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Latitude 10°�S 9°�S 8°�S 7°�S 6°�S 5°�S 107 106 94 104105 99 93 100 91 90 79 95 92 63 87 98 61 60 89 88 62 78 96 101 97 77 64 82 86 23 26 29 72 71 70 69 68 67 76 80 59 27 28 81 58 24 25 66 22 46 65 37 83 102 103 47 75 44 45 50 52 57 84 85 73 21 35 15 51 74 17 16 48 56 19 18 20 36 34 49 42 53 54 14 39 43 33 306 38 12 40 13 55 2 32 11 41 1 3 4 31 5 7 8 9 10 108 106°�E 108°�E 110°�E 112°�E 114°�E 116°�E Longitude Figure 7b. Five least severe districts based on 3 indicators, mapped by the sp package of R [11],[12] Ranking of districts is continued based on salient scaling of 5 indicators (salient5) and salient scaling based on 3 indicators (salient3) as indicators for new ranks. Table 8a shows the five least severe districts; and Table 8b shows the five most severe districts. Here sal5 and sal3 (salient result of 5 and 3 indicators) used as indicators to obtain ranking of the districts. It has positive sense, so smaller number of the salient result means least severe of districts. Table 8a. Five least severe districts with salient5 and salient3 as indicators according to ORDIT ordering and salient scaling, as retrieved by R commands [8] CaseIDs AA SS ORDIT Salient 35 99.07 0.00 93 1 31 97.2 0.93 280.332 2 36 95.33 0.00 467 3 73 95.33 1.87 467.4 4 43 94.39 2.80 561.499 6 Table 8b. Five most severe districts with salient5 and salient3 as indicators according to ORDIT ordering and salient scaling, as retrieved by R commands [8] CaseIDs AA SS ORDIT Salient 47 1.87 91.59 9813.933 104 83 0.93 96.26 9907.972 105 44 0 92.52 10000.925 106 90 0 96.26 10000.963 107 87 0 98.13 10000.981 108 Figure 8a and 8b shows the districts of Table 8a and 8b, respectively. Table 9 is a resume of all results of ORDIT rankings. The first column is the ids of districts, second, third, and fourth columns indicate whether the district is a member of 5 least or 5 worst severe districts based on 5 indicators, 3 indicators, and 2 233 indicators (salient5 and salient3), respectively. The fifth column is a mark for a district as a member of all results. Latitude 10°�S 9°�S 8°�S 7°�S 6°�S 5°�S 107 106 94 104105 99 93 100 91 90 79 95 92 63 87 98 61 60 89 88 62 78 96 101 97 77 64 82 86 23 26 29 72 71 70 69 68 67 76 80 59 27 28 81 58 24 25 66 22 46 65 37 83 102 103 47 75 44 45 50 52 57 84 85 73 21 35 15 51 74 17 16 48 56 19 18 20 36 34 49 42 53 54 14 39 43 33 306 38 12 40 13 55 2 32 11 41 1 3 4 31 5 7 8 9 10 108 106°�E 108°�E 110°�E 112°�E 114°�E 116°�E Longitude Figure 8a. Five best districts based on salient 5 and salient 3 as indicators, mapped by the sp package of R[11],[12] Latitude 10°�S 9°�S 8°�S 7°�S 6°�S 5°�S 107 106 94 104105 99 93 100 91 90 79 95 92 63 87 98 61 60 89 88 62 78 96 101 97 77 64 82 86 23 26 29 72 71 70 69 68 67 76 80 59 27 28 81 58 24 25 66 22 46 65 37 83 102 103 47 75 44 45 50 52 57 84 85 73 21 35 15 51 74 17 16 48 56 19 18 20 36 34 49 42 53 54 14 39 43 33 306 38 12 40 13 55 2 32 11 41 1 3 4 31 5 7 8 9 10 108 106°�E 108°�E 110°�E 112°�E 114°�E 116°�E Longitude Figure 8b. Five most severe districts based on salient 5 and salient 3 as indicators, mapped by the sp package of R [11],[12]. Table 9. Five worst and five best districts as the result of ORDIT rankings Worst District salient5 salient3 salient53 Mark 90 Yes Yes Yes √ 87 Yes Yes Yes √ 47 Yes No Yes 58 Yes No No 81 Yes No No 44 No Yes Yes 83 No Yes Yes 45 No Yes No Best District 73 Yes Yes Yes √ 31 Yes Yes Yes √ 106 Yes No No 35 Yes Yes Yes √ 36 Yes No Yes 43 No Yes Yes 38 No Yes No 6.3 THE RESULTS OF ULS HOTSPOT DETECTION The districts in the hotspot area as the result of ULS hotspot detection based on each indicator are shown in Figure 9. It shows that for the first three indicators hotspot area (a,b,c) is dominant in Central Java, but for the forth indicator (d), the hotspot area is 8
dominant in West Java, while for the fifth indicator (e) the hotspot area comprises some districts of East Java Latitude Latitude 9°�S 8°�S 7°�S 6°�S 5°�S Latitude 9°�S 8°�S 7°�S 6°�S 5°�S 10°�S 9°�S 8°�S 7°�S 6°�S 5°�S The Hotspot of Number of Infants Death in Java Island, 2007 190 266 89 196 115 174 28 170 86 328 244 39 109 189 413 85 134 345 60134 114 107 392 173 178 10 44 145 169 78 170 210 83 208 96 329 125 162 83 223 31 56 72 117 115 177 147 186 69 312 104 69 663 25 392 270 141 217 142 347 96 340 14 372 100 2 47 260 101 111 66 100 133 96 18 62 61 63 253 46 66 37 32 24 85 298 17 82 50 187 131 112 174 172 84 61 97 6 78 83 142 77 285 139 6 22 54 108 106°�E 108°�E 110°�E 112°�E 114°�E 116°�E Longitude ULS hotspot detection, mapped with R (a) The Hotspot of Number of People in Poverty (in thousands) in Java Island, 2007 519.5 229.6 400.4 111.7 231.2 49.1 70.3 371.6 365.3 701.6 267.5 319 258 203.7 215.4260.5 352.3 283.9 394.6 519.1 297.8 288.3 325.5 338.9 257.4 297.6 273.6 178.5 229.5 321.5 203.7 435.5 328.2 361.5 26.2 780 550.2 270.5 300.4 270.4 188.7 11.5 223.3 302.4 375.6 20.1 74.9 261.6143.8 214.9 130.6 230.5 420.6 102.212.1 203 21.3 161.5380.1230.9 278.7 131.5 60 34.9 35.3 267.4 15 93.9 201 277.1 273.3 157.9 56.6 165.7 301.2 125.6 170.5 15.2 199 149.1 171.2 365.3 417 227.3 213.1 9470.7 141.7 35.4 122.6 106.9 294.4 35.9 91.4 235.8 114.5 361.7 28.3 398 161.8 234.4 43.7 22.6 87.2 187.7 54.5 12.9 171.8 106°�E 108°�E 110°�E 112°�E 114°�E 116°�E Longitude ULS hotspot detection, mapped with R (b) The Hotspot of Low Birth Weight in Java Island, 2007 610 129 261 947 1962 215 392 154 815 90 540 212 814 238 96 386 580 1085 238 490 299 478 752 172 149 113 788 60 768 172 295 300 522 483 9 137 931 445 436 345 606 125 189 164 130 85 144 262 644 208 228 1307 2464 111 105 507 62 