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UDK: 711.121:330.34(438)
DOI: 10.5379/urbani-izziv-2016-27-02-001
Prejeto: 20. 11. 2015 Sprejeto: 26. 2. 2016
Piotr LITYNSKI
Povezava med nenačrtnim širjenjem mest in lokalnim gospodarstvom na Poljskem
V literaturi je nenačrtno širjenje mest zaradi stroškov, ki jih povzroča, ocenjeno kot negativen pojav, vendar so različne raziskave preučevale vpliv nenačrtnega širjenja mest samo na podlagi enega merila (na primer javnih izdatkov ali porabe goriva), namesto da bi uporabile celovitejši pristop. V raziskavah vpliva nenačrtnega širjenja mest na lokalno gospodarstvo s širšega vidika je torej vrzel. Avtor v članku preučuje povezavo med nenačrtnim širjenjem mest in lokalnim gospodarstvom, pri čemer za merjenje nenačrtnega širjenja mest uporablja prilagojeno metodo, ki temelji na indeksaciji tovrstnega širjenja. Kot mera lokalnega gospodarstva je uporabljen BDP. Ugo-
tovitve analize so pokazale, da je močnejše nenačrtno širjenje mest povezano z nižjim BDP v občinah. Celo občine s podobnim številom hiš in različno razporeditvijo v prostoru se lahko razlikujejo z vidika ravni lokalnega gospodarstva. Za višjo raven lokalnega gospodarstva bi morala biti zato gostota zgrajenih hiš večja. Sklepne ugotovitve kažejo, da izgube v lokalnem gospodarstvu, ki so posledica kaotične prostorske zgradbe, niso povezane z oddaljenostjo od mesta.
Ključne besede: nenačrtno širjenje mest, lokalno gospodarstvo, BDP, korekcija
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1 Uvod
Nenačrtno širjenje mest je splošno priznan pojav v postsocialis-tičnih evropskih mestih, ki ima sporne družbene, gospodarske in okoljske posledice (Nuissl in Rink, 2005; Pichler-Milano-vic idr., 2007; Sykora in Stanilov, 2014, ter Rosu in Blageanu, 2015). Tak razvoj večinoma sprožijo ljudje, ki se zaradi večje dostopnosti in nižjih stroškov nepremičnin ter okoljskih preferenc raje naseljujejo v predmestjih (Lisowski in Wilk, 2002; Sendi, 2013; Grum in Kobal Grum, 2015, ter Rogatka in Ramos Ribeiro, 2015). Občine v bližini mest ne morejo preprečiti tega pojava in pogosto celo podpirajo priseljevanje iz mest, saj več prebivalstva pomeni tudi več davčnih prihodkov (Chmie-lewski, 2002). Hkrati lokalne uprave pogosto izvajajo prostorsko politiko, ki ni prilagojena obsežnemu priseljevanju. Slabo lokalno prostorsko načrtovanje na Poljskem je predvsem posledica liberalizacije urejanja prostora v 90. letih 20. stoletja. Tedanja zakonodaja je poudarjala zaščito pravic do zasebne lastnine, zagotavljala več svobode gradbenim izvajalcem in razveljavljala obstoječe prostorske načrte (Martyniuk-P^czek, 2005). Do leta 2003 lokalne uprave niso smele zavrniti izdaje gradbenega dovoljenja za gradnjo samostojnih hiš, od tedaj naprej pa morajo oblikovati nove prostorske načrte (Lisows-ki idr., 2014). Pod pritiski lastnikov zemljišč so lokalne uprave nove načrte pogosto oblikovale precej nenatančno, večinoma kot splošne načrte za nepozidana območja, ki so usmerjali rast ob cestah in včasih na okoljsko občutljivih območjih in se izogibali posegom v obstoječo strukturo posestev. Tovrstno pomanjkanje vizije pri nadzoru rasti mest je preprečilo uporabo mehanizmov za nadzor rasti kot strategije za spopadanje z nenačrtnim širjenjem mest (Lisowski idr., 2014; Tsenkova, 2014, ter Mandič in Filipovič Hrast, 2015). Danes se hiše gradijo na lokacijah, ki nimajo strnjene prostorske ureditve, taka gradnja pa ima gospodarske posledice za posamezne občine in tudi za gospodarstvo celotne države.
Nenačrtno širjenje mest je težava, ki jo priznavajo vladni prostorski načrtovalski dokumenti srednje- in vzhodnoevropskih držav (Couch idr., 2007), med njimi tudi poljski. Koncept nacionalnega prostorskega razvoja do leta 2030 (pol. Koncepc-ja Przestrzennego Zagospodarowania Kraju 2030) enega od svojih šestih političnih ciljev posveča prav tej problematiki. Hkrati pa je zelo malo raziskav posledic nenačrtnega širjenja mest za gospodarstvo, še zlasti na Poljskem (Sleszynski, 2014). V prej omenjenem poljskem dokumentu določanje učinkov tovrstnega širjenja temelji na tujih, večinoma ameriških raziskavah, čeprav se nenačrtno širjenje mest v ZDA razlikuje od tistega v postsocialističnih evropskih državah. Poleg tega na Poljskem ter tudi v drugih srednje- in vzhodnoevropskih državah primanjkuje raziskav vplivov nenačrtnega širjenja mest na lokalno gospodarstvo.
Ob zavedanju potrebe po empirični raziskavi nenačrtnega širjenja mest avtor v članku ocenjuje povezavo med nenačrtnim širjenjem mest in lokalnim gospodarstvom. Njegova hipoteza je, da visoko stopnjo tovrstnega širjenja spremlja nizka raven lokalnega gospodarstva. V raziskavi se stopnja nenačrtnega širjenja mest nanaša na razdrobljene prostorske vzorce ali kaotično prostorsko zgradbo. Na podlagi te definicije obseg ali meje tovrstnega širjenja niso vključene v raziskavo. Avtor preučuje nenačrtno širjenje mest in njegove gospodarske posledice v izbranih občinah, posledic za vso državo pa ne obravnava. V korelacijski analizi so zajeta predmestja največjih poljskih mest: Krakov, Vroclav, Lodž in Poznanj. Razpoložljivi podatki ne omogočajo dinamične časovne analize, zato se raziskava nanaša samo na leto 2011 (najnovejši razpoložljivi podatki).
2 Teoretično ozadje
Literatura ne ponuja enotne definicije nenačrtnega širjenja mest. Pojav je običajno predstavljen na podlagi glavnih značilnosti, ki se lahko pripišejo določenemu urbanemu območju (Nelson in Duncan, 1995; Burchell, 1998; Ewing idr., 2002; Knapp, 2002; Wassmer, 2002; Bose, 2004; Neumann, 2005; Lisowski in Grochowski, 2009, ter Daneshopur in Shakibama-nesh, 2011). Tako je nenačrtno širjenje mest opisano kot razpršitev mestnega prebivalstva v bolj predmestne občine. Med njegovimi značilnostmi avtorji omenjajo razpršitev stavb in nizko gostoto. Povezano je tudi z redko pozidano in kaotično obliko stanovanj ter pomanjkanjem prostorske kontinuitete. Pogosto je to pomanjkanje opisano kot »učinek preskoka«, ki je značilen za stanovanjske soseske na nekdanjih kmetijskih zemljiščih, ki ustvarjajo videz krpanke.
Ta pojav zaradi makro- in mikroekonomskih stroškov, ki jih povzroča, že veliko let velja za negativnega. Ti stroški vključujejo povečane javne izdatke za gradnjo ter vzdrževanje infrastrukture in javnih storitev, negativni poslovni učinek na mestno središče, večjo porabo energije in goriv ter negativni vpliv na proračun gospodinjstev (Real Estate Research Corporation, 1974; Jackson, 1985; Downs, 1994; Bank of America, 1995; Fulton idr., 2002; Gibson in Li, 2013, in Shrestha, 2013). Hkrati so nekatere novejše raziskave, opravljene zunaj Evrope, pokazale, da je lahko obsežno nenačrtno širjenje mest z gospodarskega vidika celo koristno. V veliko primerih je lahko decentralizacija mesta koristna, saj se tako vzdržujejo stabilni in nizki prevozni stroški, zniža se prenatrpanost in poveča se učinkovitost gospodarstva. Decentralizacija je lahko koristna tudi z vidika potencialne odstranitve delovnih mest iz prenatrpanega in dragega poslovnega središča (Anas, 2012). Ugotovitve različnih avtorjev, objavljene v literaturi, so lahko nedosledne in zavajajoče (Hall, 2001). Peter Hall (2001) opozarja, da številne raziskave ocenjujejo nenačrtno širjenje mest samo na podlagi
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Povezava med nenačrtnim širjenjem mest in lokalnim gospodarstvom na Poljskem
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Preglednica 1: Analiza korelacije med regionalnim BDP in davčnimi prihodki občin (brez mest s statusom okrožja) na Poljskem
leto	malopoljsko vojvodstvo	spodnješlezijsko vojvodstvo	loško vojvodstvo	velikopoljsko vojvodstvo
	BDP* DOH, KD*	BDP* DOH, KD*	BDP* DOH, KD*	BDP* DOH, KD*
2000	56.338	402	58.552	422	45.520	322	69.726	493
2001	57.693	397	60.009	426	47.832	304	72.887	477
2002	60.782	375	63.293	410	50.446	303	74.094	479
2003	64.256	390	65.632	440	53.411	319	78.520	493
2004	69.979	510	71.231	571	57.982	412	87.540	660
2005	74.578	586	77.143	663	61.586	466	93.783	758
2006	82.229	661	86.568	752	66.287	520	100.350	861
2007	90.847	826	97.669	951	73.782	647	111.286	1.073
2008	98.621	986	104.254	1.104	79.593	759	120.217	1.252
2009	104.366	915	112.215	1.039	83.358	698	130.960	1.199
2010	109.096	928	122.539	1.023	88.202	699	135.124	1.184
2011	119.539	1.049	134.040	1.127	94.866	784	146.386	1.327
2012	123.832	1.142	138.298	1.250	98.819	855	154.153	1.448
	r = 0,9820 p = 0,0000		r = 0,9655 p = 0,0000		r = 0,9764 p = 0,0000		r = 0,9797 p = 0,0000	
Koeficient korelacije za vse štiri vojvodstva skupaj (r = 0,9768,				p = 0,000)				
Opomba: *v milijonih PLN
Vir: Lastni izračuni na podlagi lokalne podatkovne zbirke poljskega centralnega statističnega urada (Central Statistical Office of Poland, 2015)
enega merila, namesto da bi uporabile celovitejši pristop. V raziskavah vpliva nenačrtnega širjenja mest na gospodarstvo je s širšega vidika torej vrzel - namesto skozi prizmo posameznih kazalnikov bi ga bilo treba preučevati z vidika širšega sistema. Pomanjkljivosti pri ocenjevanju vpliva nenačrtnega širjenja mest na lokalno gospodarstvo so posledica kompleksnosti tovrstnega širjenja in slabega dostopa do podatkov o BDP na lokalni ravni. Zato se avtor v nadaljevanju osredotoča na merjenje teh dveh dejavnikov: nenačrtnega širjenja mest in lokalnega gospodarstva.
