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14
szept

Curves analysis of routing data – Dangerous curves ahead

Szerző: Gábor NyőgériDSM-10 map, GeoXIoT, Navigation, News, Spatial AnalysisMég nem érkeztek hozzászólások

One subject, that is always on our mind is what we can do with the massive number of our location measurements, how we can get value from billions of GPS-locations recorded in the Egérút-system. This drive made us to analyze how people are driving along the curves on the roads, and if dangerous, curves could be determined on-the-fly. Our idea was to develop and integrate an alerting system to a navigation application that informs and warns the drivers about their (over)speed in the curve ahead.

First, we have built an inventory of curves by defining curve categories created from the basic geometry attributes of the curves: the radius and the length. Second, using the data recorded in the Egérút-system, we have analyzed and set up the relation between the basic road characteristics, the restrictions and the distribution of the traveling speeds. This analysis resulted in a look-up table (matrix) with recommended (or safe) speeds for each curve-type. Third, we have developed an on-the-fly curve recognition algorithm, which calculates the basic characteristics of the curves from navigation data. We have included this module in the Egérút navigation system. The real-life working demonstration, our first experience with the “Dangerous curve alerting” feature can be found in the following clip:

 

 

In the future, we will include more curve characteristics (e.g. banking) and other factors like the current weather or the visibility.

The brief description of the analysis and the development of the curve recognition module can be found in the following slideshow:

This alerting on dangerous curves feature will be included in the extended version of the Antares Maps&Navigation SDK.

29
júl
Kaposvár - Pécs - Szekszárd közös elérési zóna térképe

Drive time zones of county seats in Hungary

Szerző: Fehér KatalinArcHungary map, DSMMatrix database, Spatial AnalysisMég nem érkeztek hozzászólások

The subject of a lot of analysis tasks we’re working on is the catchment area analysis of a dedicated settlement, based on ZIP codes.

Here is a sample analysis using ArcHungary vector map of Hungary, and DSMMatrix best route and time database of Hungarian settlements.

Drive time zones analysis

The main goal of the analysis is to define catchment area of a city or a supply area of a service location or sales territory.

The basic analysis result shows a list and a map of settlements or ZIP codes belonging to 15-30-60-minute drive time zones of the enrolled settlements. This result can be completed with the number of coverage population (maybe population by age groups), or purchasing power of population.

Common drive time zones of county seats in Hungary

Common drive time zones of county seats in Hungary

Overlaps and peripheral settlements

The advantage of data visualization on a map is that we can see territorial connections that graphs are unable to show.

The map below shows the common 60 minutes drive time area of three county seats. The different colors show if we can reach one, two or both three county seats from a settlement within an our by car using the main road network.

Pecs-Szekszard_kaposvar-eleres-egyutt-20160727

There are only 3 settlements (pink area) in the region where both three county seats are reachable from within an hour and there are 112 settlements (grey areas) where none of the county seats of the region are reachable from within an hour by car. Important to note: between Kaposvár and Szekszárd there are only six more settlements (green area) within an hour drive time, while between Pécs and Szekszárd (orange area), or Pécs and Kaposvár (purple area) the number of settlements are more than a hundred.

Characteristic of zones in these counties

On this map we can see the drive time zones of the county seats from the settlements of their own counties. The main goal is to show the percentage of  settlements belongs to the different drive time zones in the county.

Eleresi-zonak-megyen-belul-2016

The graph shows that from the settlements of Somogy county takes more time to reach the county seat than from the others.

Eleresi-zonak-megyen-belul-graf-2016

In Somogy county from more than 40 % of settlements take more than an hour to reach the county seats (Baranya – Pécs – 2 %, Tolna – Szekszárd – 20%)!

These maps are sample how to analyse a catchment area. Using our Business GIS services and databases you can order specific analysis by connecting map based data to market research and statistical data to.