572 815 1003 208 276 413 271 36 971 616 357 83 209 556 295 131 214 422 238 274 43 476 86 68 40 350 156 204 5 164 66 401 19 220 530 409 383 187 164 250 176 145 252 11 130 250 668 573 388 184 1456 106°�E 108°�E 110°�E 112°�E 114°�E 116°�E Longitude ULS hotspot detection, mapped with R (c) 234 Latitude 10°�S 8°�S 6°�S Latitude The Hotspot of Non Health Personnel Assisted (in percentage) in Java Island, 2007 6.3 14.6 3.2 0 26.6 30.3 14.5 11.7 21.9 43.8 18.5 22.5 10.5 2.5 14.2 43.3 39.7 13.6 21.6 7.6 4.8 18.7 29.7 3.2 33.9 13.5 18.3 37.3 18.5 17.8 15.1 2.3 22 9.9 22.6 21.2 48.9 6 8.9 0 20.6 27.3 28.2 8.7 8.8 10.6 5 18.9 47.6 22.8 22.7 25.8 24.1 0.2 12 5.9 25.9 15.8 9 29.6 0.2 41.1 4.8 9.5 0 4.5 4.5 14 0.2 2.4 11.9 20.3 12.4 6.2 0 9.1 0.9 7.3 9.8 10.4 19.7 6 7.2 4.6 11.6 9.9 19 21.3 14.5 11.3 4.6 14.6 0 12.6 17.6 20.2 3 2.4 16.2 0.5 22.2 5.4 19.3 13.9 0 0 8.9 12.6 106°�E 108°�E 110°�E 112°�E 114°�E 116°�E 10°�S 9°�S 8°�S 7°�S 6°�S 5°�S 4°�S 107 106 94 104 105 99 93 100 79 95 91 92 90 96 80 81 10197 82 87 98 89 88 86 108 83 102 103 84 85 ULS hotspot detection, mapped with R (d) Longitude The Hotspot of Average Education Shortfall in Java Island, 2007 63 62 61 60 78 77 64 23 29 72 71 70 69 68 67 76 26 27 58 59 28 24 25 66 22 46 65 37 47 75 44 45 50 52 57 73 21 35 15 51 74 17 16 19 18 48 56 20 36 49 42 53 54 34 14 39 43 306 33 38 12 40 13 55 2 32 11 41 1 3 4 315 7 8 9 10 106°�E 108°�E 110°�E 112°�E 114°�E 116°�E Longitude ULS Hotspot Detection, mapped with R (e) Figure 9. ULS Hotspot districts of infd (a), pov (b), lbw (c), abhp (d), and avedsf (e) as generated by the R commands [11],[12]. Table 10 shows the five worst districts for salient5, salient3 and salient53 and information whether the district is a hotspot or not a hotspot. There is no district of the first column as the hotspot based on indicator avedsf (average education shortfall), so this column is omitted. Districts 90 and 87 are strong enough to be considered as the worst districts of infant health. Table 10. Five worst districts for salients and ULS hotspot districts for each indicator Distr sal5 sal3 sal53 hinfd hpov hlbw habhp 90 Yes Yes Yes Yes No Yes Yes 87 Yes Yes Yes Yes No Yes Yes 47 Yes No Yes Yes Yes Yes Yes 58 Yes No No Yes Yes Yes No 81 Yes No No No No No Yes 83 No Yes Yes Yes No Yes Yes 44 No Yes Yes Yes Yes Yes No 45 No Yes No Yes Yes Yes No 9
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2012 ALBANY, NEW YORK UNITED STATES
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Foreword The 6 th International Con
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Table of Contents Forward .........
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Session 10: ICTs & Governance Trans
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o Revival of Farming Community with
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International Steering Committee Jo
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Jim DAVIES, Computing Laboratory, U
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Session 1 Open Government Data
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the combination of their moral sent
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involve the creation of a Network o
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approach [28] to expose indicators
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4. IMPLENTING PAR In order to come
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conclusions that might arise rom th
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6.3 Large Scale Implementation In r
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Implementation Framework for Open D
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2) Usage: Assessed by quantifying t
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Increasing Kenyan Open Data Consump
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insight into available data resourc
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New Perspectives for Electronic Gov
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Not without reason therefore that t
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Another principle adopted by this s
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access to public information, chang
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Open Government 2.0: Citizen Empowe
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As a third finding, we also found a
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Session 2 E-democracy & Participati
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two polarized (optimistic or utopia
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through aggregating, shaping, and c
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in-depth expert interviews, and wer
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government made of citizens’ cont
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[8] Putman, R.D. 1994. Making Democ
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The third stream of studies reviewe
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interests, gaining access to inform
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2. THEORETICAL FOUNDATION 2.1 The P
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epresentatives from the three main
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While letters to the editor could b
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person. Table 5: Public sphere char
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[32] Yang, S., H. Lee, and S. Kurni