Meritve nenačrtnega širjenja mest običajno temeljijo na kazalnikih gostote stanovanj in stalnega prebivališča (Sierra Club, 1998; Pendal, 1999; Fulton idr., 2001; Galster idr., 2001; Gle-aser in Khan, 2001; Ewing idr., 2002, ter Knaap idr., 2005), v literaturi pa so navedeni še drugi kazalniki, ki lahko skupaj z gostoto še bolje prikažejo dani pojav. To kaže na to, da bi bilo treba ta pojav meriti na podlagi večkriterijske analize, pri kateri bi uporabili merila, ki prikazujejo različne značilnosti nenačrt-nega širjenja mest. Tak pristop sta uporabila Paul M. Torrens in Marina Alberti (2000), ki sta predlagala pristop, ki temelji na gostoti, razpršenosti, estetiki, ekologiji in dostopnosti. Uporabo multikriterijske analize sta predlagala tudi Amnon Frankel in Maya Ashkenazi (2008), ki sta tovrstno širjenje merila z vidika pokrajine, pri čemer sta uporabila podatke iz popisa rabe tal. Frankel in Ashkenazi (2008) navajata, da lahko nenačrtno širjenje mest merimo na podlagi stopenj rasti, gostote, prostorske geometrije, dostopnosti in estetskih meril.
Čeprav sta oba opisana pristopa zanimiva in privlačna, sta izjemno zahtevna z vidika potrebnih metodoloških spretnosti in razpoložljivosti podatkov. Zanimiv pristop so predstavili tudi George Galster idr. (2001) v zvezi z možnostjo merjenja nenačrtnega širjenja mest na podlagi osmih razsežnosti, povezanih z rabo tal: gostote, sklenjenosti, koncentracije, strnje-nosti, središčnosti, nuklearnosti, mešane rabe in bližine. S to metodo lahko ocenimo stopnjo nenačrtnega širjenja mest na izbranem območju, vendar pa z njo pojava ne moremo zamejiti. Galster idr. (2001) so predlagali teoretični okvir merjenja nenačrtnega širjenja mest na podlagi statističnih kazalnikov, ki so ga uporabili za oceno stopnje nenačrtnega širjenja mest v trinajstih metropolitanskih območjih v ZDA. S to metodo so prikazali splošno stopnjo nenačrtnega širjenja mest in tudi razlike med preučevanimi mesti.
Gospodarstveniki običajno gospodarstvo ocenjujejo na podlagi BDP. Na Poljskem ni na voljo podatkov o skupnem BDP na občinski ravni. Najnižja stopnja agregacije podatkov o BDP je na voljo za vojvodstva, čeprav se BDP meri tudi na nižjih ravneh (za podvojvodstva in nekaj okrožij). Podatki o BDP za voj-vodstva temeljijo na primarnih podatkih, medtem ko je BDP za podvojvodstva razdeljen na regionalne podatke. Pomanjkanje podatkov o BDP na občinski ravni zahteva nadomestno merilo. V eni od poljskih študij so Jacek Zaucha idr. (2015) kot merilo uporabili davčne prihodke posameznih občin. Ugotovili so, da so davki povezani s proizvodnjo na določenem ozemlju, zato bi bilo s tega vidika najprimernejše merilo davek
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b
d
Vjelka Vješ Mihalovice
Zjelonki	Kocmirzou
Zabjerzov
Konju sa
igolomja
Krstov
Vjfil Čk.l
Skavina MogilaniSvjontniki Gorne
Njepolomnice
Slika 1: Območje raziskave: (a) Poljska z izbranimi mesti; (b) Poznanj z okoliškimi občinami; (c) Lodž z okoliškimi občinami; (d) Vroclav z okoliškimi občinami; (e) Krakov z okoliškimi občinami (ilustracija: Piotr Litynski)
od dohodka pravnih oseb. Kompleksnost poljskega davčnega sistema pa onemogoča uporabo tovrstnega pristopa, in sicer zaradi teh razlogov: (a) davki se plačujejo po sedežu podjetja in ne po kraju proizvoda ali opravljene storitve, (b) oprostitve davka (na primer za posebne gospodarske cone) in (c) možnost prenosa davčnih izgub na naslednje davčno leto. V nasprotju s tem je primernejše merilo dohodnina, čeprav ima določene pomanjkljivosti (na primer plača se na podlagi kraja
registracije in ne kraja proizvoda ali opravljene storitve). Poleg tega Zaucha idr. (2015) predlagajo, naj se k davčnim prihodkom ob dohodnini prišteje še kmetijski davek, glede na to, da kmetijska gospodarstva ne plačujejo dohodnine, ampak samo kmetijski davek.
Uporaba prihodkov od dohodnine (DOH) in kmetijskega davka (KD) namesto BDP je upravičena tudi s statističnega
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Slika 2: Območje Vroclava kot primer kaotične prostorske zgradbe, povezane s temi razsežnostmi nenačrtnega širjenja mest: nizka stopnja središčnosti = stanovanjske hiše so daleč (20 km) od mesta; (a) nizka stopnja sklenjenosti; (b) nizka stopnja koncentracije = stanovanjske hiše, zgrajene na kmetijskih območjih (vir: Google Earth, 2011)
vidika. Koeficient korekcije med BDP na regionalni ravni in opisanimi davčnimi prihodki občin razkriva popolno povezavo. Rezultati analize korelacije med regionalnim BDP in davčnimi prihodki so prikazani v preglednici 1, ki vključuje podatke za vojvodstva, v katera spadajo mesta, analizirana v tem članku.
3 Metodologija
Avtor uporablja v članku prilagojen pristop Galster-ja idr. (2001) za oceno nenačrtnega širjenja mest, ki omogoča upoštevanje razpoložljivih brezplačnih podatkov iz lokalne podatkovne zbirke poljskega centralnega statističnega urada in spletne aplikacije Google Earth. Ocena nenačrtnega širjenja mest se nanaša na te stanovanjske kazalnike: na gostoto, sk-lenjenost, koncentracijo, strnjenost in središčnost. Raziskava je bila opravljena za občine, ki mejijo na mesta Krakov, Vroclav, Lodž in Poznanj. Gre za največja poljska mesta za prestolnico Varšavo. Prostorski obseg analize je predstavljen na sliki 1.
Z opisano metodo avtor ocenjuje nenačrtno širjenje mest na podlagi teh kazalnikov: 1. gostota, 2. sklenjenost, 3. koncentracija, 4. strnjenost in 5. središčnost. Višja razmerja pomenijo nižjo stopnjo nenačrtnega širjenja mest:
1.	Gostota se nanaša na število stanovanjskih enot (enodružinske hiše, stanovanja v večstanovanjskih stavbah itd.) na hektar zazidljivega zemljišča. Zazidljivo zemljišče (ZZ) je območje, na katerem ni naravnih ovir za stanovanjsko gradnjo. V tej raziskavi se ZZ nanaša na razliko med skupno površino občine ter površino, ki jo pokrivajo vode, gozd, rekreacijska območja, ceste in območja, namenjena ekološki rabi.
2.	Sklenjenost se nanaša na stopnjo neprekinjene pozidave ZZ. Raziskava se osredotoča na najmanjšo prostorsko enoto: vas. Za vsako vas je določena povprečna gostota stanovanj na njenem ZZ. Določena vas velja za pozidano, če ima več kot pet stanovanjskih enot na hektar. Delež vseh vasi s tako gostoto pozidave ponazarja merilo sklenjenosti.
