Location check for E-PRTR facilities: Hungary 1
Source data
Reporting date
2009-07-02 08:48:35
Reportnet identifier
http://cdr.eionet.europa.eu/hu/eu/eprtrdat/envsktmla
Envelope name
E-PRTR data FR 2007 - v1
XML file
E_PRTR_HU_2007_v1.xml
Derived data used for this report
HU geo checks.xls HU geo checks.kmz
2
(table with E-PRTR facility records) (zipped KML file for viewing with Google Earth)
Approach for checking locations of E-PRTR facilities
E-PRTR data deliveries contain both postal addresses and geographic coordinates of the actual site of the facilities. In order to verify the reported coordinates for a facility, its address information has been extracted from the XML data file and sent to Google and Yahoo public geocoding services. Both services are able to convert postal addresses into coordinate values. The returned results are always tagged with a precision label: • ‘address’ precision: the Google/Yahoo geocoder was able to find coordinates for the exact address • ‘street’ precision: coordinates for the street name could be found, but not for the exact address • ‘zip’ precision: the geocoder could only return central coordinates for the given zip/postal code • ‘city’, precision: the geocoder was only able to return central coordinates for the given city name • ‘not geocoded’: a match could not be found by the geocoding service The geocoding precision depends on both the accuracy and completeness of the facility addresses in the EPRTR data file and also the level of detail in the geocoder’s dataset for a given country. The distance between the reported E-PRTR facility coordinates and the coordinates from the geocoded facility address was then calculated. Distances above a certain limit value were defined to be ‘far’ according to the following simple evaluation schema: Geocoding precision Address Street Zip City
Distance is ‘far’ if > 1 km > 3 km > 5 km > 10 km
If distances between the reported E-PRTR facility coordinates and geocoded Google/Yahoo facility address were less than the distance limits above, an evaluation of ‘near’ was given. In this context, near or far does not necessarily mean correct or wrong. A ‘far’ evaluation is rather an indication that further evaluation is needed to make a judgement on the correctness of the reported information (address and coordinates) as well as of the results of the geocoding services. The distance evaluation was used in a subsequent visual inspection of selected facility locations with Google Earth. During this step, the main focus was to check those facilities where the distance between the reported facility coordinates and the coordinates from both geocoders were ‘far’. Main findings from the geocoding, the distance evaluation and the location checking (with some relevant screenshots for individual facilities) follow.
Page 1/14
3
Summary of geocoding results
A. Number of facilities for which geocoding was possible either by Google or Yahoo: Facilities included in E-PRTR reporting:
646
100%
Total number of facilities geocoded:
643
99.5%
B. Overview of the results from the individual geocoding services as well as the overall highest level of precision for which coordinates could be obtained for each facility (taking the highest level of precision from either the Google or Yahoo geocoding service). Not geocoded
Geocoded facilities
Facilities (*) Geocoding service
Total
Google Yahoo Highest level of precision from either Google or Yahoo
552 85% 640 99% 643 100%
Level of precision for geocoded facilities Address Street Zip City 128 104 299 21 20% 16% 46% 3% 0 0 199 441 0% 0% 31% 68% 128 104 320 91 20%
16%
50%
94 15% 6 1% 3
14%
0%
(*) Where relevant for your country, a full list of the facility/facilities that could not be geocoded is given in Annex 3.
4
Summary of distance evaluation
The table below provides an overview of the evaluated consistency between the reported E-PRTR coordinates and the geocoded coordinates derived from the reported E-PRTR addresses. Distance is ‘far’ for the geocoder returning the highest precision coordinates
Number of facilities % of MS facilities
Distance for both (a) geocoders is ‘far’ (Case 1) (b) 101 16%
Distance of other geocoder is ‘near’ (Case 2) (c) 66 10%
a
Distance is ‘near’ for geocoder with highest precision (d)
476 74%
( ) Includes facilities for which only one geocoder could locate the address and for which the distance is ‘far’. b ( ) If relevant for your country, a list of all Case 1 facilities is provided in Annex 1. These facilities are shown as ‘Case 1’ facilities in the country-specific KMZ file (see Section 5.1). A limited evaluation of selected Case 1 facilities is given in section 5.3. c ( ) If relevant for your country, a list of all Case 2 facilities is provided in Annex 2. The facilities are shown as ‘Case 2’ facilities in the country-specific KMZ file (see Section 5.1). d ( ) Includes facilities for which only one geocoder could locate the address and for which the distance is ‘near.