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In March 2000, just a couple of mon
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society the gap will close [2]. The
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A New Roadmap for Next-Generation P
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needs and the activities of policy-
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[18] Moss, S., (2010). Policy Model
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2.1. National digital inclusion exp
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advanced initiatives specifically d
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involved later [22]. Several instit
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among lecturers and students parall
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2. COMPARATIVE E-GOVERNMENT IN LATI
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governments can innovate or be disc
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2. E-GOVERNMENT AS IS 2.1. E-Govern
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ankings on the formation of nationa
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Framework for Useful Transparency W
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are doing a good job overall in reg
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The Circular Continuum of Agencies,
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continue to rely on dial-up Interne
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this time the authors continue to w
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government agencies should consider
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The critical success factors for we
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include customers and competitors,
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Websites or columns for MFWs In thi
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According to our investigation, the
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Construction of Inter-regional Publ
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The Development of Public Smart Pho
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Stage Description 1 st stage (Simpl
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Understanding Citizens’ Perceptio
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performance compared to those provi
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Session 5 Information Sharing, Mode
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arising as a result of these two bi
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forms for the system. At the same t
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further investigation of the relati
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5.3.10 Commitment Both personal and
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law. Virginia Journal of Internatio
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available to others” [5]. Informa
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conflict around information sharing
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for understanding information polic
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primarily on the information flows
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On Public Service Provision Informa
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employs workflow management technol
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o Information. Represents any infor
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common documents repository at the
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Figure 15. Full model of Passport I
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142
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policies to prevent government agen
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ecause the majority of the shared i
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A Patent System Ontology for Facili
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uilt on top of the semantics of RDF
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dynamically derive relevancy based
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Figure 6. Cross-‐Reference betw
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Category II: After identifying some
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[8] De Nicola, A., Missikoff, M. an
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oral option, it also allows for a m
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mapping of citizens would ensure un
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Here the authors wish to extend the
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http://www.ecp.gov.pk/Reports/SPStr
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will enable to assess and confront
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The underlying DeLP process is as f
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Preparing for Digital Curation Gove
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semester hours. Nine of the forty-f
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REGNET: Regulatory Information Mana
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Search Terms/ Concepts References D
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4. COMPARATIVE STUDIES OF REGULATOR
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1105.4 [6] Content of Section 1105.