3.	Koncentracija se nanaša na stopnjo nesorazmerne razporejenosti stanovanjskih enot na razmeroma majhnem območju (se pravi, niso razpotegnjene prek celotnega območja). Analiza je narejena na ravni občin. Koncentracija je izračunana na podlagi indeksa delta, ki ponazarja delež stanovanjskih enot, potreben za spremembo občinske merske enote, ki bi omogočala enakomerno porazdelitev po vsem preučevanem območju (Massey in Denton, 1988, ter Galster idr., 2001).
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Preglednica 2: Podatki, upoštevani pri korelacijski analizi: dohodnina, neobdelani kazalniki/razsežnosti nenačrtnega širjenja mest, z-vrednost nenačrtnega širjenja mest in sestavljeni indeks nenačrtnega širjenja mest (SIŠ), 2011.
območje	občina	prilagojeni BDP	razsežnosti nenačrtnega širjenja mest					z-vrednost razsežnosti nenačrtnega širjenja mest					SIŠ
			gost.	sklen.	konc.	strn.	sred.	gost.	sklen.	konc.	strn.	sred.	
	Igolomja	2.672.002	0,33	0,00	0,13	0,37	2,51	-1,17	-0,28	-0,71	0,12	-0,98	-3,02
	Kocmirzov	7.995.981	0,50	0,00	0,22	0,57	4,08	-0,84	-0,28	0,39	1,15	0,33	0,75
	Liški	8.449.830	0,69	0,00	0,15	0,36	4,65	-0,46	-0,28	-0,42	0,10	0,80	-0,26
	Mihalovice	6.319.467	0,54	0,00	0,18	0,43	4,36	-0,77	-0,28	-0,13	0,43	0,56	-0,18
	Mogilani	11.367.548	0,99	0,00	0,36	0,56	3,82	0,12	-0,28	2,16	1,07	0,11	3,19
	Skavina	22.826.969	1,54	0,00	0,19	0,04	3,12	1,20	-0,28	0,00	-1,51	-0,47	-1,06
Krakov	Švjontniki Gorne	6.877.184	1,50	0,00	0,06	0,09	1,61	1,13	-0,28	-1,60	-1,24	-1,72	-3,72
	Vjelka Vješ	9.220.544	0,60	0,00	0,10	0,29	4,15	-0,64	-0,28	-1,05	-0,27	0,39	-1,85
	Zabjerzov	19.501.453	0,91	0,00	0,27	0,64	4,78	-0,04	-0,28	1,06	1,50	0,91	3,16
	Zjelonki	19.740.027	1,23	0,00	0,25	0,52	6,02	0,60	-0,28	0,86	0,87	1,94	4,00
	Konjuša	3.389.684	0,33	0,00	0,11	0,34	2,92	-1,18	-0,28	-0,92	-0,03	-0,63	-3,04
	Njepolomnice	14.492.882	0,94	0,00	0,20	0,14	2,15	0,02	-0,28	0,19	-1,01	-1,27	-2,36
	Vjelička	34.060.543	1,97	0,03	0,20	0,11	3,69	2,06	3,33	0,18	-1,18	0,00	4,39
	Mjenkinja	8.676.812	0,29	0,00	0,24	0,55	5,07	-0,94	-0,33	-0,82	-0,35	0,18	-2,28
	Oborniki Šlonskje	11.927.783	0,61	0,00	0,50	0,06	2,20	1,22	-0,33	0,58	-0,70	-2,00	-1,23
	Višnja Mala	6.357.466	0,33	0,00	0,22	0,69	5,83	-0,66	-0,33	-0,91	-0,25	0,76	-1,40
	Černica	8.821.713	0,61	0,00	0,23	0,59	4,26	1,19	-0,33	-0,85	-0,33	-0,44	-0,76
Vroclav	Dlugolenka	18.765.808	0,40	0,02	0,74	4,65	4,39	-0,21	2,67	1,90	2,50	-0,34	6,52
	Konti Vroclavskje	15.312.563	0,40	0,00	0,27	0,08	4,74	-0,24	-0,33	-0,65	-0,68	-0,07	-1,97
	Kobjerzice	21.040.032	0,38	0,00	0,45	1,77	6,84	-0,37	-0,33	0,32	0,49	1,52	1,64
	Šjehnice	13.594.862	0,63	0,00	0,57	0,39	4,33	1,33	-0,33	0,99	-0,47	-0,38	1,14
	Žoravina	6.054.948	0,23	0,00	0,29	0,75	5,86	-1,33	-0,33	-0,55	-0,21	0,78	-1,66
	Andrespol	8.297.586	2,92	0,22	0,26	0,85	2,21	0,21	0,13	-0,11	1,08	-1,92	-0,61
	Brojce	2.856.592	0,27	0,00	0,23	0,49	3,75	-0,61	-0,47	-0,61	-0,30	-0,20	-2,18
	Novosolna	6.827.454	0,35	0,00	0,23	0,69	4,12	-0,58	-0,47	-0,58	0,48	0,21	-0,95
	Rzgov	7.477.305	0,51	0,00	0,32	0,48	4,15	-0,54	-0,47	0,89	-0,32	0,25	-0,19
	Konstantinov Lodžki	10.336.980	2,83	0,00	0,24	0,59	4,54	0,18	-0,47	-0,35	0,09	0,68	0,14
	Pabjanice	38.402.783	10,76	1,00	0,24	0,59	3,09	2,63	2,22	-0,35	0,09	-0,93	3,66
Lodž	Ksaverov	4.258.934	1,83	0,00	0,21	0,75	3,15	-0,13	-0,47	-0,87	0,70	-0,87	-1,63
	Pabjanice	4.521.550	0,31	0,00	0,30	0,74	4,36	-0,60	-0,47	0,64	0,65	0,48	0,71
	Zgjerz	33.075.301	7,31	1,00	0,24	0,59	5,03	1,56	2,22	-0,35	0,09	1,23	4,75
	Aleksandrov Lodžki	16.507.727	1,26	0,04	0,41	0,09	3,26	-0,30	-0,37	2,35	-1,77	-0,75	-0,83
	Strikov	5.815.305	0,31	0,00	0,20	0,05	4,07	-0,60	-0,47	-0,96	-1,93	0,16	-3,80
	Zgjerz	6.921.363	0,33	0,00	0,34	0,98	5,65	-0,59	-0,47	1,26	1,57	1,92	3,69
	Brzezini	2.188.116	0,17	0,00	0,21	0,45	3,70	-0,64	-0,47	-0,96	-0,44	-0,26	-2,76
	Lubonj	21.592.379	8,69	1,00	0,43	1,61	3,63	2,85	3,13	-0,48	-0,29	-0,89	4,33
	Puščikovo	12.906.121	4,22	0,00	0,74	3,41	1,70	1,01	-0,41	2,10	1,83	-2,21	2,32
	Červonak	20.303.830	1,73	0,08	0,56	2,94	6,37	-0,02	-0,11	0,85	1,55	0,98	3,25
	Dopjevo	19.082.988	0,60	0,00	0,43	1,34	5,12	-0,48	-0,41	0,19	-0,11	0,13	-0,67
	Kleščevo	5.315.885	0,25	0,00	0,39	1,12	4,96	-0,63	-0,41	0,02	-0,33	0,02	-1,32
Poznanj	Komorniki	17.600.794	1,07	0,00	0,38	0,89	4,81	-0,29	-0,41	-0,02	-0,57	-0,08	-1,37
	Kornik	17.989.392	0,49	0,04	0,43	2,05	5,06	-0,53	-0,26	0,19	0,64	0,09	0,12
	Mosina	18.502.121	0,75	0,00	0,52	1,33	4,84	-0,42	-0,41	0,65	-0,12	-0,06	-0,36
	Rokjetnica	9.471.848	0,47	0,00	0,33	0,84	3,88	-0,54	-0,41	-0,31	-0,63	-0,72	-2,59
	Suhi Las	19.829.090	0,65	0,14	0,56	2,94	6,29	-0,47	0,10	0,85	1,55	0,93	2,97
	Svarzendž	41.180.380	1,68	0,05	0,65	1,89	7,43	-0,04	-0,24	1,28	0,46	1,71	3,18
	Tarnovo Podgorne	30.027.843	0,71	0,06	0,41	1,95	5,05	-0,44	-0,18	0,12	0,53	0,09	0,11
Vir: lastni izračuni na podlagi lokalne podatkovne zbirke poljskega centralnega statističnega urada (Central Statistical Office of Poland, 2015)
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Povezava med nenačrtnim širjenjem mest in lokalnim gospodarstvom na Poljskem
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Slika 4: Nenačrtno širjenje Krakova (vir: Google Earth, 2011)
4.	Stmjenost se nanaša na to, kako tesno skupaj so pozidane hiše, pri čemer večja stmjenost pomeni manjšo površino ZZ posamezne občine. Meritve temeljijo na standardnih odklonih v gostoti posameznih vasi, standardiziranih s povprečno gostoto občine.
5.	Središčnost se nanaša na lokacijo stavb glede na mestno središče. Meritev temelji na izračunu obratne vrednosti povprečne vsote razdalje med mestnim in vaškim središčem, ponderirane s številom stanovanjskih enot v vasi, pri čemer je končno povprečje standardizirano s kvadratnim korenom ZZ.