Page 2/14
Box 1. Example evaluation Google precision Address
Google distance (km) 4
Google distance (evaluation) Far
Yahoo precision city
Yahoo distance (km) 5
Yahoo distance (evaluation) near
The highest level of precision for which geocoded results could be obtained for this facility was from the Google geocoder, at an ‘address’ level of precision. The Yahoo geocoder provided coordinates at a less detailed ‘city’ level of precision. At the highest level of precision, the distance between the reported E-PRTR coordinates and the coordinates returned by Google is 4 km. The facility was subsequently evaluated as being ‘far’ (i.e. greater than the 1 km distance threshold for ‘address’) for the Google geocoder returning the highest precision coordinates, but ‘near’ for the Yahoo geocoder (i.e. less than the 10 km distance threshold for ‘city’). The distance of 4 km between the Google geocoded ‘address’ (based on the exact streetname and building number) and the E-PRTR reported coordinates indicates that there is a rather large inconsistency between the reported address and the reported coordinates. Therefore, the reported geographical information (address and coordinates) for this facility should be checked. This facility is therefore categorised as a ‘Case 2’ facility.
General recommendation • As a first priority, MS should check the reported coordinates of the ‘Case 1’ facilities for which the distance for both geocoders is ‘far’. • As a second priority, MS should also check the reported coordinates of the remaining facilities (‘Case 2’) for which the distance is ‘far’ (based on the geocoder with the highest precision) but where the distance of the other geocoder is ‘near’).
5
Location evaluation and selected spot-checks
This section includes information on: • the viewing of the information in Google Earth Viewer which allows for a visual comparison of the reported and geocoded coordinates and • spot checks of selected facilities for Case 1 and not geocoded facilities. 5.1
Screen viewing of results in Google Earth Viewer
The tabular results included in the MS-specific xls file (reported and geocoded facility locations, the distances between them and distance evaluation) were converted into a MS-specific KML file which can be loaded into Google Earth Viewer. The KML file presents all reported E-PRTR facilities and their evaluation. represents the reported EThe point locations are visualised on the map with small placemarks, where PRTR coordinates and the balloons with G and Y stand for geocoding results from Google and Yahoo. Points referring to the same facility are connected with lines. The evaluation results, i.e. ‘far’ and ‘near’ distances are shown using different icons (green for near and red for far). In the places list on the left in the Google Earth Viewer, the facilities are sorted with different icons according to the final evaluation: Case 1 2 , not geocoded and Other facilities . Case 1 facilities - example
Page 3/14
, Case
Case 2 facilities - example
Not geocoded facilities - example
Other facilities - example
5.2
Listing of common issues related to Case 1 and Case 2 facilities
In the table below, 5 categories are used to group facilities according to the highest geocoding precision and the distance between reported E-PRTR coordinates and the geocoded coordinates. For each of the groups, a list of common issues which may cause the discrepancies is given and these may help guide your checks of the facilities provided in the Annexes. Precision(a) Address
Distance(b) > 50 km
B
Address
50 km – 1.5 km
C
Address
1 km - 1.5km
Group A
Main issues Wrong geocoding (typical for very high distances) Inconsistency between the reported address and the reported coordinates (c) Inconsistency between the reported address and the reported coordinates (c) Imprecision of the geocoding Imprecision of the reported coordinates Certain distance between the address location on the street and the centre of the facility (in cases where the facility is at a certain distance from the main road, e.g. a typical case for landfills) Page 4/14
Precision(a)
Distance(b)
D
Street
> 3 km
E
Zip or City
> 5 km (zip) or > 10 km (city)
Group
Main issues Inconsistency between the reported address and the reported coordinates (c) Inconsistency between the reported address and the reported coordinates (c) Long street compared to the distance threshold of 3km Wrong geocoding (typical for very high distances) Large zip or city area compared to the distance threshold of 5km and 10km respectively Wrong geocoding (typical for very high distances)
(a)
Highest geocoding precision for the facility Distance between the reported E-PRTR coordinates and the geocoded coordinates (c) Both sets of information point to different locations such as e.g. the E-PRTR coordinates point to the physical location of the industrial facility while the address points to the main office of the parent company, or e.g. a misreporting of the coordinates. (b)
5.3 Selected spot checks for ‘Case 1’ facilities (where the distance between the reported E-PRTR coordinates and both geocoded coordinates was ‘far’) The list of all Case 1 facilities is given in Annex 1 (ranked according to precision and distance between reported and geocoded coordinates). Selected spot-checks (including screen shots) for some Case 1 facilities from your MS are presented below. National ID
FacilityName
CityName
Distance Google
100465106
Salker Salakfeldolgozo és Kereskedelmi K Apc
100284770
Szegedi Vízmű Zrt.
