatureLayer  interface for the desired layer 
       ifl = (IFeatureLayer)lyr; 
// using IFeatureLayer interface we can access FeatureClass property which contains properties  
// and methods of the class with spatial characteristics 
       ifc = (IFeatureClass)ifl.FeatureClass; }} 
 
  Building the intervals for counties classifying according with the population number. 
// building a histogram based on the attribute used to make the classification  
ITableHistogram ith = new BasicTableHistogramClass(); 
ith.Field = "Total_populatie"; // the field 
ith.Table = tpop; // table with the field 
object vval, vfrecv;  // vval - attribute value, vfrecv - frequencies 
 ((IBasicHistogram)ith).GetHistogram(out vval, out vfrecv); // loading objects  
// building an interface for classification, based on NaturalBreaks method  
IClassify div_grupe = new NaturalBreaksClass(); 
// assigning data used to generate the ranges  
div_grupe.SetHistogramData(vval, vfrecv); 
div_grupe.Classify(ref nrg);  //loading the number of ranges which will be generated 
double[] vecgr; //declaring an array with the ranges limits 
vecgr = (double [])div_grupe.ClassBreaks; // limits generation 
 
  Coloring the map using colors from the color ramp and  the classification  in the three 
ranges of values, previously generated. 
// defining an object for a symbology based on the ranges of values 
IClassBreaksRenderer cbr = new ClassBreaksRenderer(); 
cbr.Field = "Total_populatie";// the field which contains the values used to color the map 
cbr.BreakCount = nrg; // the number of range 
// setting the minimum value, which is the lowest limit of the first range of values 
cbr.MinimumBreak = vecgr[0];  
// declare an interface which shows the way to fill the polygons with color 
ISimpleFillSymbol fs; 
for (i = 0; i < cbr.BreakCount; i++) 
   { 
      fs = new SimpleFillSymbol(); 
      fs.Color = rcul.get_Color(i); // getting filling color 
      cbr.set_Symbol(i, (ISymbol)fs);      // associate the filling symbol 
      cbr.set_Break(i, vecgr[i+1]);      // setting the highest limit of the range  5 
 
      cbr.set_Label(i, vecgr[i].ToString()+ "-" + vecgr[i+1].ToString() ); // labeling in the legend of the    
//map from Table of Content 
   } 
IGeoFeatureLayer gfl = (IGeoFeatureLayer)lyr; 
// associate the symbology (cbr) to the lyr layer, corresponding to the counties, by Renderer property  
gfl.Renderer = (IFeatureRenderer)cbr; 
doc.ActiveView.Refresh();// redraw the content of current view  
doc.UpdateContents(); // update the content of the Table of Content window 
 
This symbology is used as background for a chart symbology that is presented in the next 
item. 
 
  The next step is to display the charts on the spatial data to figure the proportions of the 
variables inside the statistical data represented. To exemplify this, we choose to represent 
the proportion of the population from the cities, towns and rural areas in total population, 
for all counties, on the map using pie type charts, as shown in Figure 1. Data about the 
population, in counties, cities, towns and rural population are stored in the populatie table, 
from the bd_ro.mdb database. To avoid the code duplication code comparing with the 
previous example, we will present only the specific code sequences. 
 
 
 
 
  To effectively display symbology of piechart type, on the thematic layer counties from the 
map, it is necessary to do as following: 
// building a color ramp of nrg (= 3) colors, using Red to Green color ramp from ESRI galery,  
IColorRamp rcul = null; 
EnumStyleGalleryItem vst = doc.StyleGallery.get_Items("Color Ramps", "ESRI.style",  
"Default Ramps"); //doc= map document 
 IStyleGalleryItem elem = vst.Next(); 
while (elem != null) 
    { if (elem.Name == "Red to Green") 
         { rcul = (IColorRamp)elem.Item; 
Figure 1 Colouring the map using the color ramp and the PieChart symbology based on counties’ population   6 
 