Stopnjo nenačrtnega širjenja mest v vsaki občini lahko izračunamo tako, da seštejemo opisane razsežnosti. Za vsako razsežnost nenačrtnega širjenja mest je bila izračunana z-vrednost (ko-
eficient razlike vrednosti kazalnika in aritmetične sredine v razmerju s standardnim odklonom). Nižja ko je z-vrednost, višja je stopnja nenačrtnega širjenja mest. Pet z-vrednosti za vsako preučevano mesto je bilo združenih v sestavljeni indeks nenačrtnega širjenja mest (SIŠ). Z-vrednosti so bile uporabljene za izračun SIŠ, ne pa tudi za korelacijsko analizo. Raziskava se osredotoča na oceno korelacij med davčnimi prihodki od dohodnine in kmetijskega davka, ki ponazarjajo lokalni BDP, in SIŠ. Obravnavane so tudi korelacije med različnimi razsežnostmi nenačrtnega širjenja mest (neobdelani podatki in ne z-vrednost) in lokalnim BDP. Izračunan je Pearsonov koeficient korelacije in raven njegove statistične pomembnosti. Na podlagi opisanega je treba opozoriti, da se korelacijska analiza nanaša na lokalni BDP in stopnjo nenačrtnega širjenja mest. Korelacija med nenačrtnim širjenjem mest in kaotično
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Preglednica 3: Rezultati korelacijske analize: prilagojeni BDP v primerjavi s SIŠ in prilagojeni BDP v primerjavi z razsežnostmi nenačrtnega širjenja mest
	štiri območja skupaj (n = 47)	Krakov (n = 13)	Vroclav (n = 9)	Lodž (n = 13)	Poznanj (n = 12)
SIŠ	0,64	0,68	0,69	0,72	0,50
	(p = 0,00)	(p = 0,01)	(p = 0,02)	(p = 0,00)	(p = 0,04)
gostota	0,53 (p = 0,00)	0,82 (p = 0,00)	0,18 (p = 0,33)	0,94 (p = 0,00)	0,10 (p = 0,38)
sklenjenost	0,50 (p = 0,00)	0,71 (p = 0,00)	0,45 (p = 0,11)	0,94 (p = 0,00)	0,13 (p = 0,34)
koncentracija	0,38 (p = 0,00)	0,41 (p = 0,08)	0,68 (p = 0,02)	0,08 (p = 0,40)	0,34 (p = 0,14)
strnjenost	0,29 (p = 0,03)	-0,27 (p = 0,19)	0,55 (p = 0,06)	-0,06 (p = 0,42)	0,17 (p = 0,29)
središčnost	0,31	0,26	0,06	-0,01	0,58
	(p = 0,02)	(p = 0,19)	(p = 0,44)	(p = 0,49)	(p = 0,02)
Slika 5: Prostorska zgradba nenačrtnega širjenja Lodža (vir: Google Earth, 2011)
prostorsko zgradbo je izražena s petimi kazalniki, vključenimi v SIŠ. Korelacija med lokalnim BDP in obsegom nenačrtnega širjenja mest ni analizirana.
4 Rezultati in razprava
V preglednici 2 so predstavljeni podatki, na katerih temelji korelacijska analiza. V stolpcu »prilagojeni BDP« so predstavljeni podatki o lokalnem gospodarstvu oziroma združeni podatki o prihodkih v občinskih proračunih (od dohodnine in kmetijskega davka). V stolpcih s skupnim naslovom »razsežnosti nenačrtnega širjenja mest« so prikazani neobdelani rezultati kazalnikov gostota, sklenjenost, koncentracija, strnjenost in središčnost. V stolpcu »z-vrednost razsežnosti nenačrtnega širjenja mest« so ti kazalniki obdelani tako, da omogočajo izračun sestavljenega indeksa nenačrtnega širjenja
mest. V stolpcu »SIŠ« (sestavljeni indeks nenačrtnega širjenja mest) je prikazana vsota stopenj tovrstnega širjenja: višji ko je indeks, nižja je stopnja širjenja. Seštevanje stopenj v SIŠ je upravičeno, saj je nenačrtno širjenje mest pojav s številnimi vidiki, ki se nanašajo na prostorsko zgradbo, ki v sebi združuje zgoraj opisane razsežnosti, in če bi katero koli izključili iz analize, bi zmanjšali bistvo pojava. Korelacijska analiza se je osredotočala na (a) prilagojeni lokalni BDP v primerjavi s SIŠ in (b) prilagojeni lokalni BDP v primerjavi z razsežnostmi nenačrtnega širjenja mest (neobdelane vrednosti). Rezultati korelacijske analize so predstavljeni v preglednici 3.
Pri interpretaciji rezultatov je treba upoštevati, da višji SIŠ pomeni nižjo stopnjo nenačrtnega širjenja mest. Na podlagi zgornjih domnev in rezultatov korelacijske analize je iz preglednice 3 razvidno, da je med stopnjo nenačrtnega širjenja mest in ravnjo lokalnega gospodarstva pomembna povezava,
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saj njun koeficient korelacije znaša 0,64. Vrednost koeficienta se močno razlikuje od vrednosti 0 pri p < 0,00. Zato lahko z razmeroma visoko verjetnostjo sklepamo, da je nižja stopnja širjenja mest povezana z višjo ravnjo lokalnega gospodarstva. To pomeni, da se lahko občine s podobnim številom hiš in njihovo različno razporeditvijo v prostoru razlikujejo glede na raven lokalnega gospodarstva. Gosteje ko so zgrajene hiše v določeni občini, višja je raven lokalnega gospodarstva ne glede na oddaljenost od mesta.
Korelacije med prilagojenim BDP in razsežnostmi nenačrt-nega širjenja mest so: gostota = 0,53, sklenjenost = 0,50, koncentracija = 0,38, strnjenost = 0,29 in središčnost = 0,31. Statistična pomembnost koeficientov korelacije je zadovoljiva. Rezultati zato ponujajo zanimive ugotovitve. Noben koeficient korelacije ni višji od tistega med BDP in SIŠ, za kar obstaja dober razlog. Nenačrtno širjenje mest je namreč kompleksen pojav, ki ni samo posledica nizke gostote, koncentracije, decentralizacije itd., saj posamezni kazalniki kažejo nižjo korelacijo med posameznimi razsežnostmi širjenja in BDP. Do nenačrt-nega širjenja mest lahko pride tudi, če gradnja ni strnjena. Zato ga je treba obravnavati kot skupek več značilnosti.
Korelacija med BDP in kazalniki širjenja kaže, da če se pojavi, kot so visoka gostota poselitve, sklenjenost pozidave (brez preskokov), koncentracija in strnjenost stavb in bližina mestnega središča, pojavljajo posamično, je njihova jakost zmerna ali nizka in v korelaciji z BDP. Šele ko se ti kazalniki ali razsežnosti pojavijo skupaj, pride do obratnega pojava (na primer, strnje-nosti) in višje ravni lokalnega gospodarstva. Te ugotovitve so pomembne za prostorsko politiko, ki spodbuja rešitve, usmerjene proti nenačrtnemu širjenju mest, ki se ne osredotočajo samo na en kazalnik (na primer gostoto). Nenačrtno širjenje mest je kompleksen pojav, zato bi moralo tovrstno spremljanje temeljiti na več razsežnostih.
Analiza korelacij med ravnmi lokalnega gospodarstva in ne-načrtnim širjenjem mest je bila razdeljena na štiri območja: Krakov, Vroclav, Lodž in Poznanj. Statistično pomembna vrednost koeficienta korelacije p omogoča natančno interpretacijo SIŠ. Visoke vrednosti p so najverjetneje posledica majhnega vzorca n. Tako je v občinah, ki mejijo na Lodž, močna korela-cija med povečanjem nenačrtnega širjenja mest in nižjo ravnjo lokalnega gospodarstva, saj koeficient korelacije znaša 0,72 (p < 0,01). V občinah, ki mejijo na Krakov ali Vroclav, lahko opazimo podobno razmerje, saj koeficienta korelacije znašata 0,68 (p = 0,01) oziroma 0,69 (p = 0,02). Rahlo nižja korelacija je značilna za občine v okolici Poznanja. Koeficient korelacije za to območje je 0,50, na podlagi česar lahko sklepamo, da gre za povezavo med visoko stopnjo nenačrtnega širjenja mest in nizko ravnjo gospodarstva v občinah.
5 Sklep
V literaturi je nenačrtno širjenje mest zaradi stroškov, ki jih povzroča, ocenjeno negativno. Ti stroški vključujejo povečane javne izdatke za gradnjo ter vzdrževanje infrastrukture in javnih storitev, negativni poslovni učinek na mestno središče, večjo porabo energije in goriv in negativen vpliv na proračun gospodinjstev. V veliko raziskavah pa se preučuje vpliv nena-črtnega širjenja mest samo na podlagi enega merila ali pojava, namesto da bi se uporabil celovitejši pristop. V raziskavah vpliva nenačrtnega širjenja mest na gospodarstvo s širšega vidika je torej vrzel: namesto skozi prizmo posameznih kazalnikov bi ga bilo treba preučevati z vidika širšega sistema.