Szeged
3.99
124.12
address
zip
B
100284873
A.K.S.D. Kft.
Debrecen
3692.94
3689.27
street
city
D
101820434
Molnárfarm-2000 Kft.
Kengyel
4.48
288.5
Group B facility Salker Salakfeldolgozo és Kereskedelmi Kft. The reported facility coordinates match with an industrial facility and therefore appear correct. Google geocoded position (address match) is located on Vasút utca (reported streetname) in the town Jobbágyi close to Apc. The location is 4.5 km from the reported coordinates. It is not clear from the visual check whether this is a wrong geocoding or whether this is a correct match with the reported address and that there might be an inconsistency between the reported coordinates and address.
Page 5/14
Distance Yahoo 55.52
not geocoded
Google precision
Yahoo precision
Group
zip
B
address
zip
zip
E
Szegedi Vízmű Zrt. The reported coordinates correspond to an industrial facility outside Szeged. It can be assumed that is correct. The gecoded Google coordinates (address precision) are located in the centre of Szeged. It is not clear why the geocoder match was made with this location.
Group D facility A.K.S.D. Kft. The latitude and longitude values have been swapped in the reported coordinates for the facility resulting it to be located in Saudi Arabia.
Group E facility Molnárfarm-2000 Kft. The reported coordinates correspond to an industrial facility (intensive livestock farm) in Kengyel. It can be assumed that this location is correct. The Yahoo geocoded coordinates are wrong. The geocoder matches the streetname “Harkányi major” with the zip area of the town “Harka”. This results in a location of 288km from the reported coordinates. No conclusion can be drafted concerning the completeness or correctness of the reported address information. Note: similar cases for Yahoo wrong geocoding have happened for the following facilities: Baromfi-Coop Kft./ ID 100342081, Gallus Bársonyos / ID 100736224, Palotabozsoki Rt. / ID 100787123.
5.4 ‘Case 2’ facilities (where the distance between the reported E-PRTR coordinates and the geocoded result with the highest precision was ‘far’ and where the distance of the other (less precise) geocoder was ‘near’). No additional evaluation of these facilities has been performed by EEA. Member States should however also check the reported geographical information of the Case 2 facilities. A list of the Case 2 facilities is provided in Annex 2.
Page 6/14
5.5
Selected spot checks for non-geocoded facilities NationalID
Facility name
100328476
Ecomissio Kft.
100328605
Vértesi Erőmű Zrt.
101211115
Baromfi-Tím Baromfitenyésztő
Viewing the facilities in Google Earth Viewer, showed that for the three facilities, the reported coordinates correspond to an industrial site and so appear correct. Ecomissio Kft.
6
Vértesi Erőmű Zrt.
Baromfi-Tím Baromfitenyésztő
Main findings and remarks
Geocoding 646 facilities, of which 3 could not be geocoded Of all the facilities, 20% could be geocoded on the highest precision level (address match, taking the highest level of precision from either the Google or Yahoo geocoding service), 16% could be geocoded with street precision and 50% and 14% with zip and city precision respectively. This is a relatively low level of geocoding precision compared to results from other countries. For Hungary a number of reasons for the overall low geocoding precision could be the following: Address information in the reported dataset: - For 8 facilities, no streetname is reported - For 139 facilities [mainly with Annex I acitivity 7.(a)], the reported streetname is ‘külterület’ - For 238 facilities no buildingnumber is reported - It is not clear in how far the entries for the ‘StreetName’ field are actual streetnames or are a description, a postbox, the name of a small town or hamlet, etc For Hungary 354 facilities (55%) have as main Annex I activity 7.(a). The rural setting of these installations, could be an explanation for the limited (and sometimes descriptive) address information in the E-PRTR dataset.