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Pollution Prevention Guide for Vehi
Page 213 and 214: From Information-Poor to Informatio
Page 215 and 216: To address the divide, specific gov
Page 217 and 218: ICT for Development and the MuNet P
Page 219 and 220: competencies and skills, contributi
Page 221 and 222: ICT Policy in Africa - A Comparativ
Page 223 and 224: essential and necessary to guide th
Page 225 and 226: Kenya it is the minister for Inform
Page 227 and 228: of existing Public Internet Access
Page 229 and 230: the policy process e.g. formulation
Page 231 and 232: Laggards or Victims of Socioeconomi
Page 233 and 234: Figure 3. Facilities & Services Enj
Page 235 and 236: Promoting user uptake of e-governme
Page 237 and 238: It was also decided that it was nec
Page 239: Session 8 Measurement 1
Page 242 and 243: technology (IT) projects by governm
Page 244 and 245: Gap = 5; The weak and unreliable In
Page 246 and 247: 6. ACKNOWLEDGEMENT We are grateful
Page 248 and 249: aspects are hardly discussed as the
Page 250 and 251: service level agreements. The inter
Page 252 and 253: concensus of all participants, the
Page 254 and 255: Furthermore, it is understood from
Page 256 and 257: Mobile Technologies on the Internet
Page 258 and 259: of mothers. Based on this reason, w
Page 260 and 261: 5.1.4 Precedence Plots Based on com
Page 262 and 263: 3 indicators are similar but not th
Page 266 and 267: 7. CONCLUSION According to ORDIT an
Page 268 and 269: include but not limited to: payment
Page 270 and 271: Categories Only XSS (%) Table 2: Di
Page 272 and 273: From table 3, it can be deduced tha
Page 274: Session 9 Social Media in Governmen
Page 277 and 278: 1. What kind of social media servic
Page 279 and 280: 4.2.1 Facebook Usage Figure 3. Perc
Page 281 and 282: Government Crisis Communication on
Page 283 and 284: estoration theory. He termed it as
Page 285 and 286: Table 3. Strategies for Pre-crisis
Page 287 and 288: accounts, among which seven message
Page 289 and 290: information in the crisis event sta
Page 291 and 292: Government Official Microblogs: An
Page 293 and 294: It is worth mentioning that, while
Page 295 and 296: Managing Chinese Government Microbl
Page 297 and 298: due to their tremendous influence,
Page 299 and 300: 3. ACKNOWLEDGEMENT This research is
Page 301 and 302: 3) Established year: to specify the
Page 303 and 304: Figure 6. Language used in Hong Kon
Page 306 and 307: E-Government and Transformation of
Page 308 and 309: and domestic as well as internation
Page 310 and 311: 4.6 Computerisation of Railway Tick
Page 312 and 313: abysmally low (2.25). In addition t
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Evaluating and Assessing a Typology
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Exhaustiveness indicates that all c
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Fig. 1 Spatial Unit Fig. 3. By whom
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SUPPLEMENTARY TABLE 1. RESULT OF SE
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Explaining the eGovernment Paradox:
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eGovernment services to a large ext
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policy agenda, as Heeks [10] says,
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Implementation Plan’s outcomes an
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Conference on eGovernment (pp. 90-9
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Investigating the Relationships bet
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hybrid or authoritarian regime. Bov
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Technologies, Tools and Web 2.0 in
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with resources, peers and experts w
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cluster were: Court Clerks; Court,
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(8 questions) and the third subpart
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http://ux.brookdalecc.edu/governanc
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the examination of the 311 program
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3. CASE STUDY METHOD This study use
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discuss departmental performance at
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or laptop computers. Internet conne
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[2] Anavitarte, L. and Tratz-Ryan,
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A Literature Review: IT Governance
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Interviewees Useful? Table 3. Inter
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Assessment of Success Factors of e-
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In view of different theoretical ap
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approximate figure of 60% of intern
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difference between its priority and
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Computer Supported Contractor Selec
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(2) conduction of the public auctio
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E-Government Success Factors in the
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creating different incentives and p
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American and the Caribbean Institut
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* Note: A 7-points Likert scale was
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showed to be useful, for collecting
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Public CIO, Figurehead or Decision-
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4.2 Operative CIO The Operative CIO
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Session 12 Implementation & Impact
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NYSED is responsible for collecting
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considered markers that are able to
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many of the MDGs. The direct impact
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strongly believed that local govern
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Goal Variables Measurements 2 3 Com
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Hypothesis Variables Endogenous Exo
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[16] Joreskog, K.G., and Sorbom, D.