Ugotovitve, predstavljene v članku, kažejo, da je višja stopnja nenačrtnega širjenja mest povezana z nižjim BDP v občinah, kar potrjuje raziskovalno hipotezo. Na podlagi tega modela lahko potrdimo povezavo med nenačrtnim širjenjem mest in lokalnim gospodarstvom. Nenačrtno širjenje mest pa obstaja že dolgo in v zvezi s tem se pojavita dve vprašanji: zakaj ta problem ni bil rešen že prej in v čem se zdajšnje reševanje tega problema razlikuje od tistega v preteklosti. Na Poljskem je zdajšnja preobrazba prostorske zgradbe predmestij veliko bolj očitna kot v preteklosti, in sicer predvsem zaradi pojava novega dejavnika: v zadnjih dveh desetletjih si je vse več zasebnih gradbenih izvajalcev močno prizadevalo za razvoj bolj razpršene metropolitanske oblike, in zakonodaja je te procese podpirala. Kot navajajo Andrzej Lisowski idr. (2014), se počasi oblikuje nova prostorska ureditev, pri čemer prihaja do konfliktov med skupinama državljanov z nasprotnimi vrednotami. Na eni strani so tisti, ki si močno želijo izboljšati kakovost življenja (na podlagi gospodarske koristi ali prednosti, ki jih ponuja prebivanje na lokacijah, kjer je kakovost okolja boljša), na drugi pa tisti, ki zagovarjajo načela odgovornosti in trajnostnega razvoja. Poleg tega primanjkuje raziskav vpliva nenačrtnega širjenja mest na poljskih metropolitanskih območjih. Zaradi tega lokalne poljske uprave nerade preprečujejo tovrstno širjenje mesta. Vidijo samo njegove pozitivne vplive, kot so davčni prihodki od novih gospodinjstev. Širše posledice tega razvoja, kot so vse večji izdatki za novo infrastrukturo, ne bremenijo samo državnega gospodarstva, kar priznava tudi poljska vlada, ampak tudi lokalno gospodarstvo, tega pa lokalne oblasti ne vidijo, zato bi bilo treba s tem dejstvom seznaniti tudi širšo javnost.
Ugotovitve te raziskave so pomembne za lokalne oblasti, saj bi za doseganje višje ravni lokalnega gospodarstva morale biti hiše grajene bolj strnjeno. Strnjenost je pomembna, saj se celo občine s podobnim številom hiš, ki so različno razporejene v prostoru, lahko med seboj razlikujejo glede na raven lokalnega gospodarstva. Pomembna je torej zgradba in ne oddaljenost. V
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P. LITYNSKI
tem smislu raziskava prispeva k boljšemu razumevanju stroškov nenačrtnega širjenja mest. To je lahko pomembno za lokalne uprave in občine v bližini večjih mest, ki podpirajo priseljevanje iz mest, ker verjamejo, da se bodo zaradi rasti prebivalstva povečali davčni prihodki. Vendar je ta korist samo navidezna. Stroški nenačrtnega širjenja so večji od morebitnih koristi. Hkrati občine niso vedno zmožne preprečiti tega pojava. V tem primeru je pomembno, da se izvaja ustrezna prostorska politika, ki pravilno prepozna tovrstno širjenje in ponudi tudi ustrezne rešitve za njegovo omejitev.
Metoda prepoznavanja nenačrtnega širjenja mest, uporabljena v tem članku, temelji na pristopu, ki so ga razvili Gals-ter idr. (2001). Metoda je koristna, ker z njo ne merimo samo stopnje tega pojava, ampak tudi številne lastnosti, kot so gostota, sklenjenost, koncentracija, strnjenost in središčnost. Ob najpomembnejši ugotovitvi (to je da višjo stopnjo nenačrtnega širjenja mest spremljajo nižje ravni lokalnega gospodarstva) se je treba zavedati, da je nenačrtno širjenje mest večrazsežnostni pojav in da njegovih gospodarskih posledic ne moremo oceniti samo na podlagi ene lastnosti. Ta na videz očitna ugotovitev bi se morala izražati v prostorski politiki, saj bi tako lahko ta pojav preprečili. Gre za to, da nenačrtnega širjenja mest v dokumentih ne moremo opredeliti samo na podlagi gradnje nizke gostote in da so dejavnosti za preprečevanje tega pojava usmerjene le v povečevanje gostote stanovanj. Nenačrtno širjenje mest je pojav, ki vključuje tudi druge pojavne oblike, ki jih je treba prepoznati, meriti in nadzorovati. Raziskava je pokazala višjo korelacijo z nenačrtnim širjenjem mest kot kompleksnega pojava, ki vključuje več značilnosti ali kazalnikov, kot pa z njegovimi posameznimi razsežnostmi (na primer gostota stanovanj). Če nenačrtno širjenje mest preučujemo samo na podlagi enega kazalnika (na primer decentralizacije stavb; korelacija z BDP = 0,31), lahko sklepamo, da zaradi nizke korelacije pojav sploh nima pomembne povezave z gospodarstvom. Predstavljena raziskava je seveda pokazala, da to ne drži, saj obstaja med nenačrtnim širjenjem mest in stanjem lokalnega gospodarstva statistično pomembna povezava. Gre za večrazsežnostni pojav, ki ga je treba preučevati skozi prizmo njegovih številnih pojavnih oblik. Tovrstni večrazsežnostni pristop k preučevanju nenačrtnega širjenja mest je uporabljen v tem članku in potrjuje, da je med visoko stopnjo nenačrtnega širjenja mest in nizkim BDP statistično pomembna povezava. Z vidika prostorske politike in lokalnega gospodarstva je torej metoda, ki so jo razvili Glaser idr. (2001), uporabna za prepoznavanje nenačrtnega širjenja mest in oblikovanje prostorskih načrtov.
Piotr Litynski
Cracow University of Economics, Department of Regional Economy, Krakov, Poljska
E-pošta: litynskp@uek.krakow.pl
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UDC: 711.121:330.34(438)
DOI: 10.5379/urbani-izziv-en-2016-27-02-001
Received: 20 Nov. 2015 Accepted: 26 Feb. 2016
Piotr LITYNSKI
The correlation between urban sprawl and the local economy in Poland
The literature has assessed urban sprawl as a negative phenomenon because of the costs it generates. However, various studies have examined the impact of urban sprawl based on only one parameter, such as public expenditures or fuel consumption, instead of taking a more comprehensive approach. Thus, there is a gap in research on the impact of urban sprawl on the local economy from a broader perspective. This article examines the correlation between urban sprawl and the local economy. Urban sprawl is quantified using a modified method based on sprawl indexation. GDP is used as a measure of the local economy. The analysis discussed in the article shows that
greater urban sprawl is accompanied by lower GDP in municipalities. Even municipalities with a similar number of houses and varying distribution in space may differ in terms of the level of the local economy. Therefore, houses should be built more densely to achieve a higher level of the local economy. The conclusions show that losses for the local economy resulting from a chaotic spatial structure are unrelated to the distance from the city.
Keywords: urban sprawl, local economy, GDP, correlation
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1 Introduction
Urban sprawl is a recognised phenomenon in post-communist European cities and has controversial social, economic and environmental consequences (Nuissl & Rink, 2005; Pichler-Milanovic et al., 2007; Sykora & Stanilov, 2014; Rosu & Blageanu, 2015). This type of development is mainly caused by people that prefer to settle in suburban areas due to the availability and lower cost of real estate and due to environmental preferences (Lisowski & Wilk, 2002; Sendi, 2013; Grum & Kobal Grum, 2015; Rogatka & Ramos Ribeiro, 2015). Municipalities located near cities are unable to prevent this phenomenon; moreover, they often favour migration from a city because population growth will increase tax revenues (Chmielewski, 2002). At the same time, local governments often implement spatial policies that are not adapted to large-scale migration. The weakness of local spatial planning in Poland is primarily due to the liberalisation of spatial management in the 1990s. This legislation emphasised the protection of private property rights, giving greater freedom to building contractors and invalidating existing spatial plans (Martyniuk-P^czek, 2005). Until 2003, local governments could not deny building permits on any grounds for the construction of detached homes. Since 2003, local governments have been obliged to develop new spatial plans (Lisowski et al., 2014). Under pressure from landowners, local governments often drew up new local development plans in a rather imprecise manner, mostly as general plans for undeveloped areas, directing growth alongside roads and sometimes in environmentally sensitive areas while avoiding changes to the existing property structure. This lack of vision designed to regulate urban growth has prevented growth control mechanisms as a strategy for combating sprawl (Lisowski et al., 2014; Tsenkova, 2014; Mandic & Filipovic Hrast, 2015). Today houses are built in locations without a compact spatial layout, and this development has economic consequences not only for individual municipalities, but also for the national economy.
The problem of urban sprawl is recognised in government spatial planning documents in central and eastern Europe (Couch et al., 2007); one example is the current Polish government documents - for example, the 2030 National Spatial Development Concept (Pol. Koncepcja Przestrzennego Zagospodarow-ania Kraju 2030), which dedicates one of its six policy objectives to this issue. At the same time, research on the implications of sprawl for the economy is sparse, especially in Poland (Sleszynski, 2014). Moreover, in the Polish document the diagnosis of sprawl's effects are based on foreign studies, mostly American. However, there are differences between American and post-communist European urban sprawl. In addition, not only in Poland but also in other central and eastern Europe
countries there is a lack of studies on how urban sprawl affects the local economy.
Bearing in mind the need for empirical research on urban sprawl, this article assesses the correlation between urban sprawl and the local economy. The hypothesis is that a high degree of urban sprawl is accompanied by a low level of the local economy. In this study, the degree of sprawl refers to fragmented spatial patterns or a chaotic spatial structure. This definition excludes the extent or delimitation of sprawl from the research. The article examines urban sprawl and its economic consequences for selected municipalities - its consequences for the country as a whole are not taken into consideration. Correlation analysis is performed for the suburban areas of the largest Polish cities: Krakow, Wroclaw, Lodz and Poznan. The available data do not allow for dynamic analysis across time, and so this study applies only to 2011 (the most recent data). In the future, this could change.