Geocoding precision/performance: - Yahoo geocoding services encounter problems in certain cases where the streetname is a name of a place such as e.g. a smaller town (or a combination of several towns). Yahoo tends to make incomplete matches of a name. This means that part of streetname will be matched to a) a streetname or b) a location/town name (e.g. the streetname “Harkányi major” is matched by Yahoo to the zip area of the town “Harka”) for the geocoding. This results in a completely wrong geocoding which in a number of facilities is at a very large distance of the actual reported city. For the Hungarian dataset, it makes the Yahoo geocoded results very unreliable which has an Page 7/14
impact on the usefulness of the exercise for the MS. This is an exceptional case. (examples in Section 5.3, Group E facilities) - In the Google geocoding results for Hungary, a number of facilities seem to be wrongly geocoded at address level without a clear reason (examples in Section 5.3, Group B facilities). The distance to the actual location is however within a range up to 5 km. - No further analysis has been performed concerning the accuracy and the geocoding precision of Google and Yahoo Location evaluation In total 26% of the facilities have a distance ‘far’ for the geocoder which returns the highest precision. This is a rather high percentage compared to other evaluated MS. The below table, provides an overview of the number of Case 1 and Case 2 facilities in each of the groups as presented in Section 5.2. Group A B C D E Total
Case 1 0 10 0 5 83 101
Case 2 0% 10% 0% 5% 82% 100%
3 28 5 9 21 66
5% 42% 8% 14% 32% 100%
For the Case 1 facilities a number of facilities of Group B, D and E have been spot-checked in section Error! Reference source not found.. The facilities that could not be geocoded are spot-checked in Section Error! Reference source not found.. These showed the following : For all the visually checked facilities, the reported coordinates correspond to an industrial facility except for the case where the lat/lon coordinates were inverted in the reporting. Yahoo geocoding results were in a large number of instances at a very large distance of the reported coordinates due to ‘false’ geocoding. Google made some ‘false’ geocodings within the geographical area of the main municipality Based on the above findings, there is a high probability that the majority of the Case 1 and Case 2 facilities are flagged because of geocoding errors or imprecisions rather than due to a misreporting of coordinates. It seems therefore not a useful exercise to spot-check the remaining Case 1 and Case 2 facilities.
Conclusions The geo-checking approach developed does not appear to be useful exercise for Hungary due to geocoding errors. The reported coordinates of the Hungarian E-PRTR facilities appear to have a high level of accuracy. In the case that certain address information provided for the Hungarian facilities is incomplete or does not correspond to the actual address information as officially used in Hungary (eg missing streetnames), the addresses should be completed.
Page 8/14
Annex 1
List of ‘Case 1’ facilities
The table below provides a list of all the ‘Case 1’ facilities in your MS where the distance between the reported E-PRTR coordinates and the geocoded coordinates from the two geocoding services was ‘far’. National ID
FacilityName
Distance Google
Distance Yahoo
Google precision
Yahoo precision
Group
100465106
Salker Salakfeldolgozo és Kereskedelmi Kft.
4.48
55.52
address
zip
B
100465117
Adacast Kft.
4.27
55.73
address
zip
B
100284770
Szegedi Vízmű Zrt.
3.99
124.12
address
zip
B
100683559
DMRV Duna Menti Regionális Vízmű Zrt.
3.18
188.15
address
zip
B
100661591
Kálmánházi Baromfifeldolgozó Kft.
2.96
20.85
address
zip
B
100130400
Zalakerámia Rt.
2.78
365.03
address
zip
B
100426222
Nyírségi-Szárnyas Kft.
2.53
address
EMA Power Energiatermelő és szolgáltató Kft.
2.17
address
zip not geocoded
B
100372804
36.3 not geocoded
100726063
Hungerit Zrt.
1.86
40.4
address
city
B
100327295
AES Tisza Erőmű Kft.
1.52
195.61
address
zip
B
100756026
Sága Foods Zrt.
1.31
360.3
address
zip
C
100378769
Le Bélier Magyarország Zrt.
1.21
129.71
address
zip
C
101705359
Kövesi Kft.