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GoN has addressed the challenge to
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Figures 6 and 7. Computer classes a
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ICT-enabled Delivery of Maternal He
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3. E-HEALTH 3.1 ICTs and Health Com
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Chart 1 [40] GHANA - Total Telephon
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change their behaviour, improve the
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activities such as the MoTeCH partn
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If We Build it Will They Come? A Mi
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The methodology contains inherent l
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information about their broadband s
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Session 13 E-democracy & Participat
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attempt to influence future behavio
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Evidence shows that cognitive facto
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success factors for eParticipation[
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structure of the forum as well as t
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government as well as augmenting di
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Figure 1: Message content 4.3 Messa
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Figure 6: Associated levels 4.10 At
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improve the government management e
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Session 14 Information Structure
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2. HEALTHCARE IN RURAL INDIA Health
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Cloud Data Center Internet Service
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Reliability of data: data collected
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Information System (NHIS). A Nation
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charge are either not aware of the
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from this medium. Additional securi
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attacks require the adversary to ma
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actions with yet unknown recipients
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Session 15 Measurement 2
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2. LITERATURE REVIEW 2.1 Electronic
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Among the main limitations and chal
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Furthermore, our study demonstrates
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E-Government Portals in Central Ame
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13 Guadaloupe 31.74 14.19 14 Aruba
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Evolving e-Government Benchmarking
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judgments are made about the qualit
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percentage of online availability o
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methodology, and few national gover
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integrating them worth it? Because
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Impacts of the Public Procurement R
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Not all the municipalities have the
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3. HYPHOTESIS AND RELEVANT QUESTION
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58% of the buyers believe that the
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A strategy of evolution of the mark
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Posters
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Russian Federation legislative prop
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Figure 1. Document Flow and E-DRM P
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We further assume that each key tar
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egion that is more representative o
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2.2 C-DAC C-DAC’s PARAM series of
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The Metropolitan Area Planning Coun
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expanded functionality in three are
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For each case, semi-structured inte
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Figure 1. Comparative analysis on t
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projects and their on-site images.
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key challenges of Enterprise System
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5. PART THREE: E GOVERNMENT APPLICA
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Following the research objectives,
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From Inter-Agency Information Shari
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479
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more and more valuable with the gro
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Implementation of Integrated Tax Sy
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Information Sharing in Inter-depart
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Interagency Collaboration in Provid
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489
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noted. The list of basic e-services
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the description of procedure for mo
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Opportunities for Improving eGovern
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Prioritization and Geo-Spatiotempor
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[4] R.Bruggemann and Ganapati P. Pa
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equirements. A direct communication
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Stage - 2: Here the actual software
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membership associations, recreation
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Fig 1 : Agriculture Revolution 3. P
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sector, private sector, and civil s
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System For Measuring e-Gov: A Multi
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The Digital Divide’s Devaluing of
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[3] Coursey, D., & Norris, D.F. (20
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In Brazil, electronic accessibility
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2) The areas to which mobile and wi
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2. OVERVIEW OF DISTRICT MAPPING The
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3.4 Driving License Driving License
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and socio-economic backgrounds. The
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Figure - 2: eGovernment Implementat
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Tutorial: Digital Curation for Publ
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ut also provide sufficient detail o
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ABM is known as a bottom-up simulat
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5.2 Relation to Empirics “Where d
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Larson, Elizabeth .................
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