2 Theoretical background
The literature offers no unified definition of urban sprawl; instead, it is presented through the main features that can be applied to a specific urban area (Nelson & Duncan, 1995; Burchell, 1998; Ewing et al., 2002; Knapp, 2002; Wassmer, 2002; Bose, 2004; Neumann, 2005; Lisowski & Grochowski, 2009; Daneshopur & Shakibamanesh, 2011). Thus, the phenomenon of urban sprawl is described as the dispersion of a city's population to more suburban municipalities. Among the features of urban sprawl, the authors mention dispersion of buildings and low density. Urban sprawl is also associated with a sparse, chaotic form of housing and lack of spatial continuity. Very often, the lack of building continuity is referred to as a "leapfrog effect", which applies to housing estates on agricultural land that create a patchwork.
For many years, this phenomenon has been considered a negative one due to the macroeconomic and microeconomic costs it generates. These include an increase in public expenditures for building and maintaining infrastructure and public services, a commercially negative impact on the city centre, an increase in energy and fuel consumption, and a negative impact on household budgets (Real Estate Research Corporation, 1974; Jackson, 1985; Downs, 1994; Bank of America, 1995; Fulton et al., 2002; Gibson & Li, 2013; Shrestha, 2013). On the other hand, some recent studies conducted outside Europe conclude that large-scale urban sprawl can be potentially beneficial from an economic point of view. In many circumstances, decentralisation of a city could be beneficial in relation to maintaining stable and low communication costs, reducing overcrowding and business efficiency. In addition, decentralisation of the city
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Table 1: Analysis of the correlation between regional GDP and tax revenues of municipalities (excluding cities with county rights), Poland.
Year	Lesser Poland		Lower Silesia		tödz		Greater Poland	
	GDP*	PIT, AT*	GDP*	PIT, AT*	GDP*	PIT, AT*	GDP*	PIT, AT*
2000	56,338	402	58,552	422	45,520	322	69,726	493
2001	57,693	397	60,009	426	47,832	304	72,887	477
2002	60,782	375	63,293	410	50,446	303	74,094	479
2003	64,256	390	65,632	440	53,411	319	78,520	493
2004	69,979	510	71,231	571	57,982	412	87,540	660
2005	74,578	586	77,143	663	61,586	466	93,783	758
2006	82,229	661	86,568	752	66,287	520	100,350	861
2007	90,847	826	97,669	951	73,782	647	111,286	1,073
2008	98,621	986	104,254	1,104	79,593	759	120,217	1,252
2009	104,366	915	112,215	1,039	83,358	698	130,960	1,199
2010	109,096	928	122,539	1,023	88,202	699	135,124	1,184
2011	119,539	1,049	134,040	1,127	94,866	784	146,386	1,327
2012	123,832	1,142	138,298	1,250	98,819	855	154,153	1,448
	r = 0.9820		r = 0.9655		r = 0.9764		r = 0.9797	
	p = 0.0000		p = 0.0000		p = 0.0000		p = 0.0000	
The correlation coefficient for the four provinces together (r				= 0.9768, p	= 0.000)			
Note: *PLN million
Source: Own calculations based on Local Data Bank of the Central Statistical Office of Poland (2015).
could be beneficial considering the possible removal of jobs from the overcrowded and expensive CBD (Anas, 2012). The results of various authors presented in the literature may be inconsistent and misleading (Hall, 2001). Peter Hall (2001) points out that various studies assess the impact of urban sprawl with regard to only one parameter instead of taking a more comprehensive approach. There is a gap in the research on the impact of urban sprawl on the economy from a broader perspective; that is, not seen through the prism of individual indicators, but as a wider system. Deficiencies in assessing the impact of urban sprawl on the local economy are due not only to the complexity of urban sprawl, but also to lack of access to GDP ratios at the local level. Bearing this in mind, further discussion concerns ways of measuring these two issues: urban sprawl and the local economy.
Measuring urban sprawl is typically based on indicators of housing densities and residence (Sierra Club, 1998; Pendal, 1999; Fulton et al., 2001; Galster et al., 2001; Gleaser & Khan, 2001; Ewing et al., 2002; Knaap et al., 2005). However, the literature on urban sprawl points to additional significant measures that, in combination with density, may better depict this phenomenon. This suggests the need for a multi-criteria analysis to measure this phenomenon using measures that can present diverse features of urban sprawl. This approach to sprawl can be found in the work of Paul M. Torrens and Marina Alberti (2000), who propose an approach based on density, scatter, aesthetics, ecology and accessibility. Multi-criteria analysis is also suggested by Amnon Frankel and Maya
Ashkenazi (2008) to measure sprawl from the perspective of the landscape, using an inventory of land use. According to Frankel and Ashkenazi (2008), sprawl can be measured by growth rates, density, spatial geometry, accessibility and aesthetic measures. Both approaches to measuring sprawl, although appealing, are characterised by high demands in terms of methodological skills and data availability. An interesting approach is presented by George Galster et al. (2001) on the possibility of measuring urban sprawl from the perspective of eight dimensions relating to land use. These are density, continuity, concentration, clustering, centrality, nuclearity, mixed uses and proximity. This method is used to assess the degree of urban sprawl in a given area, but does not serve to delimit the phenomenon. In their work, Galster et al. (2001) proposed a theoretical framework for measuring urban sprawl based on statistical indicators, demonstrating this approach in assessing the degree of urban sprawl in thirteen US metropolitan areas. This method therefore made it possible to present both the overall degree of urban sprawl and differences between the cities studied.
Economists assess the economy mostly through the prism of GDP. In Poland there is no aggregated GDP at the municipal level. The lowest level of aggregation of GDP is the province (Pol. wojewodztwo). GDP is also estimated at a level lower than the province (i.e., for sub-provinces, or a couple of counties). However, GDP for the provinces is based on primary data, whereas the sub-provinces' GDPs are divided into regional data. The lack of GDP at the municipal level
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b
d
e
Figure 1: Study area: a) Poland with selected cities; b) Poznan with surrounding municipalities; c) todz with surrounding municipalities; d) Wroclaw with surrounding municipalities; e) Krakow with surrounding municipalities (illustration: Piotr Litynski).
a
forces a substitution measure. In one Polish study, tax revenues of municipalities are used interchangeably (Zaucha et al., 2015). Jacek Zaucha et al. (2015) argue that taxes are associated with production in a territory. In this light, corporate income tax (CIT) would be the most appropriate measure. However, the complexity of the Polish tax system prevents such an approach for several reasons: a) taxes are paid at the place of the headquarters and not at the site of the product or service, b) the existence of tax exemptions (e.g., special eco-
nomic zones) and c) the ability to cover losses from one year to the next tax year. In contrast, personal income tax (PIT) shows less interference, despite the fact that it has some shortcomings (e.g., payment of the tax at the place of registration and not the site of the product or service). In addition, Zaucha et al. (2015) propose adding the sum of PIT revenue and agricultural tax to revenues due to the fact that agricultural holdings do not pay PIT, but only agricultural tax (AT).
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Figure 2: The Wroclaw area as an example of chaotic spatial structure referring to the dimensions of urban sprawl: low centrality = residential houses far from the city (20 km away); a) low continuity "leapfrog"; b) low concentration = residential houses built on agricultural areas (source: Google Earth, 2011).
Using replacement revenue from PIT and agricultural tax instead of GDP also has statistical justification. The correlation coefficient between GDP at the regional level and these tax revenues for municipalities indicates a full correlation. The results of the analysis of the correlation between regional GDP and tax revenues are shown in Table 1. The analysis in Table 1 shows the provinces for the cities analysed in this article.
3 Methodology
This article applies an approach to evaluating sprawl proposed by Galster et al. (2001) modified to accommodate the availability of free data from the Local Data Bank of the Central Statistical Office of Poland and Google Earth. The assessment of urban sprawl refers to the following housing indicators: density, continuity, concentration, clustering and centrality. The study was conducted for the municipalities neighbouring four cities: Krakow, Wroclaw, Lodz and Poznan. These cities are among the largest in the country after the capital, Warsaw. The spatial extent of the analysis is presented in Figure 1.
The method used in this paper assesses urban sprawl based on the following indicators: 1. density, 2. continuity, 3. concen-
tration, 4. clustering and 5. centrality. Higher ratios indicate less urban sprawl:
1.	Density is the number of housing units (single-family homes, apartments in multi-family buildings, etc.) per hectare of developable land. Developable land (DL) is an area that does not have natural features or barriers to housing development. In this study, DL is the difference between the total area of a municipality and the sum of the land covered by water, forest, recreation areas and roads, and land reserved for ecological uses.
2.	Continuity is the degree to which the DL has been developed in an unbroken fashion. Research is conducted on the smallest possible spatial units: in this case, villages. For each village, the average housing density in its DL is determined. A certain village is considered developed if the density is greater than five housing units per hectare. The proportion of all of the villages that are this developed is a measure of continuity.
3.	Concentration is the degree to which housing units are disproportionately located in a relatively small area rather than spread throughout the area. The analysis is conducted at the municipal level. A Delta Index was used to calculate this concentration; it is the share of housing units that
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Table 2: Correlation analysis data: income tax, raw indicators/dimensions of Urban Sprawl, Z-score of Urban Sprawl Dimensions, Sprawl Composite Index (SCI), 2011.