80.16
C
Táp Kft.
address not geocoded
city
100725826
1.18 not geocoded
E
100430355
Columbian Tiszai Koromgyártó Kft.
city not geocoded
100723235
Erdei Farm Bt.
city
E
101329845
Kövesi Kft.
city
E
101329867
Kövesi Kft.
city
E
101708132
Táp Kft. I.
city
E
101708110
Táp Kft. III.
city
E
101708121
Táp Kft. II.
city
E
101708095
Táp Kft. IV.
city
E
100796192
Baromfi-Coop Kft.
city
E
100775441
Bábolna Tetra Kft.
city
E
100580926
Beneti-Pig Kft.
city
E
100969615
Bábolna Tetra Kft.
city
E
100969970
Bábolna Tetra Kft.
city
E
100904890
Bábolna Tetra Kft.
city
E
101704950
Kövesi Kft.
city
E
100316691
Baromfi-Coop Kft.
city
E
100349356
Branau 2002 Kft.
city
E
101242843
Baracsi Béla
city
E
101023541
Rohodi-Hús Kft.
city
E
100889922
Hungerit Zrt.
city
E
100879392
Kastélydomb Kft.
city
E
101120817
He-Si-Pu Bt.
city
E
247.32 not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded
Page 9/14
257.64 not geocoded 106.57 104.18 103.74 90.04 89.69 89.28 88.47 86.91 84.59 84.11 81.68 81.01 80.47 80.12 65.84 58.35 55.48 47.72 44.34 39.54 36.89
city not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded
B
E
National ID
FacilityName
Distance Google not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded
Distance Yahoo
100980883
Sága Foods Zrt.
100316004
Poncsák Béla
100342081 100760171
Baromfi-Coop Kft. Agroinvest Tsb Mezőgazdasági Termelő és Kereskedelmi Kft.
100755960
Sága Foods Zrt.
101337611
Grót-Broyler Kft.
100287379
Nagisz Zrt.
100740025
Sága Foods Zrt.
100755993
Sága Foods Zrt.
100980861
Sága Foods Zrt.
100890674
Felgyő-2000 Baromfinevelő Egyszemélyes Kft.
100752992
DDA Agrár Kft.
100368782
Csibe-Top Kft.
101603664
Nemes Nagy Zsolt
100796022 100686479
Bereg-Gabona Rt. Agroinvest Tsb Mezőgazdasági Termelő és Kereskedelmi Kft.
100306670
Nagisz Zrt.
100752969
Pal-Ko Kft.
101385427
Agro-Cikó Kft.
100724070
Böszörményi Állattenyésztő Kft.
100298283
Avifarm Mezőgazdasági Kft.
100635190
Fiorács Kft.
100950934
Bátortrade Kft.
100598611
Mezei Jenő István
101000782
Szerencs Rt 282/93
101619001
Bóly Rt.
101001044
Szerencs Rt 282/93
101346037
Bóly Zrt.
100776563
Gallus Kft.
100776600
Gallus Kft.
101629941
Uralgo Termelő-Szolgáltató és Kereskedelmi Kft.
101148523 100355427
Csatári József Szentistváni Mezőgazdasági Szolgáltató és Kereskedő Szövetkezet
100284873
A.K.S.D. Kft.
100332462
3692.94
Hage Zrt.
15.18
100335382
Orviron-Plus Környezettechnológiai Kft.
100297563
Pécsi Vízmű Zrt.
Page 10/14
Yahoo precision
Group
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
city
E
10.24
Google precision not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded
city
E
3689.27
street
city
D
238.45
street
zip
D
6.43
5.27
street
zip
D
5.98
12.72
street
city
D
36.59 35.49 32.78 31.51 30.74 28.91 25.22 24.42 24.41 22.63 22.46 22.14 22.07 19.98 19.96 19.94 18.88 18.77 17.8 17.11 16.57 16.02 14.81 13.66 13.21 12.54 11.82 11.81 11.69 11.69 11.22 10.76
National ID
FacilityName
Distance Google
Distance Yahoo
Google precision
Yahoo precision
Group
100872153
Pigomix Kft.
16.39
D
Molnárfarm-2000 Kft.
zip
E
100725099
Baromfi-Coop Kft.
zip
E
100747312
Bács-Tak Kft.
zip
E
100376880
Zoltek Zrt.
zip
E
100736224
Gallus Bársonyos
zip
E
100787123
Palotabozsoki Rt.
zip
E
100753003
47.73
zip
E
100304584
Mészáros és Társa Bt Békés Megyei Vízművek Zrt.- Békéscsaba szennyvíztisztító telep
street not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded
zip
101820434
4.17 not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded not geocoded 28.84
351.24
zip
zip
E
100736202
B-Broiler Kft.
269.67
E
Baromfi-Coop Kft.
15.13
zip not geocoded
zip
100725103
19.61 not geocoded
zip
E
100753025
DDA Agár Kft.