If you have any questions, contact us »

9
jún

Vector maps of Hungary – ZIP codes and Districts of cities

Szerző: Fehér KatalinArcHungary map, Spatial AnalysisMég nem érkeztek hozzászólások

ArcHungary vector map of Hungary contains all ZIP codes of Hungary, and city districts for more than 70 Hungarian cities.

Analysis under settlement level

For spatial analysis the settlement level administrative map is not enough. To recognize the differences of micro regions, we had to use more detailed maps. If we have address level customer database, it’s more useful if we use ZIP codes or district level maps to visualize our market data. ArcHungary Map provide these map layers.

ArcHungary ZIP codes Map of Hungary - 2015

ArcHungary ZIP codes Map of Hungary – 2015


ArcHungary ZIP codes map layer - Budapest - 2015

ArcHungary ZIP codes map layer – Budapest – 2015

All objects of these layers contains the higher level administrative units NUTS names (NUTS0-NUTS5) too.

We can use these map layers in different spatial analysis processes and as a base map.

Sample maps and different analysis are available here »

If you have any questions or need a vector map, contact us »

27
ápr
Budapest városrészei - 2015

Analysis of districts and city quarters in Hungary

Szerző: Fehér KatalinArcHungary map, Geocoding, GeoIndeX database, GeoXRaster, News, Spatial Analysis, Updates, WMS-TMSMég nem érkeztek hozzászólások

In spatial analysis the ZIP codes, districts or city quarters are important units of data collection and data analysis. In ArcHungary map these elements have own map layers.

Parts of settlements

In Hungary a lot of settlements have more than one independent populated areas, with own territory, name and ZIP code.  These areas located in the administrative boundaries of nominator settlement, but their neighbourhood profile can be different, which is very important in spatial analysis.

Győr parts of settlement – 2015
Szeged parts of settlement – 2015
Debrecen parts of settlement – 2015

Parts of main cities

Typically the historical cities have more districts, which have very different characters, like downtown, industrial district, suburb area, etc. In location based data collecting or data analysis is important to identify these parts of a city.

Districts of Budapest – 2015
Districts of Győr – 2015
Districts of Debrecen – 2015

In ArcHungary vector map of Hungary contains the districts of 70 settlements in Hungary, and all suburb areas of every settlements in Hungary (if exist). In Hungary the capital: Budapest has the most named zone (209), next is Debrecen (96), Pécs (82) and Kecskemét (63).

Spatial differences of city zones

The GeoIndeX statistical and analytical database of cities of Hungary helps to understand and analyse the spatial differences in a city. The main topics of this database are demography, net income, households and purchasing power.

Population of districts

Aggregating 100×100 grid index data we can compare the characteristic of city zones. You can combine the statistical and your own customer data in your market research process and visualize the results using GeoIndeX maps, database and services.

Population of districts – Szeged – 2015
Population of districts – Győr – 2015
Debrecen – population of districts – 2015

Purchasing power of districts

Szeged – annual net income in city quarters – 2015
Győr – annual net income in city quarters – 2015
Debrecen – annual net income in city quarters – 2015

Sample maps and analysis are available here »

If you have any questions, contact us »

9
feb

Next steps with analysing and mapping the “ECO-factors” of floating car data

Szerző: Gábor NyőgériDSM-10 map, GeoXIoT, Navigation, Spatial AnalysisMég nem érkeztek hozzászólások

We are going on with the spatial and temporal analyis and the inspection of the floating car data collected and recorded with the on-board diagnostic units installed in the vehicles of our fleet.

We have decided to normalize the data series by calculating the rate of the efficiency of the energy consumption of each driver related to his average value. Looking at the results, it is also possible to figure out the state of the traffic and congestion, the course and the engine characteristics, and the driving style of the particular drivers.

For collecting the data we have used “Torque Pro” application for smartphones (Android) connected with different OBD-units.

We will be back with some more results, conclusions, new maps an new topics!

Music by Stellardrone – Billions and Billions.

 

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