Area	Municipality	Adjusted GDP	Urban Sprawl Dimensions					Z-score of Urban Sprawl Dimensions					SCI
			Dens.	Cont.	Conc.	Clust.	Centr.	Dens.	Cont.	Conc.	Clust.	Centr.	
	Igotomia	2,672,002	0.33	0.00	0.13	0.37	2.51	-1.17	-0.28	-0.71	0.12	-0.98	-3.02
	Kocmyrzöw	7,995,981	0.50	0.00	0.22	0.57	4.08	-0.84	-0.28	0.39	1.15	0.33	0.75
	Liszki	8,449,830	0.69	0.00	0.15	0.36	4.65	-0.46	-0.28	-0.42	0.10	0.80	-0.26
	Michatowice	6,319,467	0.54	0.00	0.18	0.43	4.36	-0.77	-0.28	-0.13	0.43	0.56	-0.18
	Mogilany	11,367,548	0.99	0.00	0.36	0.56	3.82	0.12	-0.28	2.16	1.07	0.11	3.19
	Skawina	22,826,969	1.54	0.00	0.19	0.04	3.12	1.20	-0.28	0.00	-1.51	-0.47	-1.06
Krakow	Swiqtniki Görne	6,877,184	1.50	0.00	0.06	0.09	1.61	1.13	-0.28	-1.60	-1.24	-1.72	-3.72
	Wielka Wies	9,220,544	0.60	0.00	0.10	0.29	4.15	-0.64	-0.28	-1.05	-0.27	0.39	-1.85
	Zabierzöw	19,501,453	0.91	0.00	0.27	0.64	4.78	-0.04	-0.28	1.06	1.50	0.91	3.16
	Zielonki	19,740,027	1.23	0.00	0.25	0.52	6.02	0.60	-0.28	0.86	0.87	1.94	4.00
	Koniusza	3,389,684	0.33	0.00	0.11	0.34	2.92	-1.18	-0.28	-0.92	-0.03	-0.63	-3.04
	Niepotomnice	14,492,882	0.94	0.00	0.20	0.14	2.15	0.02	-0.28	0.19	-1.01	-1.27	-2.36
	Wieliczka	34,060,543	1.97	0.03	0.20	0.11	3.69	2.06	3.33	0.18	-1.18	0.00	4.39
	Miçkinia	8,676,812	0.29	0.00	0.24	0.55	5.07	-0.94	-0.33	-0.82	-0.35	0.18	-2.28
	Oborniki Sl^skie	11,927,783	0.61	0.00	0.50	0.06	2.20	1.22	-0.33	0.58	-0.70	-2.00	-1.23
	Wisznia Mata	6,357,466	0.33	0.00	0.22	0.69	5.83	-0.66	-0.33	-0.91	-0.25	0.76	-1.40
	Czernica	8,821,713	0.61	0.00	0.23	0.59	4.26	1.19	-0.33	-0.85	-0.33	-0.44	-0.76
Wroctaw	Dtugotçka	18,765,808	0.40	0.02	0.74	4.65	4.39	-0.21	2.67	1.90	2.50	-0.34	6.52
	Kqty Wroctawskie	15,312,563	0.40	0.00	0.27	0.08	4.74	-0.24	-0.33	-0.65	-0.68	-0.07	-1.97
	Kobierzyce	21,040,032	0.38	0.00	0.45	1.77	6.84	-0.37	-0.33	0.32	0.49	1.52	1.64
	Siechnice	13,594,862	0.63	0.00	0.57	0.39	4.33	1.33	-0.33	0.99	-0.47	-0.38	1.14
	Zörawina	6,054,948	0.23	0.00	0.29	0.75	5.86	-1.33	-0.33	-0.55	-0.21	0.78	-1.66
	Andrespol	8,297,586	2.92	0.22	0.26	0.85	2.21	0.21	0.13	-0.11	1.08	-1.92	-0.61
	Bröjce	2,856,592	0.27	0.00	0.23	0.49	3.75	-0.61	-0.47	-0.61	-0.30	-0.20	-2.18
	Nowosolna	6,827,454	0.35	0.00	0.23	0.69	4.12	-0.58	-0.47	-0.58	0.48	0.21	-0.95
	Rzgöw	7,477,305	0.51	0.00	0.32	0.48	4.15	-0.54	-0.47	0.89	-0.32	0.25	-0.19
	Konstantynow tödzki	10,336,980	2.83	0.00	0.24	0.59	4.54	0.18	-0.47	-0.35	0.09	0.68	0.14
tödz	Pabianice	38,402,783	10.76	1.00	0.24	0.59	3.09	2.63	2.22	-0.35	0.09	-0.93	3.66
	Ksaweröw	4,258,934	1.83	0.00	0.21	0.75	3.15	-0.13	-0.47	-0.87	0.70	-0.87	-1.63
	Pabianice	4,521,550	0.31	0.00	0.30	0.74	4.36	-0.60	-0.47	0.64	0.65	0.48	0.71
	Zgierz	33,075,301	7.31	1.00	0.24	0.59	5.03	1.56	2.22	-0.35	0.09	1.23	4.75
	Aleksandröw tödzki	16,507,727	1.26	0.04	0.41	0.09	3.26	-0.30	-0.37	2.35	-1.77	-0.75	-0.83
	Strykow	5,815,305	0.31	0.00	0.20	0.05	4.07	-0.60	-0.47	-0.96	-1.93	0.16	-3.80
	Zgierz	6,921,363	0.33	0.00	0.34	0.98	5.65	-0.59	-0.47	1.26	1.57	1.92	3.69
	Brzeziny	2,188,116	0.17	0.00	0.21	0.45	3.70	-0.64	-0.47	-0.96	-0.44	-0.26	-2.76
	Lubon	21,592,379	8.69	1.00	0.43	1.61	3.63	2.85	3.13	-0.48	-0.29	-0.89	4.33
	Puszczykowo	12,906,121	4.22	0.00	0.74	3.41	1.70	1.01	-0.41	2.10	1.83	-2.21	2.32
	Czerwonak	20,303,830	1.73	0.08	0.56	2.94	6.37	-0.02	-0.11	0.85	1.55	0.98	3.25
	Dopiewo	19,082,988	0.60	0.00	0.43	1.34	5.12	-0.48	-0.41	0.19	-0.11	0.13	-0.67
	Kleszczewo	5,315,885	0.25	0.00	0.39	1.12	4.96	-0.63	-0.41	0.02	-0.33	0.02	-1.32
Poznan	Komorniki	17,600,794	1.07	0.00	0.38	0.89	4.81	-0.29	-0.41	-0.02	-0.57	-0.08	-1.37
	Kornik	17,989,392	0.49	0.04	0.43	2.05	5.06	-0.53	-0.26	0.19	0.64	0.09	0.12
	Mosina	18,502,121	0.75	0.00	0.52	1.33	4.84	-0.42	-0.41	0.65	-0.12	-0.06	-0.36
	Rokietnica	9,471,848	0.47	0.00	0.33	0.84	3.88	-0.54	-0.41	-0.31	-0.63	-0.72	-2.59
	Suchy Las	19,829,090	0.65	0.14	0.56	2.94	6.29	-0.47	0.10	0.85	1.55	0.93	2.97
	Swarzçdz	41,180,380	1.68	0.05	0.65	1.89	7.43	-0.04	-0.24	1.28	0.46	1.71	3.18
	Tarnowo Podgörne	30,027,843	0.71	0.06	0.41	1.95	5.05	-0.44	-0.18	0.12	0.53	0.09	0.11
Source: Own calculations based on Local Data Bank of the Central Statistical Office of Poland (2015).
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L r	, T1^ if rr'iV	n .
liiliatf^.:
Figure 3: Urban sprawl in Poznan: a) spatial structure; b) street view (source: Google Earth, 2011).
Figure 4: Urban sprawl in Krakow (source: Google Earth, 2011).
would be needed to shift a municipality's unit of scale to achieve a uniform distribution across the entire study area (Massey & Denton, 1988; Galster et al., 2001).
4.	Clustering is the degree to which development is tightly bunched to minimise the amount of land in each municipality's DL. The measurement is based on the standard deviations of density among villages, standardised by the average density of a municipality.
5.	Centrality is the degree to which buildings are located in relation to the city centre. The measurement is based on a calculation of the inverse of the average sum of distance from the city centre to the village centre weighted by the number of housing units in the village, with the resulting average standardised by the square root of the DL.