11.89
14.38
zip
city
E
100353711
11.72
14.32
zip
city
E
8.03
13.02
zip
city
E
100444639
Nagyatád Szövetkezet Agro-Duál Mezőgazdasági Termelő és Kereskedelmi Kft. Hajdúböszörményi Mezőgazdasági Zártkörűen Működő Részvénytársaság
6.97
27.08
zip
zip
E
100445429
R & J Termelő, Kereskedő és Szolgáltató Kft.
6.63
95.94
zip
zip
E
101163320
Extra Pig Kft.
6.04
E
Tótkomlósi Tojástermelő Kft.
5.86
zip not geocoded
zip
101698291
6.39 not geocoded
zip
E
100250052
Hage Zrt.
5.85
6.25
zip
zip
E
100296533
Mizse-Táp Kft.
6.08
5.64
zip
zip
E
100903907
Termal 2002. Kft.
49.19
5.52
zip
zip
E
100407737
Dalmand Zrt.
5.43
237.69
zip
zip
E
100750781
Debreceni "Löszhát" Kft.
5.27
E
Nagisz Zrt.
5.27
zip not geocoded
zip
100690100
5.35 not geocoded
zip
E
100694603
Bbt 2002 Kft.
5.21
142.33
zip
zip
E
100424424
Goldfood Kereskedő és Szolgáltató Kft.
5.74
5.19
zip
zip
E
100407852
Dalmand Zrt.
5.16
18.5
zip
zip
E
100404633
Pankotai Agrár Zrt.
5.1
5.23
zip
zip
E
100337571
Page 11/14
288.5 288.35 181.87 171.27 145.6 74.11
Annex 2
List of ‘Case 2’ facilities
The table below provides a list of all the ‘Case 2’ facilities in your MS where the distance is ‘far’ for the geocoder with the highest precision coordinates and the distance of the other geocoder is ‘near’ (Case 2) (c). National ID
Facility name
Distance Google
Distance Yahoo
Google precision
Yahoo precision
Highest precision
Group
101074679 100317931
Organica -WWS Zrt.
76.56
1.14
address
city
address
A
Rába Futómű Kft.
76.56
1.14
address
city
address
A
100348636
Rába Energia Kft.
76.22
1.45
address
city
address
A
100695792
Mizse-Táp Kft.
14.78
8.27
address
city
address
B
101592450
10.37
9.16
address
city
address
B
9.47
1.49
address
zip
address
B
100241821
Kóbor Tamás Szigethát Mezőgazdasági, Kereskedelmi és Szolgáltató Kft. Mezőhegyesi Sertéstenyésztő és Értékesítő Kft.
7.84
4.66
address
city
address
B
101701188
Mántelki Parasztgazdaság Zrt.
6.66
1.44
address
city
address
B
100708472
Atek Agrár Termelö Kft.
5.61
3.26
address
zip
address
B
101422364
Kalcinátor Kft.
5.37
7.86
address
city
address
B
100365312
SOLE-MiZo Zrt.
5.25
3.05
address
city
address
B
100431466
BC-Erőmű Kft.
4.91
3.87
address
zip
address
B
100825562
4.75
3.67
address
city
address
B
100308168
Abádszalóki Meta Kft. MOL Magyar Olaj- és Gázipari Nyrt.
4.05
4.46
address
city
address
B
100351773
Kenvéd Kft.
3.58
3.18
address
city
address
B
100519133
Bácsalmási Agráripari Zrt.
3.36
3.37
address
zip
address
B
100289649
Linde Gáz Magyarország Zrt.
3.32
1.27
address
zip
address
B
100688185
Nagyhegyesi Agrár Kft. MOL Magyar Olaj- és Gázipari Nyrt.
3.25
3.74
address
city
address
B
3.13
2.64
address
city
address
B
2.89
6.88
address
city
address
B
2.87
6.93
address
city
address
B
100228006
Holland Colours Pigment Kft. Bige Holding Kereskedelmi és Termelő Kft. MOL Magyar Olaj- és Gázipari Rt.
2.75
4.92
address
zip
address
B
100606972
Ipari Robbanó Kft.
2.7
1.14
address
zip
address
B
100333366
2.49
0.01
address
city
address
B
2.23
0
address
city
address
B
100726340
Holcim Hungária Zrt. Produkem Fejlesztö És Termelö Kft. Bodzási Brojler Agrár Termelő és Műszaki Fejlesztési Eredményeket Hasznosító Kft.