           break;} 
      elem = vst.Next();} 
rcul.Size = nrg; 
bool vb; 
rcul.CreateRamp(out vb); 
ISimpleFillSymbol fs; //defining an interface to describe the filling types 
IPieChartSymbol pcs = new PieChartSymbolClass(); //building an interface for piechart symbology  
// building the interface to display the chart type symbology 
IChartRenderer cr = new ChartRenderer();  
pcs.Clockwise = true; // setting the direction for building of the pie chart   
 ((IMarkerSymbol)pcs).Size = 30; // the symbol size (for the piechart) 
// the parameters for the nrg (=3) variables which compose the piechart  
for (i = 0; i < nrg; i++) 
   { // building a symbol for chart filling  
     fs = new SimpleFillSymbol(); 
     fs.Color = rcul.get_Color(i);     // associate the filling color taken from the color ramp previous build  
     ((ISymbolArray)pcs).AddSymbol((ISymbol)fs);     // adding the symbol to the collection of symbols 
     ((IRendererFields)cr).AddField(numec[i]);     // adding the field used to create the piechart 
     // associate the field label used to display the legend  
     ((IRendererFields)cr).set_FieldAlias(i, vs[i]); } 
cr.ChartSymbol = (IChartSymbol)pcs; // associate the piechart type symbology  
cr.CreateLegend(); // building the legend – will be dispayed in the Table of Content 
cr.Label = "Distributia populatiei pe medii"; // labeling the symbology in the legend 
cr.UseOverposter = true; // avoidance the charts overlapped 
// associate the symbology (cr) to the lyr layer, corresponding to the counties  
IGeoFeatureLayer gfl = (IGeoFeatureLayer)lyr; 
gfl.Renderer = (IFeatureRenderer)cr; 
doc.ActiveView.Refresh(); // redraw the content of the current view  
doc.UpdateContents(); // updated the content of the Table of Content window 
 
5. Conclusions 
The proposed solution involves two symbologies that are overlaid and that reflect in two ways 
the same thing. The Class Breaks symbology reflect the total population indicator and plays 
role of background for a Chart symbology that reflect de proportion of the different categories 
of population in total population for each county. This type of cartogram is suitable in case of 
large enough regions because in this case the cartogram is more visible and the users easily 
can distinguish the results displayed by cartogram. 
 
Acknowledgments 
This work was supported by the project ”Performance and excellence in postdoctoral research 
in Romanian economics science domain”, contract POSDRU/89/1.5/S/59184. 
 
References 
1.  ***, “Exploring ArcObjects”, ESRI Press, 2002; 
2.  Burke, R., “Getting to Know ArcObjects: Programming ArcGIS with VBA”, ESRI 
Press, 2003; 
3.  Borden D. D., “Cartography: Thematic Map Design”, 4th edition, William C Brown 
Publishing House, 1996. 
4.  Chang, K.-T., “Programming ArcObjects with VBA”, CRC Press, 2008; 7 
 
5.  Ioniţă,  C.,  “Collaborative  Business  Process  Optimization  Using  Domain  Specific 
Languages”,  Proceedings  of  the  IXth  International  Conference  on  Informatics  in 
Economy, Bucharest, 2009.  
 
 Adriana  REVEIU  has  graduated  the  Faculty  of  Cybernetics,  Statistics  and 
Economic Informatics. She holds a PhD diploma in Economic Cybernetics and Statistics. She 
is  associate  professor  in  Economic  Informatics  field  and  branches  within  Department  of 
Economic Informatics at faculty of Cybernetics, Statistics and Economic Informatics from 
Academy of Economic Studies. She is the author and co-author of 10 books and over 50 
articles in journal and proceedings of national and international conferences, symposiums, 
workshops  in  the  fields  of  multimedia,  GIS,  communications,  learning  systems  and  data 
management. 
 
Marian  DÂRDALĂ  has  graduated  the  Faculty  of  Cybernetics,  Statistics  and 
Economic Informatics. He holds a PhD diploma in Economic Cybernetics and Statistics. He is 
professor  in  Economic  Informatics  field  and  branches  within  Department  of  Economic 
Informatics at faculty of Cybernetics, Statistics and Economic Informatics from Academy of 
Economic Studies. He is the author and co-author of 11 books and over 50 articles in journal 
and proceedings of  national and  international conferences, symposiums, workshops  in the 
fields of multimedia, human computer interaction - HCI, GIS, data bases and object oriented 
programming. 

Geographical information systems technologies for spatial visualization of statistical data

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