The level of urban sprawl for each municipality can be calculated by summing up these dimensions included in the rates. To be able to add these indicators for each dimension of sprawl, a z-score was developed (the ratio of the difference between the indicator value and the average to the standard deviation). The lower the value of the z-score, the higher the degree of sprawl. Consequently the five z-scores for each city were summed to provide the Sprawl Composite Index (SCI). Z-scores are used only to obtain the SCI, but not used for correlation analysis. The research in the article relates to the estimate of correlations between tax revenues from PIT and agricultural tax, which represent the local GDP, and the SCI. The article also takes into consideration correlations between the various dimensions of urban sprawl (raw data ofdimension,
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Table 3: Results of the correlation analysis: Adjusted GDP vs. SCI, adjusted GDP vs. Urban Sprawl Dimensions
	The four areas together (n = 47)	Krakow (n = 13)	Wroclaw (n = 9)	todz (n = 13)	Poznan (n = 12)
SCI	0.64 (p = 0.00)	0.68 (p = 0.01)	0.69 (p = 0.02)	0.72 (p = 0.00)	0.50 (p = 0.04)
Density	0.53 (p = 0.00)	0.82 (p = 0.00)	0.18 (p = 0.33)	0.94 (p = 0.00)	0.10 (p = 0.38)
Continuity	0.50 (p = 0.00)	0.71 (p = 0.00)	0.45 (p = 0.11)	0.94 (p = 0.00)	0.13 (p = 0.34)
Concentration	0.38 (p = 0.00)	0.41 (p = 0.08)	0.68 (p = 0.02)	0.08 (p = 0.40)	0.34 (p = 0.14)
Clustering	0.29 (p = 0.03)	-0.27 (p = 0.19)	0.55 (p = 0.06)	-0.06 (p = 0.42)	0.17 (p = 0.29)
Centrality	0.31 (p = 0.02)	0.26 (p = 0.19)	0.06 (p = 0.44)	-0.01 (p = 0.49)	0.58 (p = 0.02)
Figure 5: Spatial structure of urban sprawl in todz (source: Google Earth, 2011).
not z-score) and the local GDP. It calculates the Pearson correlation coefficient and its significance level. Given the above, it should be emphasised that the correlation analysis applies to the local GDP and the degree of urban sprawl. The correlation between the degree of urban sprawl and chaotic spatial structure is expressed by five indicators that were included in the SCI. The correlation between local GDP and the extent of urban sprawl was not analysed.
4 Results and discussion
Table 2 presents the data on which the correlation analysis is based. The column "Adjusted GDP" represents the local economy and is the aggregation of revenue to the budgets of municipalities with respect to PIT and agricultural tax. The columns under "Urban Sprawl Dimensions" include raw results of the evaluation indicators: density, continuity, con-
centration, clustering and centrality. The column "Z-score of Urban Sprawl Dimensions" collects these indicators processed in such a way that the Sprawl Composite Index can be constructed. The column "Sprawl Composite Index" (SCI) includes the sum of the degree of urban sprawl: the higher the index, the lower degree of sprawl. The summation of the degree of urban sprawl in the SCI is justified for the following reason: urban sprawl is a phenomenon with many aspects relating to spatial structure, which combines these dimensions, and excluding any of these from the analysis narrows the phenomenon. Correlation analysis was performed with respect to a) adjusted local GDP versus SCI and b) adjusted local GDP versus Urban Sprawl Dimensions (raw value). The results of the correlation analysis are presented in Table 3.
To interpret the results, it is necessary to note that in the correlation analysis greater SCI means less urban sprawl. Given the above assumptions and the results of the correlation, Table 3
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shows that there is a significant relationship between the level of urban sprawl and the level of the local economy because the correlation coefficient is 0.64. The correlation coefficient's value is significantly different from zero at p < 0.00. Therefore it can be concluded with a significant probability that a smaller degree of urban sprawl is accompanied by higher levels of local economies. This means that municipalities with a similar number of houses and varying distribution in space may differ in terms of level of the local economy. Consequently, the more compactly the houses in a given municipality are built across its space, the higher the level of the local economy, regardless of the distance from the city.
The correlations between adjusted GDP and Urban Sprawl Dimensions are: density = 0.53, continuity = 0.50, concentration = 0.38, clustering = 0.29 and centrality = 0.31. The significance for the correlation coefficients is satisfactory. The results therefore offer interesting conclusions. None of the correlation coefficients are higher than the correlation between GDP and SCI. These results have a substantive justification. Urban sprawl is a complex phenomenon that cannot be the result of only low density, lack of concentration, decentralisation and so on because individual indicators point to a lower correlation between individual dimensions of sprawl and GDP. For example, there may be a situation where there is urban sprawl despite the lack of building clustering. Hence, urban sprawl should be recognised as an aggregate comprising several features.
The correlation of GDP with sprawl indicators shows that, if phenomena such as high density living, continuity of buildings (no leapfrogging), concentration and grouping of buildings, and the proximity to the city centre occur individually, then their intensity will be at moderate or low levels and will correlate with GDP. Only when all of these indicators or dimensions occur together does one deal with the phenomenon of anti-sprawl (e.g., compactness) and have to deal with higher levels of the local economy. These applications are essential for spatial policy, which has promoted anti-sprawl solutions that do not focus on only one indicator (e.g., density). Urban sprawl is a complex phenomenon, and so such monitoring should offer multidimensionality.
The analysis of the correlations between local economy levels and urban sprawl was broken down into four areas: Krakow, Wroclaw, Lodz and Poznan. The significant value of the correlation coefficient (p) allows a cautious interpretation of SCI. High p values are likely to result from the low sample size (n). Thus in the municipalities neighbouring Lodz there is a strong correlation between the increase in urban sprawl and a lower level of the local economy because the correlation coefficient = 0.72 (p < 0.01). In the municipalities neigh-
bouring Krakow or Wroclaw such a relationship can be noted because the correlation coefficients are 0.68 (p = 0.01) and 0.69 (p = 0.02), respectively. A slightly lower level of correlation for the phenomena analysed is found for the municipalities surrounding Poznan. The correlation coefficient for this area is 0.50, which allows a connection to be assumed between the high degree of sprawl and low level of the economy in the municipalities.
5 Conclusion
The literature negatively assesses urban sprawl because of the costs it generates, such as increases in public expenditures for building and maintaining infrastructure and public services, a commercially negative impact on the city centre, an increase in energy and fuel consumption, and a negative impact on household budgets. However, many studies assess the impact of urban sprawl based on only one parameter or phenomenon instead of taking a more comprehensive approach. Thus, there is a gap in research on the impact of urban sprawl on the economy from a broader perspective: not seen through the prism of individual indicators, but as a wider system.
This article shows that greater urban sprawl is accompanied by lower GDP in municipalities, which proves the research hypothesis. Based on this model, it can be confirmed that there is a relation between urban sprawl and the local economy. However, urban sprawl has existed for a long time, and so two questions arise: Why was this problem not solved before ? What is the difference between the past and future in resolving this issue? In Poland, the transformation of the spatial structure of suburban areas is much more evident now than it was in the past, due to the entry of a new factor: during the past two decades, a rising number of private building contractors have strongly pushed for the creation of a more dispersed metropolitan form and the law favoured those processes. As noted by Andrzej Lisowski et al. (2014), the profile of a new spatial order is beginning to emerge, and conflicts between two citizens' groups with opposite values are highlighted. Citizens with a significant stake in improving their individual quality of life (either through economic gain or through the benefits of residence in locations with better environmental quality) contrast with citizens that defend the principles of responsibility and sustainable development. On top of this, there has been a lack of research on the impact of sprawl in Polish metropolitan areas. Thus, Polish local governments have been reluctant to prevent sprawl, and they remain so. They see only positive impacts of sprawl through tax revenues from new households. The wider consequences of this development, such as growing expenditures on new infrastructure, tend to be difficult not only for the national economy, which is recognised by
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the Polish government, but also for the local economy, which is not obvious to the local authorities and needs to be widely publicised.
The implications of this study are important for local authorities because houses should be built more compactly to attain a higher level of the local economy. Compactness is important because even municipalities with a similar number of houses, and at varying distribution in space, may differ in terms of the level of the local economy. The implications for losses for the local economy resulting from a chaotic spatial structure are real regardless of the distance from the city. Thus, it is not about distance, but structure. In this sense, the study contributes to knowledge about the costs of urban sprawl. This may be relevant to local government authorities and municipalities that are located near a large city and favour migration from the city, considering that population growth will increase tax revenues. This is only an apparent benefit. The costs of sprawl seem to be higher than the potential benefits. At the same time, municipalities are not always able to prevent this phenomenon. In this case, it is important to maintain a proper spatial policy that not only accurately diagnoses sprawl, but also offers proper solutions to limit its intensity.
The method for identifying urban sprawl used in this article is based on the approach by Galster et al. (2001). This method is valuable because it not only quantifies the level of this phenomenon, but also quantifies a number of attributes, such as density, continuity, concentration, clustering and centralisation. In addition to the article's most important conclusion - that increasing sprawl is accompanied by lower levels of the economy - it is important to recognise that urban sprawl is a multidimensional phenomenon and evaluating it through the prism of only one feature will prevent an assessment of its economic implications. This somewhat obvious observation should be reflected in spatial policies to prevent the phenomenon of urban sprawl. The point is that sprawl cannot be defined in documents only through the prism of low-density development, and anti-sprawl activities are only aimed at increasing housing density. Sprawl is a phenomenon that also comprises other manifestations that should be diagnosed, measured and monitored. This study has shown that there is a higher correlation with respect to urban sprawl as a complex phenomenon involving several features or indicators than for its individual dimensions - for example, housing density. If one looks at urban sprawl through only one indicator - for example, building decentralisation (correlation with GDP = 0.31) - then one could conclude that, because there is not a high correlation with this, urban sprawl therefore has no significant correlation with the economy. As demonstrated in this study, that is not true, and urban sprawl significantly correlates with the state of the local economy. Urban sprawl
is a multidimensional phenomenon and should be evaluated through the prism of many of its manifestations. Such a multidimensional approach to urban sprawl was used in this article and it was demonstrated that there is a significant correlation between a high degree of urban sprawl and low GDP. Thus, from the perspective of spatial policies and the local economy, the method offered by Glaser et al. (2001) is useful for diagnosing urban sprawl and creating spatial plans.
Piotr Litynski
Cracow University of Economics, Department of Regional Economy, Krakow, Poland
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