2.18
0.15
address
city
address
B
100500821
Dunaferr-DBK Kokszoló Kft.
2.17
1.06
address
city
address
B
100423302
ISD Dunaferr Zrt.
1.8
1.34
address
city
address
B
100329026
Borsodchem Zrt.
1.62
3.4
address
zip
address
B
100451741
1.6
3.74
address
zip
address
B
100197274
Alufix'Szefém Kft. MOL Magyar Olaj- és Gázipari Rt.
1.56
1.75
address
city
address
B
100647894
Palota Környezetvédelmi Kft.
1.45
1.52
address
city
address
C
100475493
DAK Kft.
1.37
2.1
address
city
address
C
100400808
Lighttech Lámpatechnológiai Kft.
1.29
0.81
address
zip
address
C
100763714
1.1
2.05
address
city
address
C
100814647
Dunafin Kft. Dunapack Papír- és Csomagolóanyag Zrt.
1.01
1.9
address
city
address
C
100901051
Dpmg Zrt.
11.6
1.93
street
city
street
D
100327538
Mátrai Erőmű Rt.
10.86
5.04
street
city
street
D
100329451
Mátrai Erőmű ZRt. Béke Agrárszövetkezet, Hajdúböszörmény
10.38
4.65
street
city
street
D
6.05
6.87
street
city
street
D
100385804
100368313 100377234 100333012
100596710
100416625
Page 12/14
National ID
Facility name
Distance Google
Distance Yahoo
Google precision
Yahoo precision
Highest precision
Group
100633761
Tarnamérai Hústermelő és Értékesítő Kft. /Tarnahús Kft./
5.74
1.56
street
city
street
D
101051072 100363271
Édv Zrt.
5.7
0.01
street
city
street
D
4.83
0.9
street
city
street
D
100288310
Inotai Alumíniumfeldolgozó Kft. Galisz Galvanizáló Ipari és Szolgáltató Szövetkezet
4.37
1.97
street
city
street
D
100961572
Jászberényi Kossuth Zrt.
4.07
3.42
street
city
street
D
100240385
Atev Zrt.
1.67
199.13
city
zip
zip
E
101083730
Pannon Pulyka Kft.
0.88
137.99
city
zip
zip
E
101226212
Pellmix Kft.
0.72
129.24
city
zip
zip
E
100304573
4.61
46.64
city
zip
zip
E
100696087
Pécsváradi Agrover Zrt. Hajdunánási Béke Mezőgazdasági Szövetkezet
9.86
7.15
zip
city
zip
E
100422914
Hód-Mezőgazda Zrt.
9.53
7.47
zip
city
zip
E
100880217
Vasdinnyei Brojler Kft.
9.14
9.4
zip
city
zip
E
100294078
Csabatáj Zrt.
8.96
4.83
zip
city
zip
E
100724885
Csabatáj Zrt.
8.69
4.48
zip
city
zip
E
101028432
Mátra Kincse 2002. Kft. Agro-Produkt Mezőgazdasági és Húsipari Kft.
7.96
3.28
zip
city
zip
E
7.31
4.74
zip
city
zip
E
7.21
5.12
zip
city
zip
E
100694658
Agro-M Zrt. Béke Agrárszövetkezet, Hajdúböszörmény
6.61
5.61
zip
city
zip
E
100619905
Böszörményi Állattenyésztő Kft.
6.53
6.21
zip
city
zip
E
100556253
Depónia Kft.
6.42
5.95
zip
city
zip
E
100758581
Aranybulla Mezőgazdasági Zrt.
5.71
5.15
zip
city
zip
E
100317920
Mohácsi Hungaro-Seghers Kft.
5.58
3.15
zip
city
zip
E
100332473
5.58
3.18
zip
city
zip
E
5.55
4.72
zip
city
zip
E
100947989
REMONDIS Oroszlány Zrt. Rába Járműipari Alkatrészgyártó Kft. Pálhalmai Agrospeciál Mezögazdasági Termelö, Értékesitö És Szolgáltato Kft.
5.28
3.25
zip
city
zip
E
100693064
Hüséné Nagy Enikő
5.12
4.75
zip
city
zip
E
101003990 100409948
100366803
Page 13/14
Annex 3
List of facilities that could not be geocoded
National ID
FacilityName
101211115
Baromfi-Tím Baromfitenyésztő
100328476
Ecomissio Kft.
100328605
Vértesi Erőmű Zrt.
Page 14/14
